Experts doen voorspellingen voor 2016

In 2016 staat veel te gebeuren op het vlak van privacy en dataprotectie. Hoewel de discussie ‘security versus privacy’ dit jaar al is begonnen, barst deze in 2016 echt los. Computable-experts bespreken verschillende it-beveiligingsvoorspellingen voor het komende jaar. Hierbij worden onder andere cloudbeveiliging en de integratie van beveiligingsoplossingen aangehaald.

Privacy
Richard van Lent, managing partner bij MITE Systems:
2015 stond vooral in het teken van onderwerpen als ‘Cloud First’, ‘Mobile First’ & internet of things (IoT). Daarnaast heeft de Wet Meldplicht Datalekken, een toevoeging op de bestaande Wbp, de afgelopen maanden nogal wat stof doen opwaaien binnen afdelingen als hr, legal/risk compliance, security en ict. Data Analytics-oplossingen gaan voor de meeste bedrijven rand voorwaardelijk worden als het gaat om het borgen van de privacy, compliancy en security in de dagelijkse operatie. Een belangrijke trend voor 2016, die zich momenteel in een rap tempo ontwikkelt, is dan ook het inzetten van Data Analytics-oplossingen om zaken als beveiliging, voorspelbaarheid & gedrag bijna in realtime inzichtelijk te maken.

Gerard Stroeve, manager Security & Continuity Services bij Centric:
Een andere belangrijke security-trend voor 2016 is privacy. Op het gebied van privacy staat er het komende jaar veel te gebeuren. Neem bijvoorbeeld de meldplicht datalekken. Deze gaat op 1 januari 2016 in en verplicht organisaties (zowel bedrijven als overheden) om ernstige datalekken direct te melden. Ook krijgt het College Bescherming Persoonsgegevens (CBP), dat vanaf 1 januari verder gaat als de Autoriteit Persoonsgegevens, boetebevoegdheid tot 820.000 euro. Daarnaast vormt de Europese Algemene Data Protectie Verordening een belangrijke internationale ontwikkeling op het gebied van privacy. Naar verwachting wordt ook deze begin 2016 van kracht met een overgangsperiode van zo’n anderhalf jaar. Komend jaar moeten organisaties echt aan de slag met de voorbereiding op die nieuwe wetgeving.

Het is daarbij belangrijk om privacy niet als een op zichzelf staand onderwerp te benaderen. Privacy heeft specifieke wet- en regelgeving, maar is wel een integraal onderdeel van informatiemanagement en -beveiliging als geheel.

Lex Borger, Principal Consultant bij I-to-I:
De politieke discussie ‘security versus privacy’ is al begonnen, maar gaat in het verkiezingsjaar in de Verenigde Staten echt losbarsten. Hoeveel privacy moeten we opgeven om terrorisme te bestrijden? Mogen overheden eisen stellen om toegang te krijgen tot informatie die versleuteld verzonden wordt of opgeslagen is? Kan terrorisme zo bestreden worden? Kunnen we overheden vertrouwen met die mogelijkheden? Kunnen we voorkomen dat anderen (overheden, georganiseerde criminaliteit) hier misbruik van maken? Mag de burger nog wat te verbergen hebben? Voldoende gelegenheid tot discussie, ik ben benieuwd.

Cloud, integratie en IoT
Gerard Stroeve, manager Security & Continuity Services, Centric:
De derde belangrijke trend voor 2016 is cloudsecurity. Veel organisaties geven aan nog te weinig grip te hebben op de cloudoplossingen die zij gebruiken. Hoe vind je bijvoorbeeld afstemming met grote, publieke cloudleveranciers als Google, Microsoft en Amazon? Een andere grote uitdaging vormt de zogenaamde ‘Shadow it, een fenomeen waarmee bijna iedere organisatie tegenwoordig te maken heeft. Wanneer de juiste functionaliteit niet of niet snel genoeg beschikbaar is, zoeken medewerkers daarvoor steeds vaker zelf een oplossing in de cloud. Zo ontstaat er langzaam maar zeker een wildgroei aan gebruikte cloudoplossingen waarop de organisatie geen grip heeft. Dat vraagt om helder beleid en goede richtlijnen rondom veilig cloudgebruik, zonder daarbij de productiviteit in de weg te zitten. Een praktisch handvat hierbij vormt de 4C-benadering.

John Veldhuis, Senior System Consultant bij Sophos:
Zoals we in de gateway een integratie hebben gezien van voorheen losstaande apparaten voor email/web proxy, vpn, packet filtering, layer 7 filtering, load balancing et cetera. in een UTM, gaan we een doorontwikkeling meemaken die ervoor zorgt dat anti-malware, UTM en versleutelingsoplossingen met elkaar praten. Voorbeeld: Verdacht verkeer, bijvoorbeeld ransomware gerelateerd, wordt gedetecteerd. Dit kan door software op de besmette machine zijn, maar ook door de UTM. Hierdoor kan automatisch:

• de machine ontdaan worden van sleutels, zodat vertrouwelijke data niet gelekt kan worden, maar ook het bewerken ervan (versleutelen door ransomware) niet meer kan plaatsvinden (geen sleutel = geen toegang);
• de machine in een quarantaine- of mitigatienetwerk worden geplaatst, op de overige machines een zoekactie worden gestart naar het bestand dat het verkeer veroorzaakte de herkomst van het bestand worden gezocht en in een reputatiefilter worden gezet et cetera.

Harm de Haan manager consultancy bij Telindus:
Door de toenemende complexiteit van it-infrastructuren en de mate waarin onderdelen met elkaar geïntegreerd zijn, is security steeds moeilijker te garanderen. Bovendien zijn de gevolgen van een ´breach´ groter, door de nieuwe Wet Meldplicht Datalekken. Hierdoor is security voor veel organisaties een belangrijk thema in 2016. Endpoint solutions kunnen veiligheid onvoldoende garanderen, juist door de complexiteit van moderne infrastructuren. Beter zouden organisaties zich oriënteren op ‘security by design’: een aanpak waarbij security in de ontwerpfase van een infrastructuur wordt ingericht als onderdeel van de losse componenten, zoals compute, networking en storage.

Lex Borger, Principal Consultant bij I-to-I:
Geen excuus meer: TLlskan nu overal en gratis. Ttp's moeten hun business-model veranderen, Let's Encrypt levert gratis tls-certificaten met automatische provisioning. Jouw site kan altijd https aan. Geen dure certificaten te beheren, geen moeilijk proces voor aanvraag, maar je moet wel aantonen dat je zeggenschap hebt over je website. Voor velen zal dit veilig genoeg zijn. Het betaalde proces voor certificaatbeheer is complex genoeg, het is absurd dat er nog gepleit wordt om SHA-1 langer geldig te laten zijn omdat we onze certificaten niet snel genoeg kunnen opsporen en vervangen.

Ook zal het internet of things in 2016 explosief groeien met sensors en kleine automaatjes die eenvoudig moeten werken en simpel aan te sluiten zijn. Dit geeft heel veel mogelijkheden voor hackers om ik weet niet wat te doen (Ik ben niet creatief genoeg om te bedenken wat er allemaal mogelijk is). We gaan er flink last van krijgen dat onze netwerkinfrastructuren inherent onveilig zijn. Het is nog steeds eenvoudig je voor te doen als een ander apparaat op het internet. Dit gegeven ligt aan de basis van veel aanvallen - DDoS, identity theft.

Tot slot: een gedegen basisproces
Los van deze drie thema’s vraagt informatiebeveiliging vooral een integrale benadering, meent Gerard Stroeve. ‘Informatiebeveiliging is een breed vakgebied met verschillende aandachtsgebieden. Naast de genoemde thema’s, is er bijvoorbeeld cybersecurity. Het aantal DDoS- of ransomware-aanvallen zal komend jaar niet afnemen. Ook verwachten we dat de aandacht voor beschikbaarheid en continuïteitsmanagement het komend jaar zal toenemen.’

Om effectief met al deze uiteenlopende dreigingen om te gaan, is een gedegen basisproces volgens hem cruciaal. ‘Door een goed governancemodel ben je in staat te reageren op nieuwe en veranderende dreigingen. Hierbij staan classificatie en het analyseren van risico’s centraal. Belangrijk is ook dat informatiebeveiliging een stevig managementdraagvlak krijgt.’

Source: Computable

5 Astonishing IoT examples in civil engineering

The internet of things is making a major impact on the field of civil engineering, and these five examples of IoT applications in civil engineering are fascinating.

As the Internet of Things (IoT) becomes smarter and more advanced, we’ve started to see its usage grow across various industries. From retail and commerce to manufacturing, the technology continues to do some pretty amazing things in nearly every sector. The civil engineering field is no exception.

An estimated 20 billion internet-connected devices will be active around the world by 2020. Adoption is certainly ramping up, and the technologies that support IoT are also growing more sophisticated: including big data, cloud computing and machine learning.

As a whole, civil engineering projects have a lot to gain from the integration of IoT technologies and devices. The technology significantly improves automation and remote monitoring for many tasks, allowing operators to remain hands-off more than ever before. The data that IoT devices collect can inform and enable action throughout the scope of a project and even beyond.

For example, IoT sensors can monitor soil consolidation and degradation, as well as a development project’s environmental impact. Alternatively, IoT can measure and identify public roadways that need servicing. These two basic examples provide a glimpse into what IoT can do in the civil engineering sector.

IoT, alongside many other innovative construction technologies, will completely transform the industry. That said, what role is it currently playing in the field? What are some other applications that are either planned or now in use? How can the civil engineering industry genuinely use IoT?

1. Allows a transformation from reactionary to preventative maintenance

Most maintenance programs are corrective or reactionary. When something breaks down or fails, a team acts to fix the problem. In reality, this practice is nothing more than slapping a bandage on a gaping wound.

With development projects, once things start to break down, they generally continue on that path. Problems grow much more prominent, no matter what fixes you apply. It makes more sense, then, to monitor a subject’s performance and status and apply fixes long before things break down. In other words, using a preventative maintenance routine is much more practical, efficient and reliable.

IoT devices and sensors deliver all the necessary data to make such a process possible. They collect information about a subject in real-time and then report it to an external system or analytics program. That program then identifies potential errors and communicates the necessary information to a maintenance crew.

In any field of construction, preventative maintenance considerably improves the project in question as well as the entire management process. Maintenance management typically comprises about 40% to 50% of a business’s operational budget. Companies spend much of their time reacting to maintenance issues rather than preventing them. IoT can turn that around.

2. Presents a real-time construction management solution

A proper construction management strategy is necessary for any civil engineering project. Many nuanced tasks need to be completed, whether they involve tracking and measuring building supplies or tagging field equipment and dividing it up properly.

IoT technology can reduce tension by collecting relevant information in real time and delivering it to the necessary parties. Real-time solutions also provide faster time-to-action. Management and decision-makers can see almost immediately how situations are playing out and take action to either improve or correct a project’s course.

For example, imagine the following scenario. During a project that’s underway, workers hit a snag that forced them to use more supplies than expected. Rather than waiting until supplies run out, the technology has already ordered more. That way, the supplies are already on their way and will arrive at the project site before the existing supply is exhausted. The result is a seamless operation that continually moves forward, despite any potential errors. IoT can measure the number of supplies and report it to a remote system, which then makes the necessary purchase order.

3. Creates automated and reliable documentation

One of the minor responsibilities of development and civil engineering projects is related to paperwork. Documentation records a great deal about a project before, during and after it wraps up.

IoT technologies can improve the entire process, if not completely automate many of the tedious elements. Reports are especially useful to have during inspections, insurance and liability events, and much more. The data that IoT collects can be parsed and added to any report to fill out much-needed details. Because the process happens automatically, the reports can generate with little to no external input.

4. Provides a seamless project safety platform

Worksites can be dangerous, which is why supervisors and project managers must remain informed about their workers at all times. If an accident occurs, they must be able to locate and evacuate any nearby personnel. IoT can provide real-time tracking for all workers on a site, and even those off-site.

More importantly, IoT technology can connect all those disparate parties, allowing for direct communication with near-instant delivery. The result is a much safer operation for all involved, especially the workers who spend most of their time in the trenches.

5. Enhances operational intelligence support

By putting IoT and data collection devices in place with no clear guidance, an operation can suffer from data overload: an overabundance and complete saturation of intelligence with no clear way to analyze the data and use it.

Instead, once IoT technology is implemented, organizations are forced to focus on an improved operational intelligence program to make sure the data coming in is adequately vetted, categorized and put to use. It’s cyclical because IoT empowers the intelligence program by offering real-time collection and analysis opportunities. So, even though more data is coming in and the process of extracting insights is more complex, the reaction times are much faster and more accurate as a result.

Here’s a quick example. With bridge and tunnel construction, it’s necessary to monitor the surrounding area for environmental changes. Soil and ground movement, earthquakes, changes in water levels and similar events can impact the project. Sensors embedded within the surrounding area can collect pertinent information, which passes to a remote analytics tool. During a seismic event, the entire system would instantly discern if work must be postponed or if it can continue safely. A support program can distribute alerts to all necessary parties automatically, helping to ensure everyone knows the current status of the project, especially those in the field.

Identifying new opportunities with IoT

Most civil engineering and development teams have no shortage of projects in today’s landscape. Yet, it’s still crucial to remain informed about the goings-on to help pinpoint more practical opportunities.

When IoT is installed during new projects, the resulting data reports may reveal additional challenges or problems that would have otherwise gone unnoticed. A new two-lane road, for instance, may see more traffic and congestion than initially expected. Or, perhaps a recently developed water pipeline is seeing unpredictable pressure spikes.

With the correct solutions in place, IoT can introduce many new opportunities that might significantly improve the value and practicality of a project.

Author: Megan Ray Nichols

Source: SmartDataCollective

85% van de bedrijven slaat gevoelige data op in de cloud. Dit is een flinke stijging ten opzichte van de 54% die vorig jaar aangaf dit te doen. 70% van de bedrijven maakt zich zorgen over de veiligheid van deze data.

Dit blijkt uit onderzoek van 451 Research in opdracht van Vormetric, leverancier van databeveiliging voor fysieke, big data, public, private en hybride cloud omgevingen. Gevoelige data staat uiteraard niet alleen in de cloud. 50% van de bedrijven geeft aan gevoelige data in big data systemen te hebben staan (tegenover 31% vorig jaar), en 33% heeft dergelijke data in Internet of Things (IoT) omgevingen opgeslagen.

Zorgen over de cloud
451 Research heeft respondenten ook gevraagd naar de zorgen die zij hebben over de veiligheid van hun gevoelige data die in de cloud staat. De belangrijkste zorgenpunten zijn:

• Cyberaanvallen en -inbraken bij een service provider (70%)
• De kwetsbaarheid van een gedeelde infrastructuur (66%)
• Een gebrek aan controle over de locatie waar data is opgeslagen (66%)
• Een gebrek aan een data privacy beleid of privacy SLA (65%)

Ook is respondenten gevraagd welke wijzigingen hun bereidheid data in de cloud onder te brengen zullen vergroten. De belangrijkste wijzigingen waar respondenten behoefte aan hebben zijn:

• Encryptie van data, waarbij de encryptiesleutel wordt beheerd op de eigen infrastructuur van het bedrijf (48%)
• Gedetaileerde informatie over de fysieke en IT-beveiliging (36%)
• Het zelf kunnen kiezen voor encryptie van data die is opgeslagen op de infrastructuur van een service provider (35%)

Zorgen over big data systemen
Ook de opslag van gevoelige data in big data systemen baart respondenten zorgen. De belangrijkste zorgenpunten zijn:

• De veiligheid van rapporten die met big data systemen worden gecreëerd, aangezien deze gevoelige data kunnen bevatten (42%)
• Het feit dat data op iedere locatie binnen deze omgeving kan zijn ondergebracht (41%)
• Privacyschendingen door data die uit verschillende landen afkomstig is (40%)Toegang door gebruikers met ‘superrechten’ tot beschermde data (37%)
• Een gebrek aan een security raamwerk en beheermogelijkheden binnen de omgeving (33%)

Ook merkt 451 Research op dat big data systemen vaak in de cloud draaien. Zorgen over de opslag van gevoelige data van de cloud zijn hierdoor ook van toepassing op data die in big data omgevingen is opgeslagen.

Ook data in IoT omgevingen leidt tot zorgen
Tot slot kijkt 451 Research naar de zorgen die bedrijven hebben over de opslag van data in IoT omgevingen. De belangrijkste zorgen op dit gebied zijn:

• Het beschermen van data die door IoT wordt gecreëerd (35%)
• Privacyschendingen (30%)
• Identificeren welke data gevoelig is (29%)
• Toegang van gebruikers met ‘superrechten’ tot IoT data en apparaten (28%)
• Aanvallen op IoT-apparaten die een impact kunnen hebben op de kritieke bedrijfsvoering (27%)

Het gehele onderzoek lees je HIER

Source: Executive People

The history of artificial intelligence has been marked by seemingly revolutionary moments — breakthroughs that promised to bring what had until then been regarded as human-like capabilities to machines. The AI highlights reel includes the “expert systems” of the 1980s and Deep Blue, IBM’s world champion-defeating chess computer of the 1990s, as well as more recent feats like the Google system that taught itself what cats look like by watching YouTube videos.

But turning these clever party tricks into practical systems has never been easy. Most were developed to showcase a new computing technique by tackling only a very narrow set of problems, says Oren Etzioni, head of the AI lab set up by Microsoft co-founder Paul Allen. Putting them to work on a broader set of issues presents a much deeper set of challenges.
Few technologies have attracted the sort of claims that IBM has made for Watson, the computer system on which it has pinned its hopes for carrying AI into the general business world. Named after Thomas Watson Sr, the chief executive who built the modern IBM, the system first saw the light of day five years ago, when it beat two human champions on an American question-and-answer TV game show, Jeopardy!
But turning Watson into a practical tool in business has not been straightforward. After setting out to use it to solve hard problems beyond the scope of other computers, IBM in 2014 adapted its approach.
Rather than just selling Watson as a single system, its capabilities were broken down into different components: each of these can now be rented to solve a particular business problem, a set of 40 different products such as language-recognition services that amount to a less ambitious but more pragmatic application of an expanding set of technologies.
Though it does not disclose the performance of Watson separately, IBM says the idea has caught fire. John Kelly, an IBM senior vice-president and head of research, says the system has become “the biggest, most important thing I’ve seen in my career” and is IBM’s fastest growing new business in terms of revenues.
But critics say that what IBM now sells under the Watson name has little to do with the original Jeopardy!-playing computer, and that the brand is being used to create a halo effect for a set of technologies that are not as revolutionary as claimed.

“Their approach is bound to backfire,” says Mr Etzioni. “A more responsible approach is to be upfront about what a system can and can’t do, rather than surround it with a cloud of hype.”
Nothing that IBM has done in the past five years shows it has succeeded in using the core technology behind the original Watson demonstration to crack real-world problems, he says.

Watson’s case
The debate over Watson’s capabilities is more than just an academic exercise. With much of IBM’s traditional IT business shrinking as customers move to newer cloud technologies, Watson has come to play an outsized role in the company’s efforts to prove that it is still relevant in the modern business world. That has made it key to the survival of Ginni Rometty, the chief executive who, four years after taking over, is struggling to turn round the company.
Watson’s renown is still closely tied to its success on Jeopardy! “It’s something everybody thought was ridiculously impossible,” says Kris Hammond, a computer science professor at Northwestern University. “What it’s doing is counter to what we think of as machines. It’s doing something that’s remarkably human.”

By divining the meaning of cryptically worded questions and finding answers in its general knowledge database, Watson showed an ability to understand natural language, one of the hardest problems for a computer to crack. The demonstration seemed to point to a time when computers would “understand” complex information and converse with people about it, replicating and eventually surpassing most forms of human expertise.
The biggest challenge for IBM has been to apply this ability to complex bodies of information beyond the narrow confines of the game show and come up with meaningful answers. For some customers, this has turned out to be much harder than expected.
The University of Texas’s MD Anderson Cancer Center began trying to train the system three years ago to discern patients’ symptoms so that doctors could make better diagnoses and plan treatments.
“It’s not where I thought it would go. We’re nowhere near the end,” says Lynda Chin, head of innovation at the University of Texas’ medical system. “This is very, very difficult.” Turning a word game-playing computer into an expert on oncology overnight is as unlikely as it sounds, she says.

Part of the problem lies in digesting real-world information: reading and understanding reams of doctors’ notes that are hard for a computer to ingest and organise. But there is also a deeper epistemological problem. “On Jeopardy! there’s a right answer to the question,” says Ms Chin but, in the
medical world, there are often just well-informed opinions.
Mr Kelly denies IBM underestimated how hard challenges like this would be and says a number of medical organisations are on the brink of bringing similar diagnostic systems online.

Applying the technology
IBM’s initial plan was to apply Watson to extremely hard problems, announcing in early press releases “moonshot” projects to “end cancer” and accelerate the development of Africa. Some of the promises evaporated almost as soon as the ink on the press releases had dried. For instance, a far-reaching partnership with Citibank to explore using Watson across a wide range of the bank’s activities, quickly came to nothing.
Since adapting in 2014, IBM now sells some services under the Watson brand. Available through APIs, or programming “hooks” that make them available as individual computing components, they include sentiment analysis — trawling information like a collection of tweets to assess mood — and personality tracking, which measures a person’s online output using 52 different characteristics to come up with a verdict.

At the back of their minds, most customers still have some ambitious “moonshot” project they hope that the full power of Watson will one day be able to solve, says Mr Kelly; but they are motivated in the short term by making improvements to their business, which he says can still be significant.
This more pragmatic formula, which puts off solving the really big problems to another day, is starting to pay dividends for IBM. Companies like Australian energy group Woodside are using Watson’s language capabilities as a form of advanced search engine to trawl their internal “knowledge bases”. After feeding more than 20,000 documents from 30 years of projects into the system, the company’s engineers can now use it to draw on past expertise, like calculating the maximum pressure that can be used in a particular pipeline.
To critics in the AI world, the new, componentised Watson has little to do with the original breakthrough and waters down the technology. “It feels like they’re putting a lot of things under the Watson brand name — but it isn’t Watson,” says Mr Hammond.
Mr Etzioni goes further, claiming that IBM has done nothing to show that its original Jeopardy!-playing breakthrough can yield results in the real world. “We have no evidence that IBM is able to take that narrow success and replicate it in broader settings,” he says. Of the box of tricks that is now sold under the Watson name, he adds: “I’m not aware of a single, super-exciting app.”

To IBM, though, such complaints are beside the point. “Everything we brand Watson analytics is very high-end AI,” says Mr Kelly, involving “machine learning and high-speed unstructured data”. Five years after Jeopardy! the system has evolved far beyond its original set of tricks, adding capabilities such as image recognition to expand greatly the range of real-world information it can consume and process.

Adopting the system
This argument may not matter much if the Watson brand lives up to its promise. It could be self-fulfilling if a number of early customers adopt the technology and put in the work to train the system to work in their industries, something that would progressively extend its capabilities.

Another challenge for early users of Watson has been knowing how much trust to put in the answers the system produces. Its probabilistic approach makes it very human-like, says Ms Chin at MD Anderson. Having been trained by experts, it tends to make the kind of judgments that a human would, with the biases that implies.
In the business world, a brilliant machine that throws out an answer
to a problem but cannot explain itself will be of little use, says Mr Hammond. “If you walk into a CEO’s office and say we need to shut down three factories and sack people, the first thing the CEO will say is: ‘Why?’” He adds: “Just producing a result isn’t enough.”
IBM’s attempts to make the system more transparent, for instance by using a visualisation tool called WatsonPaths to give a sense of how it reached a conclusion, have not gone far enough, he adds.
Mr Kelly says a full audit trail of Watson’s decision-making is embedded in the system, even if it takes a sophisticated user to understand it. “We can go back and figure out what data points Watson connected” to reach its answer, he says.

He also contrasts IBM with other technology companies like Google and Facebook, which are using AI to enhance their own services or make their advertising systems more effective. IBM is alone in trying to make the technology more transparent to the business world, he argues: “We’re probably the only ones to open up the black box.”
Even after the frustrations of wrestling with Watson, customers like MD Anderson still believe it is better to be in at the beginning of a new technology.
“I am still convinced that the capability can be developed to what we thought,” says Ms Chin. Using the technology to put the reasoning capabilities of the world’s oncology experts into the hands of other doctors could be far-reaching: “The way Amazon did for retail and shopping, it will change what care delivery looks like.”
Ms Chin adds that Watson will not be the only reasoning engine that is deployed in the transformation of healthcare information. Other technologies will be needed to complement it, she says.
Five years after Watson’s game show gimmick, IBM has finally succeeded in stirring up hopes of an AI revolution in business. Now, it just has to live up to the promises.

Source: Financial Times

_{A roundup of big data and analytics predictions and pontifications from several industry prognosticators.}

At the end of each year, PR folks from different companies in the analytics industry send me predictions from their executives on what the next year holds. This year, I received a total of 60 predictions from a record 17 companies. I can't laundry-list them all, but I can and did put them in a spreadsheet (irony acknowledged) to determine the broad categories many of them fall in. And the bigger of those categories provide a nice structure to discuss many of the predictions in the batch.

Predictions streaming in
MapR CEO John Shroeder, whose company just added its own MapR Streams component to its Hadoop distribution, says "Converged Approaches [will] Become Mainstream" in 2016. By "converged," Schroeder is alluding to the simultaneous use of operational and analytical technologies. He explains that "this convergence speeds the 'data to action' cycle for organizations and removes the time lag between analytics and business impact."

The so-called "Lambda Architecture" focuses on this same combination of transactional and analytical processing, though MapR would likely point out that a "converged" architecture co-locates the technologies and avoids Lambda's approach of tying the separate technologies together.

Whether integrated or converged, Phu Hoang, the CEO of DataTorrent predicts 2016 will bring an ROI focus to streaming technologies, which he summarizes as "greater enterprise adoption of streaming analytics with quantified results." Hoang explains that "while lots of companies have already accepted that real-time streaming is valuable, we'll see users looking to take it one step further to quantify their streaming use cases."

Which industries will take charge here? Hoang says "FinTech, AdTech and Telco lead the way in streaming analytics." That makes sense, but I think heavy industry is, and will be, in a leadership position here as well.

In fact, some in the industry believe that just about everyone will formulate a streaming data strategy next year. One of those is Anand Venugopal of Impetus Technologies, who I spoke with earlier this month. Venugopa, in fact, feels that we are within two years of streaming data becoming looked upon just another data source.

Internet of predicted things
It probably won't shock you that the Internet of Things (IoT) was a big theme in this year's round of predictions. Quentin Gallivan, Pentaho's CEO, frames the thoughts nicely with this observation: "Internet of Things is getting real!" Adam Wray, CEO at Basho, quips that "organizations will be seeking database solutions that are optimized for the different types of IoT data." That might sound a bit self-serving, but Wray justifies this by reasoning that this will be driven by the need to "make managing the mix of data types less operationally complex." That sounds fair to me.

Snehal Antani, CTO at Splunk, predicts that "Industrial IoT will fundamentally disrupt the asset intelligence industry." Suresh Vasudevan, the CEO of Nimble Storage proclaims "in 2016 the IoT invades the datacenter." That may be, but IoT technologies are far from standardized, and that's a barrier to entry for the datacenter. Maybe that's why the folks at DataArt say "the IoT industry will [see] a year of competition, as platforms strive for supremacy." Maybe the data center invasion will come in 2017, then.

Otto Berkes, CTO at CA Technologies, asserts that "Bitcoin-born Blockchain shows it can be the storage of choice for sensors and IoT." I hardly fancy myself an expert on blockchain technology, so I asked CA for a little more explanation around this one. A gracious reply came back, explaining that "IoT devices using this approach can transact directly and securely with each other...such a peer-to-peer configuration can eliminate potential bottlenecks and vulnerabilities." That helped a bit, and it incidentally shines a light on just how early-stage IoT technology still is, with respect to security and distributed processing efficiencies.

Growing up
Though admittedly broad, the category with the most predictions centered on the theme of value and maturity in Big Data products supplanting the fascination with new features and products. Essentially, value and maturity are proxies for the enterprise-readiness of Big Data platforms.

Pentaho's Gallivan says that "the cool stuff is getting ready for prime time." MapR's Schroeder predicts "Shiny Object Syndrome Gives Way to Increased Focus on Fundamental Value," and qualifies that by saying "...companies will increasingly recognize the attraction of software that results in business impact, rather than focusing on raw big data technologies." In a related item, Schroeder predicts "Markets Experience a Flight to Quality," further stating that "...investors and organizations will turn away from volatile companies that have frequently pivoted in their business models."

Sean Ma, Trifacta's Director of Product Management, looking at the manageability and tooling side of maturity, predicts that "Increasing the amount of deployments will force vendors to focus their efforts on building and marketing management tools." He adds: "Much of the capabilities in these tools...will need to replicate functionality in analogous tools from the enterprise data warehouse space, specifically in the metadata management and workflow orchestration." That's a pretty bold prediction, and Ma's confidence in it may indicate that Trifacta has something planned in this space. But even if not, he's absolutely right that this functionality is needed in the Big Data world. In terms of manageability, Big Data tooling needs to achieve not just parity with data warehousing and BI tools, but needs to surpass that level.

The folks at Signals say "Technology is Rising to the Occasion" and explain that "advances in artificial intelligence and an understanding [of] how people work with data is easing the collaboration between humans and machines necessary to find meaning in big data." I'm not sure if that is a prediction, or just wishful thinking, but it certainly is the way things ought to be. With all the advances we've made in analyzing data using machine learning and intelligence, we've left the process of sifting through the output a largely manual process.

Finally, Mike Maciag, the COO at AltiScale, asserts this forward-looking headline: "Industry standards for Hadoop solidify." Maciag backs up his assertion by pointing to the Open Data Platform initiative (ODPi) and its work to standardize Hadoop distributions across vendors. ODPi was originally anchored by Hortonworks, with numerous other companies, including AltiScale, IBM and Pivotal, jumping on board. The organization is now managed under the auspices of the Linux Foundation.

Artificial flavor
Artificial Intelligence (AI) and Machine Learning (ML) figured prominently in this year's predictions as well. Splunk's Antani reasons that "Machine learning will drastically reduce the time spent analyzing and escalating events among organizations." But Lukas Biewald, Founder and CEO of Crowdflower insists that "machines will automate parts of jobs -- not entire jobs." These two predictions are not actually contradictory. I offer both of them, though, to point out that AI can be a tool without being a threat.

Be that as it may, Biewald also asserts that "AI will significantly change the business models of companies today." He expands on this by saying "legacy companies that aren't very profitable and possess large data sets may become more valuable and attractive acquisition targets than ever." In other words, if companies found gold in their patent portfolios previously, they may find more in their data sets, as other companies acquire them to further their efforts in AI, ML and predictive modeling.

And more
These four categories were the biggest among all the predictions but not the only ones, to be sure. Predictions around cloud, self-service, flash storage and the increasing prominence of the Chief Data Officer were in the mix as well. A number of predictions that stood on their own were there too, speaking to issues as far-reaching as salaries for Hadoop admins to open source, open data and container technology.

What's clear from almost all the predictions, though, is that the market is starting to take basic big data technology as a given, and is looking towards next-generation integration, functionality, intelligence, manageability and stability. This implies that customers will demand certain baseline data and analytics functionality to be part of most technology solutions going forwards. And that's a great sign for everyone involved in Big Data.

Source: ZDNet

Open source big data technologies like Hadoop have done much to begin the transformation of analytics. We're moving from expensive and specialist analytics teams towards an environment in which processes, workflows, and decision-making throughout an organisation can - in theory at least - become usefully data-driven. Established providers of analytics, BI and data warehouse technologies liberally sprinkle Hadoop, Spark and other cool project names throughout their products, delivering real advantages and real cost-savings, as well as grabbing some of the Hadoop glow for themselves. Startups, often closely associated with shepherding one of the newer open source projects, also compete for mindshare and custom.

And the opportunity is big. Hortonworks, for example, has described the global big data market as a $50 billion opportunity. But that pales into insignificance next to what Hortonworks (again) describes as a $1.7 trillion opportunity. Other companies and analysts have their own numbers, which do differ, but the step-change is clear and significant. Hadoop, and the vendors gravitating to that community, mostly address 'data at rest'; data that has already been collected from some process or interaction or query. The bigger opportunity relates to 'data in motion,' and to the internet of things that will be responsible for generating so much of this.

My latest report, Streaming Data From The Internet Of Things Will Be The Big Data World’s Bigger Second Act, explores some of the ways that big data vendors are acquiring new skills and new stories with which to chase this new opportunity.

For CIOs embarking on their IoT journey, it may be time to take a fresh look at companies previously so easily dismissed as just 'doing the Hadoop thing.' 

Source: Forrester.com, 

Gaining advantages with the IoT through 'Thing Management'

Some are calling the industrial Internet of Things the next industrial revolution, bringing dramatic changes and improvements to almost every sector. But to be sure it’s successful, there is one big question: how can organizations manage all the new things that are part of their organizations’ landscapes?

Most organizations see asset management as the practice of tracking and managing IT devices such as routers, switches, laptops and smartphones. But that’s only part of the equation nowadays. With the advent of the IoT, enterprise things now include robotic bricklayers, agitators, compressors, drug infusion pumps, track loaders, scissor lifts and the list goes on and on, while all these things are becoming smarter and more connected.

These are some examples for specific industries:

● Transportation is an asset-intensive industry that relies on efficient operations to achieve maximum profitability. To help customers manage these important assets, GE Transportation is equipping its locomotives with devices that manage hundreds of data elements per second. The devices decipher locomotive data and uncover use patterns that keep trains on track and running smoothly.

● The IoT’s promise for manufacturing is substantial. The IoT can build bridges that help solve the frustrating disconnects among suppliers, employees, customers, and others. In doing so, the IoT can create a cohesive environment where every participant is invested in and contributing to product quality and every customer’s feedback is learned from. Smart sensors, for instance, can ensure that every item, from articles of clothing to top-secret defense weapons, can have the same quality as the one before. The only problem with this is that the many pieces of the manufacturing puzzle and devices in the IoT are moving so quickly that spreadsheets and human analysis alone are not enough to manage the devices.

● IoT in healthcare will help connect a multitude of people, things with smart sensors (such as wearables and medical devices), and environments. Sensors in IoT devices and connected “smart” assets can capture patient vitals and other data in real time. Then data analytics technologies, including machine learning and artificial intelligence (AI), can be used to realize the promise of value-based care. There’s significant value to be gained, including operational efficiencies that boost the quality of care while reducing costs, clinical improvements that enable more accurate diagnoses, and more.

● In the oil and gas industry, IoT sensors have transformed efficiencies around the complex process of natural resource extraction by monitoring the health and efficiency of hard-to-access equipment installations in remote areas with limited connectivity.

● Fuelled by greater access to cheap hardware, the IoT is being used with notable success in logistics and fleet management by enabling cost-effective GPS tracking and automated loading/unloading.

All of these industries will benefit from the IoT. However, as the IoT world expands, these industries and others are looking for ways to track the barrage of new things that are now pivotal to their success. Thing Management pioneers such as Oomnitza help organizations manage devices as diverse as phones, fork lifts, drug infusion pumps, drones and VR headset, providing an essential service as the industrial IoT flourishes.

Think IoT, not IoP

To successfully manage these Things, enterprises are not only looking for Thing Management. They also are rethinking the Internet, not as the Internet of People (IoP), but as the Internet of Things (IoP). Things aren’t people, and there are three fundamental differences.

Many more things are connected to the Internet than people

John Chambers, former CEO of Cisco, recently declared there will be 500 billion things connected by 2024. That’s nearly 100 times the number of people on the planet.

Things have more to say than people

A typical cell phone has nearly 14 sensors, including an accelerometer, GPS, and even a radiation detector. Industrial things such as wind turbines, gene sequencers, and high-speed inserters can easily have over 100 sensors.

Things can speak much more frequently

People enter data at a snail’s pace when compared to the barrage of data coming from the IoT. A utility grid power sensor, for instance, can send data 60 times per second, a construction forklift once per minute, and a high-speed inserter once every two seconds.

Technologists and business people both need to learn how to collect and put all of the data coming from the industrial IoT to use and manage every connected thing. They will have to learn how to build enterprise software for things versus people.

How the industrial IoT will shape the future

The industrial IoT is all about value creation: increased profitability, revenue, efficiency, and reliability. It starts with the target of safe, stable operations and meeting environmental regulations, translating to greater financial results and profitability.

But there’s more to the big picture of the IoT than that. Building the next generation of software for things is a worthy goal, with potential results such as continually improving enterprise efficiency and public safety, driving down costs, decreasing environmental impacts, boosting educational outcomes and more. Companies like GE, Oomnitza and Bosch are investing significant amounts of money in the ability to connect, collect data, and learn from their machines.

The IoT and the next generation of enterprise software will have big economic impacts as well. The cost savings and productivity gains generated through “smart” thing monitoring and adaptation are projected to create $1.1 trillion to $2.5 trillion in value in the health care sector, $2.3 trillion $11.6 trillion in global manufacturing, and $500 billion $757 billion in municipal energy and service provision over the next decade. The total global impact of IoT technologies could generate anywhere from $2.7 trillion to $14.4 trillion in value by 2025.

Author: Timothy Chou

Source: Information-management

Het Internet of Things brengt allerlei beveiligingsrisico's met zich mee, maar ervaren experts die hierbij kunnen helpen zijn schaars, zo stelt martkvorser Gartner. Volgens Gartner zijn beveiligingstechnologieën vereist om alle Internet of Things-apparaten tegen aanvallen te beschermen.

Het gaat dan om zowel bekende als nieuwe aanvallen. Gartner wijst naar aanvallen waarbij aanvallers zich als bepaalde apparaten voordoen of "denial-of-sleep-aanvallen" uitvoeren, om zo de batterij van apparaten leeg te maken. De beveiliging van het Internet of Things wordt verder gecompliceerd door het feit dat veel van de apparaten eenvoudige processoren en besturingssystemen gebruiken die geen complexe beveiligingsoplossingen ondersteunen. Ook is er een tekort aan expertise.

"Ervaren IoT-beveiligingsspecialisten zijn schaars, en beveiligingsoplossingen zijn op het moment gefragmenteerd en bestaan uit verschillende leveranciers", zegt Nick Jones, vicepresident en analist bij Gartner. Jones voorspelt dat er de komende jaren nieuwe dreigingen voor het Internet of Things zullen verschijnen, aangezien hackers nieuwe manieren vinden om aangesloten apparaten en protocollen aan te vallen. Dit zou inhouden dat veel van de IoT-apparaten gedurende hun levenscyclus van hardware- en softwareupdates moeten worden voorzien.

Source: Security.nl

Wat als IoT gewoon een overkoepelende term zou zijn voor manieren om iets bruikbaars te maken uit machine-gegenereerde data? Bijvoorbeeld, een bus vertelt mijn telefoon hoe ver mijn bushalte is en mijn fietsverhuur vertelt me ​​hoeveel fietsen beschikbaar zijn?

In 2014 vroeg IDC 400 C-suite professionals wat volgens hen IoT was. De antwoorden varieerden van soorten apparaten (thermostaten, auto's, home security-systemen) tot uitdagingen (beveiliging, data management, connectiviteit). Dezelfde analist benadrukt ook dat de wereldwijde markt voor IoT oplossingen zal groeien van 1,9 biljoen in 2013 tot 7,1 biljoen dollar in 2020. Dit optimisme wordt ondersteund door Gartner’s inschatting: 4,9 miljard gekoppelde 'dingen' zullen in 2016 in gebruik zijn. In 2020 zullen dat er 25 miljard zijn.
Met andere woorden: IoT is zeer divers en het potentieel is enorm. De waarde ligt niet alleen in de kosten van de sensoren. Het is veel meer dan dat.

Wanneer IoT begint te vertellen
Het IoT is niet iets dat op zichzelf staat. Het rijpt naast big data. Het uitrusten van miljarden objecten met sensoren is van beperkte waarde als het niet mogelijk is miljarden datastromen te genereren, verzenden, opslaan en te analyseren.
De datawetenschapper is de menselijke choreograaf van dit IoT. Zij zijn essentieel voor het identificeren van de waarde van de enorme hoeveelheid data die al deze apparaten genereren. En dat is de reden waarom connectiviteit en opslag zo belangrijk zijn. Kleine geïsoleerde apparaten zonder opslag en weinig rekenkracht vertellen ons weinig. Alleen door naar grote verzamelingen data te kijken kunnen we correlaties ontdekken en wordt het mogelijk trends te herkennen en voorspellingen te doen.
In elke zakelijke omgeving, is het scenario identiek: de CxO zal de informatie die er vandaag is bekijken ten opzichte van informatie die er was in het verleden om een voorspelbaar inzicht te krijgen in wat er gaat gebeuren in de toekomst.

Sneller inzicht leidt tot concurrentievoordeel
CxO’s willen tegenwoordig een ander soort bedrijf. Ze willen dat het in een snel tempo opereert en reageert op de markt, maar ze willen ook beslissingen nemen op basis van intelligentie verzameld via big data. En ze willen de beste producten maken, gebaseerd op klantinzicht. Bedrijven zijn op zoek naar een disruptief business model waardoor ze steeds meer in kunnen spelen op trends in de markt en daarmee een voorsprong hebben op de concurrentie.

Start-up gedrag
Het antwoord ligt in de volgende vraag aan bedrijven: "Waarom kunnen ondernemingen zich niet meer als start-ups gedragen?" Dit gaat niet over het maken van overhaaste beslissingen met weinig of geen overzicht. Het gaat over het aannemen van een slank business model dat onzekerheid en uitgerekte budgetten tolereert. En nog belangrijker, het gaat over hoe het management van het bedrijf een cultuur van slagvaardigheid neerzet.
De organisaties die zullen winnen in het big data spel zijn niet degenen die de meeste of de beste toegang ertoe hebben. De winnaars omschrijven duidelijk hun doelen, zetten de nodige operationele grenzen en stellen vast wat de uitrusting is die nodig is om de klus te klaren.

Leidende rol CIO's
CxO’s hebben de zakelijke waarde van IT erkend, en willen dat CIO's meer een leidende rol nemen en in kaart brengen wat de toekomst is van het bedrijf. IT kan een enorme rol spelen in de bouw van die toekomst door samen te werken met de business en de tools te verschaffen die nodig zijn om productief te zijn. Technologie kan voortdurende innovatie op elk niveau vergemakkelijken, waardoor het bedrijf niet alleen kan overleven maar floreren.
Het is niet niks om deze wens van bedrijven te bereiken. Maar samenwerken met technologie maakt het veel haalbaarder omdat het bedrijven in staat stelt tot een wendbare, innovatieve, data-gedreven toekomst te komen.

Source: ManagersOnline

How digital transformation drives innovation at health clubs

‘Wellness’ was the most talked about topic during the entire lockdown. With nowhere to go, it took a big hit initially but soon it found solutions in digital transformation. Thriving at a faster pace, this industry accelerated the implementation of digitization, resorted to healthcare app development and replaced its offerings with digital classes, on-demand content and live streaming.

The wellness industry runs on majorly three pillars- nutrition, fitness and travel. Health clubs are part of the fitness segment of this industry which focuses on whole-body wellness, unlike gyms where the focus is devoted to physical wellness more. A health club offers a comprehensive fitness approach, providing recreational sports and exercise facilities at one place to consumers.

According to studies, health clubs globally witness an increase of 4.6% annually. Similar to other industries, health club industry also benefited from technology during the pandemic by including healthcare app development, online on-demand services, etc, in its offering.

How digital transformation is useful for health clubs?

Fitness was never this important for people as it has been over the last few years. During the lockdown, digital solutions were welcomed to reach those lockdown fitness goals. This sudden need for a shift to digitization paved the path for many interactive and innovative fitness solutions. Even though the industry started this transformation a little late, it adapted quickly to this change.

Engaging with the members

Engaging with the clients is the root driver of digitization for health clubs. Omni-channel trends were existing but engaging with clients accelerated during this period. Brands sought after pre-recorded content, live streaming for engaging with their members. They also used text experiences to connect with their clients.

Using the technology to bridge the gap of not being accessible physically, clubs who launched their apps to have a platform for their content, stood out of the crowd. The virtual offering made it easy for clients to continue their fitness routine. Many took help from the healthcare app development companies for making this transition. Pretty sure, your gym trainer was teaching you over WhatsApp and Zoom. Wasn’t he?

Making operations easy

Digitization brings with it, ease of operations. In every industry, digital transformation has proved to improve functioning and reduced costs over time by streamlining the processes. Health clubs also took advantage of the technology to reduce their operational costs and provide an easy, seamless member experience through CRM systems and easy bookings.

Bringing personalized experience

Digitization helped health clubs to bring personalized experiences for their clients. Mobile applications enabled brands to provide personal fitness plans to each of their clients based on their needs. This was earlier done face to face but with digitization, this process has become seamless, easy and virtual. Healthcare app development integrated with AI takes this to another level.

Using data

Data is of prime importance when we talk about digital transformation in healthcare. Using the data collected through IoT devices and applications, brands can develop a 360-degree view of the client’s needs. Offering a comprehensive personalized solution to their problems, it also helped in learning about the behavior and preferences by using the algorithms and executing personalized outreach.

The data can also help the brand to anticipate the fitness needs of their clients by using AI-driven methods. People need solutions to problems and data can help brands discover those problems even before their clients discover them.

Digging deeper into a fitness experience

The IoT devices help brands in expanding this digital transformation. By integrating wearable devices, applications in their health club experience, brands can elevate their member experience. They can also provide a deeper insight into their fitness regimen through analytical analysis of the data collected. There are some health clubs who are pioneering this segment.

A roadmap to how Health Clubs can leverage the benefits of digital transformation

Digital transformation in healthcare is not an easy process. Simply investing in technology will not suffice. Digital transformation in healthcare requires a stringent cultural shift. It applies to health clubs as well.

Understanding the members and the experience you want to serve based on your brand is important. This helps in ensuring that the new technology and the fitness experience you want to serve your members complement each other. They should be in sync with each other and bring an authentic fitness experience.

From the numerous ways that health clubs can leverage the benefits of digital transformation, we have a few in the list.

Smart machines: Exercise and gym equipment that can be connected to the cloud and is responsive to an individual, is a popular thing among health clubs and gyms. Some of these smart equipment are even programmed with machine learning technology that enables self-learning and improvising the fitness journey of the user.

3D body scanning: This technology can be used to create effective and accurate personal programs targeting the specific problems of the clients.

Using SMS and texting services: Apart from providing only an exercise routine, a health club can also leverage the use of SMS or text services to motivate their members and give health reminders. This can also be used to send out awareness messages on mental health and other issues concerning the members. This will help in creating a more connected engagement channel.

Wearable 3rd party devices: Health clubs can provide members with 3rd party wearable devices to track their progress and vitals inside and outside the gym for providing in-depth analysis.

Digital transformation in healthcare has proved to be of supreme advantage. Adapting to advanced technology in the health club industry has and will prove to be groundbreaking. Not only does it bring efficiency in operations, but it also gives a competitive advantage. 

Author: Robert Jackson

Source: Datafloq

Every minute of every day, mind-blowing amounts of data are generated. Twitter users send 347,222 tweets, YouTube users upload 300 hours of video, and Google receives more than four million search queries. And in a single hour, Walmart processes more than a million customer transactions. With the Internet of Things accelerating at lightning speed – to the tune of 6.4 billion connected devices in 2016 (up 30 percent from 2015) – this already staggering amount of data is about to explode. By 2020, IDC estimates there will be 40 zettabytes of data. That’s 5,200 GB for every person on the planet.

This data is a gold mine for businesses. Or, at least, it can be. On its own, data has zero value. To turn it into a valuable asset, one that delivers the actionable intelligence needed to transform business, you need to know how to apply analytics to that treasure trove. To set yourself up for success, start out by answering these questions:

What Is the Size, Volume, Type and Velocity of your Data?

The answers to this will help you determine the best kind of database to store your data and fuel your analysis. For instance, some databases handle structured data, and others are focused on semi-structured or unstructured data. Some are better with high-velocity and high-volume data.

 Which Analytics Use Cases will You Be Supporting?

The type of use cases will drive the business intelligence capabilities you’ll require (Figure 1).

• Analyst-driven BI. Operator seeking insights across a range of business data to find cross-group efficiencies, profit leakage, cost challenges, etc.
• Workgroup-driven BI. Small teams focused on a sub-section of the overall strategy and reporting on KPIs for specific tasks.
• Strategy-driven BI. Insights mapped against a particular strategy with the dashboard becoming the “single source of truth” for business performance.
• Process-driven BI. Business automation and workflow built as an autonomic process based on outside events.

Where Do You Want your Data and Analytics to Live?

The main choices are on-premises or in the cloud. Until recently, for many companies – particularly those concerned about security – on-prem won out. However, that’s changing significantly as cloud-based solutions have proven to be solidly secure. In fact, a recent survey found that 40 percent of big data practitioners use cloud services for analytics and that number is growing.

The cloud is attractive for many reasons. The biggest is fast time-to-impact. With cloud-based services you can get up and running immediately. This means you can accelerate insights, actions, and business outcomes. There’s no waiting three to four months for deployment and no risk of development issues.

There’s also no need to purchase and install infrastructure. This is particularly critical for companies that don’t have the financial resources or skills to set up and maintain database and analytics environments on-premises. Without cloud, these companies would be unable to do the kind of analyses required to thrive in our on-demand economy. However, even companies that do have the resources benefit by freeing up people and budget for more strategic projects.

With data and analytics in the cloud, collaboration also becomes much easier. Your employees, partners, and customers can instantly access business intelligence and performance management.

Cloud Options

There are a number of cloud options you can employ. Here’s a quick look at them:

Infrastructure as a Service (IaaS) for generalized compute, network, and storage clusters. IaaS is great for flexibility and scale, and will support any software. You will be required to install and manage the software.

Database as a Service (DBaaS), where multi-tenant or dedicated database instances are hosted by the service provider. DBaaS also is great for flexibility and scale, and it offloads backups and data management to the provider. Your data is locked into the provider’s database solution.

Analytics as a Service (AaaS) provides complex analytics engines that are ready for use and scale as needed, with pre-canned reports.

Platform as a Service (PaaS) is similar to DBaaS in that it scales easily and that application backups and data management are handled by the provider. Data solutions themselves are often add-ons.

Software as a Service (SaaS) is when back office software is abstracted through a hosted application with data made available through APIs. Remote analytics are performed “over the wire” and can be limiting.

How you leverage data can make or break your business. If you decide to go the cloud route, make sure your service provider’s database and analytics applications fit your current and evolving needs. Make sure the provider has the expertise, infrastructure, and proven ability to handle data ebbs and flows in a way that’s cost-effective for you and, equally important, ensures that your performance won’t be compromised when the data tsunami hits. Your business depends on it.

 Source: DataInformed

How to translate IIoT investments to ROI

A digital transformation takes time, sometimes a considerable amount. This means it can be difficult to quantify ROI, at least in the short term. Return on investment for IIoT (Industrial Internet of Things) relies entirely on the data collected with the technology, and how it’s applied. The information itself may be incredibly valuable, but that won't matter if it's used ineffectively, further reducing the leverage.

Real-time insights provide more of a direct influence on operations, offering minimal boons to a variety of business facets. That said, measuring real ROI is about the big picture and how all those smaller wins come together to provide a wholly effective strategy.

It’s difficult to ascertain the ROI of IIoT and gauge whether or not you’re on the right track in the first place.

Spending on IoT remains high for many industries, but the ROI is still up in the air. About 72% of construction business operators include new tech adoption as part of their strategic plan or vision for the future. Despite that, only 5% see themselves on the cutting edge of adoption. Here are some tips that can help you better plan industrial IoT adoption, while also getting the most out of the new technologies:

Choose an objective

Industrial IoT is an incredibly broad field that encompasses nearly every device, machine and process that exists today, and beyond. Just because the technology can be outfitted to work with every system in a facility doesn’t mean that’s what should happen.

Before moving forward with any form of implementation, every organization should choose an objective for its IIoT campaign. What is the technology going to achieve? Should it be used to improve manufacturing efficiency? Will it help sync up workers across the plant floor? Is it better suited for fleet management and asset tracking?

While it would be great to have multiple potential solutions in place, it would be nearly impossible to verify the ROI after doing so. By selecting a single objective and following through, data teams can adopt a more systematic approach that provides more accurate insights. In the end, it allows decision-makers to see firsthand whether IoT is a proper investment and worth pursuing on a larger scale.

If nothing else, deploying IIoT with the intent to eliminate bottlenecks in existing processes is a great place to start.

Go process by process

With all the hype surrounding digitization and modern technologies, it's easy to get swept up in the tide. Overhauling every aspect of a business to honor advanced digital solutions may seem like a great idea, initially. The reality is that taking it all on at once is likely going to fail. For instance, switching to a paperless operation while simultaneously installing new IoT sensors on the factory floor will cause more confusion than positive support.

As Harvard Business Reviews’ Digital Transformation of Business report states, merely spending more on cutting-edge technologies does not guarantee a positive outcome.

The real winners will be the 'companies that both identify which core business capabilities they need to differentiate and make a commitment to transform these core business capabilities with the right digital technology'.

Instead, take a look at the processes and systems currently in place and identify what will see the most significant boon from digitization. Choose one or two, and then get to work. Once the ball is rolling, it’s going to take time and resources to implement the proper solutions. New technologies will need to be installed, which means old equipment and tools might need to be phased out or upgraded. Employees will need training, and they may also need their own set of improved tools. Leadership will need to come up with new strategies for working with upgraded systems>, communicating with their workers and taking action.

It’s a long, demanding process. Not something that happens overnight. That’s precisely why it’s best to take it one step at a time and focus on a single process or solution. Once a particular department or task is honed, then it’s time to move on to other digitization projects within the company.

Choose a reliable vendor

With new technologies it’s best to work with a vendor or specialist that already has considerable experience. Yes, it’s possible to develop an in-house IoT solution that’s also managed by a proprietary IT crew. It’s also a lot more costly and more likely that problems will arise as a result.

Third-party vendors have more resources at their disposal merely because it’s what they do, exclusively. They tend to have more robust IT and security solutions, along with the appropriate human resources to keep everything safe. They can handle installation, upgrades and repairs, which takes the responsibility away from the leading organization. They also provide comprehensive support for when problems or questions do arise.

Implement predictive operations with IIoT

Predictive maintenance is something relatively new in the industrial field, made possible thanks to IIoT and the real-time insights it can deliver. Data can reveal hidden details about working machinery, output, potential errors and more. Collectively, it provides a detailed report about performance, allowing decision-makers to pinpoint what areas of the operation are lacking. They can take action, sooner rather than later, to correct any issues and replace ailing equipment.

It’s a process that should be deployed across the entire operation instead of solely for maintenance. It can be used for a lot more than just predicting when equipment is going to fail, too. Employing machine learning and analytics applications can reveal when and how supplies are going to thin out, demand trends, and much more. Another term for this is business intelligence. Predictive operations throug big data analysis are one facet of business intelligence, albeit an incredibly lucrative one.

Invest in IoT for predictive operations and ROI will innately improve.

Improving ROI even before it can be measured

These tips offer just a few ways that organizations can improve the ROI of IIoT implementation, even through preplanning. It may be difficult to quantify the real value of the technology upfront. Nonetheless, honoring these processes can help realize the bigger picture, which is something business leaders always demand.

Author: Megan Nichols

Source: Datafloq

Machine learning in the agricultural sector

Food is a basic need of human beings that is now satisfied through farming. Machine learning in agriculture can optimize the way food gets to our table and revolutionize one of the most critical sectors of the economy.

Machine learning seems to be a perfect tool for this purpose. It can help us increase efficiency and accuracy in decision-making while simultaneously minimizing risks and costs associated with agricultural operations.

Some companies make use of AI software in agriculture by utilizing machine learning for various processes. These tools can make a real difference in agricultural productivity and profitability by reducing waste while enhancing product quality.

Today, we’ll have a closer look at machine learning in agriculture applications and techniques. We’ll also touch upon crucial ML benefits for business and the current state of artificial intelligence and machine learning in agriculture.

How machine learning can be used in agriculture: main drivers

Agriculture is an essential sector for any economy. Unfortunately, its market is volatile. Thus, the drought can easily impact future commodity prices and have serious repercussions on all food prices. Moreover, farming is an extremely challenging undertaking.

Climate change, soil erosion, and biodiversity loss can cripple the business, as are customers’ shifting tastes in food. The natural environment with which farming interacts continues to present its own set of problems. In addition to a growing population, sustainable agriculture is also threatened by urbanization. And the only way to meet the growing food demand is to keep track of crops, the environment, and the market.

This is when machine learning applications in agriculture step on the scene. By analyzing real-time sensor data and historical trends, this technology can empower farming decision-making. This will help manufacturers better predict demand, improve crop yields and reduce food production costs.

The current statistics attest to the rise of artificial intelligence and machine learning in agriculture:

1. In 2021, the AgTech market value in North America equaled 6.2 billion dollars. The sector segments include management platforms as well as supply chain and inventory management solutions. Field mapping services, monitoring, and micro-farming software also constitute a large part of Agtech.
2. Spending on AI technologies and solutions in agriculture will increase from $1 billion in 2020 to $4 billion in 2026, representing a 25.5 percent compound annual growth rate (CAGR).
3. Smart, connected agriculture’s fastest-growing technology area is IoT-enabled Agricultural (IoTAg) monitoring, which is expected to reach $4.5 billion of market value by 2025.

The benefits of tech advancement are better visible in the applications that use machine learning. With that said, let’s look at the most popular application of machine learning in agriculture.

Top application of machine learning in agriculture

The advent of IoT and other technologies has created a breeding ground for real-time monitoring. Machine learning applications in agriculture rely on real-time data to deliver exponential gains for farmers. AI and machine learning prove to be strong catalysts driving 24/7 security of remote facilities, better yields, and pesticide effectiveness. Yet, smart machine learning solutions don’t end there.

Crop management

Crop management is a huge layer of pre-harvesting activities that is responsible for future yields. However, this is one of the most challenging stages of the agricultural lifecycle. Increased frequency of drought, higher temperatures, unpredictable wetting, and drying cycles can influence crop resistance. Therefore, machine learning development is widely leveraged to amplify this stage.

For example, crop variety selection is one of machine learning in agriculture applications and techniques. To become disease- and weather-resistant, crops should have the right gene sequence. ML-based deep learning can simplify the task of crop breeding. Algorithms simply collect field data on plant behavior and use that data to develop a probabilistic model.

Crop yield prediction is another instance of machine learning in the agriculture sector. The technology amplifies decisions on what crop species to grow and what activities to perform during the growing season. Tech-wise, crop yield is used as a dependent variable when making predictions. The major factors include temperature, soil type, rainfall, and actual crop information. Based on these inputs, ML algorithms like neural networks and multiple linear regression produce forecasts.

Precision spraying

Crop health heavily depends on spraying to prevent the infestation of pests and diseases. Machine learning projects in agriculture address this area as well. Precision or targeted spraying is the technology that takes the best from intelligent software and computer vision in the agriculture sector. Thus, the technology obtains the target information such as the size and shape of the plant, and then applies herbicides as needed.

The benefit of this technique is that it allows for a more precise application of pesticides and fertilizers based on crop type. Precision spraying involves images and spectral signatures of plants, soil, and other substances to determine which chemicals should be applied. This technology minimizes the risk of crop damage while maximizing crop yield.

Israelis Greeneye Technology is a prominent example of this use case. Their AI-enabled precision spraying technology is proven to cut herbicide use by 78% and minimize costs by more than 50%. The software is compatible with any brand or size of the commercial sprayer, eliminating the need for farmers to shell for new equipment.

Insect detection

Insects are a major threat to crops in agricultural facilities. Each year, between 20 to 40 percent of global crop production is lost to pests. To protect the facility, farmers use pesticides, but this not only kills the insects, but also the other small pests that live around the farm.

Therefore, discerning the “bad actors” is difficult when done manually. The use of drones to detect insects in farming is not new, but the use of machine learning in this process has seen an increase recently. Thus, machine learning companies help farmers label pests to capture and identify them.

To do that, data engineers first use real-time images of insects. In a research project, they then based the detection and coarse counting method on YOLO object detection, and the classification and fine counting on Support Vector Machines (SVM) using global features. Combined, this data allows a computer vision model to accurately identify bees, flies, mosquitoes, moths, chafers, and fruit flies with an accuracy of over 90% and counts them with an accuracy of over 92%.

Field conditions management

One of the most important aspects of farming is soil and water management. But with such a large number of variables, it can be hard to manage them all.

That is why farmers collect information about soil and water conditions on their farms. The latter can then be input into a computer-controlled system, and produce recommendations for fertilizer application rates, pest control options, and irrigation schedules based on the weather forecast.

Machine learning is also implemented to calibrate soil sensors. These joined forces then help predict nutrient deficiencies and water stress. In agriculture, soil moisture impacts crucial farm activities from crop selection to timing of tilling and harvesting. Moisture is usually predicted using weather data and variables from soil and crops. Then empirical, regression, and machine learning methods amplify the prediction. This application allows for more data-driven water resources planning, better yields, and reduced costs.

Yield mapping

Every farmer wants to get the most out of their crops and reap all of the possible benefits. One way to do so is by planning your harvest and determining what each field can produce. One of the most exciting developments in this area is called ‘sensing and mapping’. This technique refers to using imaging techniques and digital image processing to map a field’s yield.

Essentially, yield mapping is a part of precision agriculture. It helps highlight the differences in the soil in various regions of the farm. Mapping also offers data on moisture content and enables the farmer to address various related issues in the farm.

A common approach to yield mapping is using machine learning to use data from past years. This way, farmers can identify which area will be most and least favorable for crops. This application can also involve a variety of sensors like grain flow and header position ones to generate additional insights. Yield mapping also acts as a complementary technology in perfecting fertilizer usage.

Livestock management

Animal welfare and livestock production are also among salient machine learning in agriculture applications and techniques. The technology can be applied to a number of different areas. These include animal welfare assessment, predictive modeling of animal production, as well as estimating the environmental impact from livestock operations.

Thus, farmers can get a better idea of livestock well-being by monitoring vitals, daily activity levels, and food intake. In Uganda, farmers detect livestock diseases 2 days before they manifest. This technology relies on a chip with a sensor that is connected to an RFID reader, and users’ mobile phones or computers. This way, the software can detect and monitor most health aspects – from eating to fertility.

Animal behavior classification can also identify a link between chewing signals to the need for diet changes. The amount of animal stress is also tracked by classifying their movement patterns. This application of machine learning in agriculture generates whole new insights into how farms might be more profitable and better manage livestock.

Price forecasting for crops

For decades, economists have tried to forecast prices for crops using statistical models. But with the advent of machine learning, there are now cutting-edge ways to get a much more accurate prediction of crop prices. These forecasts can become invaluable assets for making better economic decisions.

The prices of agricultural commodities are volatile. They are subject to numerous variables, including climate, government policies, demand, and others. Machine learning in agricultural and applied economics can help organizations understand price fluctuations and offer risk management measures. For example, the government can support the sector by providing loans. Farmers can also get prepared by increasing or decreasing crop production.

Price prediction is also helpful for deciding on the types of crops for planting. From a technical standpoint, predictive analytics relies on agriculture datasets for machine learning. A great number of factors are fed into the algorithms. The latter may include historical pricing, location, climate, crop data, and others. These variables, in turn, come from a variety of data sources such as aerial imagery, weather sensors, and others.

Automatic weeding

Weed control is a very important task for agricultural production. It is necessary to identify the weed types and then remove them so that the crop yield doesn’t suffer. Manual weeding can take up a large chunk of time, while also being back-breaking work.

Some years ago, farmers resorted to using herbicides, but those have been shown to have negative effects on the environment. A new alternative is already on the horizon – a robot programmed with machine learning technology.

This year, a UK farmbot field trial demonstrated a robot-assisted approach to weeding. The killer robot, called Dick, can target individual weeds in arable crops and eliminate broad-leaved weeds using pattern recognition. Its counterpart, Wilma, collects field data, including weed location. Wilma then transmits the data to Dick and offers ways to enhance yields at the individual plant level.

Another weed assassin, the Carbon robotics weeder, relies on high-resolution cameras and an intelligent camera processing unit. Eight lasers, each with 150 watts of power, are used to detect weeds in the bed. The lasers then burn out the weeds at the point of growth and prevent them from further growth.

Automatic harvesting robots

A recent study revealed that fruit and vegetable production globally has increased by more than 50% in the past 20 years. With this increasing production rate, there is a need to develop better methods for harvesting, sorting, and eventually transporting produce to various destinations.

To do so, the best machine learning companies are striving to build intelligent tools to automate the harvesting of agricultural produce. These technologies help the robots to be more accurate with picking out specific types of fruits or vegetables based on their shape, size, or color. The use of automatic robots helps minimize the harvesting time which ultimately increases the profits of the farmers.

The mechanical harvesting of potatoes and carrots isn’t new. However, leafy and cool-season vegetables used to present the biggest challenge for harvesting. Since they cannot withstand rough handling, their picking used to be manual.

Today, ML-powered robots can identify and harvest this type of produce. Dubbed Vegebot, the automated harvester can recognize and harvest vegetables when maintaining their quality. Moreover, robots can potentially minimize food waste by performing multiple passes on the same field. This will help farmers pick ripe vegetables that weren’t ready for harvesting during previous passes.

The final word

Today, machine learning in agriculture is one of the fastest-growing areas. Its applications in farming range from simple analytics systems to complex robotics hardware. Therefore, a growing number of stakeholders are raising awareness of the potential advantages of using ML agriculture and collaborating with Data Science and AI companies to get reliable input data for the data analyses.

Data-driven approaches, in turn, foster better decision-making, greater efficiency, and less waste. In the coming years, we’ll witness more digital agriculture with a projected market value of $4.0 billion by 2026.

Author: Tatsiana Isakova

Source: InDataLabs

Internet of Things technology is a hot topic. You can’t read a tech news site without coming across at least one mention of IoT. But if you’re looking to take advantage of sensors, you will likely have to update your data store to handle the workload. Once you’re set up data-wise, get ready to monitor everything from weather and the environment to overseas factory floors and even fleets of trucks.

Why NoSQL for IoT?

You might think your data needs for sensors are as tiny as these little devices, but there are several reasons you should consider a NoSQL database.

The first reason is that these sensors can send huge amounts of data since they run 24/7. All of that data adds up to the need for a larger storage capacity. While you might be tempted to use an RDBMS, relational databases were never really meant to deal with the kind of data that sensors generate. For one thing, sensor data doesn’t always make sense in tabular format.

SQL was originally designed for relatively static data structured as a table. Data from sensors can change a lot and provides a continuous stream. And you need to be able to add or remove entries on the fly, which can prove difficult with relational databases.

NoSQL databases are also more scalable, offering flexibility in data models. You can have a structure similar to SQL with wide tables, or you might choose to go with a document-oriented database, key-value database, or graph database. Time series databases are one of the more obvious choices for Internet of Things applications specifically.

Some businesses may join the big data revolution without knowing where they are actually going to store their data. You could have a cluster dedicated to your data and another to your analytics, but that’s expensive. Wouldn’t it be great if you could have your data and analytics in the same cluster? NoSQL eliminates budget waste for those with two different clusters that amount to the same thing.

Applications

So now that you’ve got your IoT-capable database, what can you do with Internet of Things technology?

In “The Only Living Boy in New York,” Paul Simon famously got all the news he needed on the weather report. While the weather might seem to most of us like no more than a cliche, “safe” topic for conversation, for many people, receiving the weather report is a matter of safety and survival.

The severe weather that has impacted much of the U.S. in recent years shows how timely weather forecasts can save lives by allowing forecasters to give accurate, quick, and up-to-the-moment warnings and alerts. Both the National Weather Service and private forecasters use sophisticated models to predict the weather, and those models get better all the time. One of the primary reasons they continue to improve is that the forecasters feed the programs with real data gathered from weather stations around the world. The Weather Channel acquired Weather Underground largely for its extensive network of weather stations operated by enthusiasts.

If you’re not interested in weather monitoring, IoT offers other options, such as monitoring pollution. Sensors can measure particles in the air, or chemicals and bacteria in the water. Agencies could use this information to plan congestion pricing for commuters or direct cleanup resources.

Nearly every State in the U.S. has called for more manufacturers to bring jobs back to the U.S. instead of offshoring, but manufacturers cite high costs as a reason to keep factory jobs overseas. One way to monitor operations abroad is to deploy IoT on factory floors. While automated process control is nothing new, what is new is the ability to connect directly to factory floors from around the world. Businesses can monitor production and instantly track problems before they become big ones.

One of the biggest successes for Internet of Things industry is in logistics, particularly fleet tracking. Trucking companies can see instantly where their vehicles are, and customers can know exactly where their stuff is. Managers can even track fuel usage and see when trucks are due for maintenance. All of these factors help logistics companies cut costs, save fuel, and keep customers.

Conclusion

NoSQL may be just the solution you need to venture into IoT technology. With the ability to handle the vast workloads from sensors running 24/7, you’ll be able to react to new situations quickly. NoSQL can help you save money, save time, and even save lives. 

Source: Smartdatacollective

Despite exponential increases in data storage in the cloud along with databases and the emerging Internet of Things (IoT), IT security executives remain worried about security breaches as well as vulnerabilities introduced via shared infrastructure.

A cloud security survey released Wednesday (Feb. 24) by enterprise data security vendor Vormetric and 451 Research found that 85 percent of respondents use sensitive data stored in the cloud, up from 54 percent last year. Meanwhile, half of those surveyed said they are using sensitive data within big data deployments, up from 31 percent last year. One-third of respondents said they are accessing sensitive data via IoT deployments.

The upshot is that well over half of those IT executive surveyed are worried about data security as cloud usage grows, citing the possibility of attacks on service providers, exposure to vulnerabilities on shared public cloud infrastructure and a lack of control over where data is stored.

Those fears are well founded, the security survey notes: “To a large extent both security vendors and enterprises are like generals fighting the last war. While the storm of data breaches continues to crest, many remain focused on traditional defenses like network and endpoint security that are clearly no longer sufficient on their own to respond to new security challenges.”

Control and management of encryption keys is widely seen as critical to securing data stored in the cloud, the survey found. IT executives were divided on the question of managing encryption keys, with roughly half previously saying that keys should be managed by cloud service providers. That view has shifted in the past year, the survey found, with 65 percent now favoring on-premise management of encryption keys.

In response to security concerns, public cloud vendors like Amazon Web Services, Google, Microsoft and Salesforce have moved to tighten data security through internal development, partnerships and acquisitions in an attempt to reduce vulnerabilities. Big data vendors have lagged behind, but the survey noted that acquisitions by Cloudera and Hortonworks represent concrete steps toward securing big data.

Cloudera acquired encryption and key management developer Gazzang in 2014 to boost Hadoop security. Among Hortonworks’ recent acquisitions is XA Secure, a developer of security tools for Hadoop.

Still, the survey warned, IoT security remains problematic.

When asked which data resources were most at risk, 54 percent of respondents to the Vormetric survey cited databases while 41 percent said file servers. Indeed, when linked to the open Internet, these machines can be exposed vulnerabilities similar to recent “man-in-the-middle” attacks on an open source library.

(Security specialist SentinelOne released an endpoint platform this week designed to protect enterprise datacenters and cloud providers from emerging threats that target Linux servers.)

Meanwhile, the top security concerns for big data implementations were: the security of reports that include sensitive information; sensitive data spread across big data deployments; and privacy violations related to data originating in multiple countries. Privacy worries have been complications by delays in replacing a 15-year-old “safe harbor” agreement struck down last year that governed trans-Atlantic data transfers. A proposed E.U.-U.S. Privacy Shield deal has yet to be implemented.

Despite these uncertainties and continuing security worries, respondents said they would continue shifting more sensitive data to the cloud, databases and IoT implementations as they move computing resources closer to data. For example, half of all survey respondents said they would store sensitive information in big data environments.

Source: Datanami

The 8 most important industrial IoT developments in 2019

From manufacturing to the retail sector, the infinite applications of the industrial internet of things (IIoT) are disrupting business processes, thereby improving operational efficiency and business competitiveness. The trend of employing IoT-powered systems for supply chain management, smart monitoring, remote diagnosis, production integration, inventory management, and predictive maintenance is catching up as companies take bold steps to address a myriad of business problems.

No wonder, the global technology spend on IoT is expected to reach USD 1.2 trillion by 2022. The growth of this segment will be driven by firms deploying IIoT solutions and giant tech organizations who are developing these innovative solutions.

To help you stay ahead of the curve, we have enlisted a few developments that will dominate the industrial IoT sphere.

1. Cobots are gaining popularity

Digitization is having a major impact in the industrial robotics segment as connected cobots or collaborative robots, making their place in the smart manufacturing ecosystem. This trend is improving the efficiency of operations and the reliability of the production cycle.

IIoT is making robots mobile and collaborative, offering technologies, such as self-driving vehicles (mobile collaborative robots), machine vision (part identification), and additive manufacturing that can boost production efficiency and business growth with an excellent ROI. No wonder, the global cobots market size has crossed USD 649 million in 2018 and is expected to expand at a CAGR of 44.5% between 2019 and 2025.

2. Digital twins are on the rise

A growing number of firms are deploying IoT solutions to develop a digital replica of their business assets. Thus, instead of sending data to each physical receiver separately, all the information is sent to the digital twin, enabling business units to access the data with ease.

Digital twins are growing in popularity as they decrease the complexity of the IoT ecosystem while boosting its efficiency. Gartner shares that 24% of enterprises are already using digital twins and an additional 42% plan to ride on this wave in the coming three years.

Smart businesses are already using digital twin software to incorporate process data, enabling them to reach accurate insights and address operational inefficiencies.

3. Augmented reality is disrupting the manufacturing domain

AR is benefiting the manufacturing domain in more ways than one. The technology has disrupted the manufacturing areas like product design and development, maintenance and field service, quality assurance, logistics, and hands-on training of new employees.

For instance, in the assembling operations, AR is replacing the traditional paper instruction manual with IoT-enabled systems that have voice-controlled instructions along with a video from the previous assembly operation.  

AR is also allowing manufacturing technicians to have access to instant intelligence and problem insights related to maintenance, thereby improving their efficiency and reducing equipment downtime.

4. IoT-enabled predictive maintenance is becoming a part of the overall maintenance workflow

With the advent of Industry 4.0, several enterprises are investing in IoT-enabled predictive maintenance of their assets to fix automated systems before they get disabled. In today’s competitive business environment, it is extremely important for firms to keep machines running seamlessly. Connected sensors and machine learning are helping companies anticipate component failures in advance, thereby reducing equipment downtime and time to locking up machines for preventative maintenance checks.

As a result, many organizations are running predictive analytics and machine learning to monitor systems and gather data, allowing them to estimate when components are likely to fail.

5. 5G will drive real-time IIoT applications

5G deployments are digitizing the industrial domain and changing the way enterprises manage their business operations. Industries, namely transportation, manufacturing, healthcare, energy and utilities, agriculture, retail, media, and financial services will benefit from the low latency and high data transfer speed of 5G mobile networks.

For instance, in the manufacturing domain, 5G will power factory automation, ensuring that the processes happen within the time frame, thereby reducing the risk of downtime. Further, 5G will help manufacturers in real-time production inspection and assembly line maintenance.

6. Firms are shifting from centralized cloud to edge computing

Until now, the centralized cloud was a popular choice among firms for controlling connected devices and data. However, with IoT devices and sensors expected to generate an ocean of data, more and more enterprises want IoT to monitor and report data and events remotely.

Though most firms are using centralized cloud-based solutions to collect data, they are facing issues, such as high network load, poor response time, and security risks. Edge computing is helping businesses collect, analyze, and store data close to its source, thereby reducing the costs and security risks and improving system efficiency. That explains the growing demand for edge computing.

A research report from Business Insider Intelligence forecasts that by 2020, there will be over 5,635 million smart sensors and other IoT devices globally, generating over 507.5 zettabytes of data. The need to collect and process this data at local collection points is what’s triggering the shift from centralized cloud to edge computing.

7. Firms will continue to invest in cybersecurity

Cybersecurity threats continue to evolve each day. Connected systems pose a serious threat to data and cause massive system disruption and loss to the firm. A 2018 Data Breach study by IBM revealed that the cost of an average data breach to companies globally is USD 3.86 million.

As a result, an increasing number of firms are investing in innovative services like virtual private network (VPN) to access the internet safely. Such innovative security solutions are becoming increasingly popular with enterprises across domains.

8. IoT analytics is gaining significance

While sectors such as manufacturing, aerospace, and energy and utilities are deploying IoT-powered sensors and wireless technologies, the true value of industrial IoT lies in analytics. The connected systems generate a large amount of data that needs to be effectively employed to optimize operations. Thus, the demand for  IoT analytics will rise in the coming years. As a result, firms will have to depend on AI and ML technologies to find and effective ways to manage the data overload.

Companies like SAS, SAP, and Teradata are already offering advanced analytics software to help enterprises evaluate real-time data streaming from connected systems on the shop floor.

Going forward

IIoT is all set to fuel the fourth industrial revolution. Firms across various industries are adopting innovative IoT devices and technologies to accelerate business growth. These IIoT deployments will help enterprises improve operational efficiency, reduce downtime, and get a serious competitive advantage in their respective domains.

The IIoT developments shared in this post will set the stage for innovative enterprise platforms and tech advancements. Organizations wanting to remain competitive should be not only aware of these trends but also take adequate measures to embrace them.

Source: Datafloq

Imagine being able to communicate effortlessly with the devices around you. This means having your devices fully automated and connected by sharing data through the use of sensors. This will definitely improve the quality of life and make our day to day activities much easier. This will also make businesses more efficient and facilitate in driving new business models.

Well, there is no need to imagine as this is already a reality. These are the wonders of the innovation brought about by the Internet of Things (IoT), which simply refers to the network of devices, such as vehicles and home appliances, that contain electronics, software, sensors, actuators and connectivity that allows them to connect, interact and exchange data. The emergence of IoT brings about numerous benefits, but also poses a huge threat to security as it creates new opportunities for all the information it gathers to be compromised.

Cybersecurity is already at the top of the agenda for many industries, but the scale and scope of IoT deployments escalate security, making it harder than ever to protect businesses and consumers from cyber attacks. intelligent organizations already need to protect their data and information, but cybersecurity is growing more important than ever with the emergence of IoT devices. Although IoT developments have made life easier on so many levels, it has also brought about serious security implications, as the scale of connected devices greatly increases the overall complexity of cybersecurity, while the scope of the IoT which isn’t operating as an independent device but an ecosystem magnifies these challenges — any data breach can cause significant damage to a whole business database.

As HP found out, 70% of the Internet of Things devices are vulnerable to external attacks. With the technical vulnerability of most of these devices, it can only escalate these threats. Also, with its constant evolution and little attention to security, the potential for damaging cyber attacks can only tend to increase in the future. The implementation of IoT networks opens up the grid to malicious cyber attacks and any form of compromise in the network could lead to great data leakage.

8 IoT threats to cybersecurity in the future

8 IoT threats to cybersecurity in the future

1. Complexity

Variation of devices connected to a network is accompanied by risks worsening cybersecurity worries with its diverse and wide ecosystem.

2. Volume of Data

With IoT’s great need of data to work, it opens up nearly every part of our lives to the Internet, posing an important threat to the possibility of data manipulation. As a result, we must consider what this kind of access to the Internet means for your digital and personal security, as the availability of numerous access points leads directly to an increase in the risk of a breach or hack.Unified attacks can bring down a system or a network of data that is relied upon by millions. IoT is an incredible idea with the potential to change our lives dramatically but brings with it a flurry of concerns that will stretch your abilities and require you to be on your toes at all times.

3. Continuous Expansion

The IoT evolution doesn’t seem like slowing down anytime soon and, in fact, it continues to evolve and expand rapidly. This makes it difficult for cybersecurity to keep up with the pace.

4. Over-Dependence On the Cloud

With the cloud infrastructure, IoT has a heavy reliance on the cloud for safety, which makes cyber attacks to be targeted to the cloud. With this knowledge, it’s important to look for more ways to reduce those threats. More monitoring will be highly needed for cloud configuration, as well as logging. This monitoring can also be done with the use of external tools — These includes antivurus softwares and VPNs needed to be reviewed and compared carefully. These reviews and comparisons will enable you to choose the tool best suited for your device and needs, while the use of these tools will go a long way in securing your internet connections.

5. Privacy Issues

The issue of privacy is generated by the collection of personal data in addition to the lack of proper protection of the data.

6. Deficiency In Authentication

This area deals with ineffective mechanisms being in place to authenticate to the IoT user interface and/or poor authorization mechanisms whereby a user can gain a higher level of access than allowed with regard to their weak authentication mechanisms. For example, there is usually a large amount of data that is not sufficiently encrypted and these data are transmitted via wireless networks, many of which are public and lacking in security.

7. Insecurity

Over the past two years,AT&T’s Security Operations Center has logged a 458% increase in vulnerability scans of IoT devices. The risk with this is that the IoT device could be easier to attack, allowing unauthorized access to the device or its data. Most IoT manufacturers concentrate more on the efficiency of the device and less on the security, making devices vulnerable to cyberattacks. It is also difficutl to secure these devices after they become an end product, which only increases the challenges of cybersecurity.

8. Industrial IoT

According to Forcepoint, in 2019 attackers will break into industrial IoT devices by attacking the underlying cloud infrastructures. This target is more desirable for an attacker, as access to the underlying systems of these multi-tenanted, multi-customer environments represents a much bigger payday.<

What does the future hold?

Due to the aforementioned IoT-related weaknesses, which give cybercriminals more access to manipulate connected devices, it’s clear that IoT is painting a scary future for cybersecurity. However, it’s noteworthy that no system can ever be perfect. A continuous effort has to be put into work in order to provide more effective cybersecurity measures to ensure more safety in our day-to-day use of the IoT devices around us.

Author: Joseph Chuckwube

Source: SAP

The computer industry moves in waves. We're at the tail end of one of those waves — the mobile revolution. What's next? Robots.

But not the way you think.

The robot revolution won't be characterized by white plastic desk lamps following you around asking questions in a creepy little-girl voice, like I saw at last week's Consumer Electronics Show in Las Vegas. That might be a part of it, but a small part. Rather, it'll be characterized by dozens of devices working on your behalf, invisibly, all the time, to make your life more convenient.

Some people in the industry use the term "artificial intelligence" or "digital assistants." Others talk about "smart" devices. But none of these terms capture how widespread and groundbreaking this revolution will be. This isn't just about a coffee maker that knows to turn itself on when your alarm goes off, or a thermostat that adjusts to your presence.

(And "Internet of Things" — please stop already.)

This is about every piece of technology in your life working together to serve you. Robots everywhere, all the time. Not like the Roomba. More like the movie "Her."

Where've we been?

Every 10 or 15 years, a convergence of favorable economics and technical advances kicks off a revolution in computing. Mainstream culture changes dramatically. New habits are formed. Multibillion-dollar companies are created. Companies and entire industries are disrupted and die. I've lived through three of these revolutions.

• The PC revolution. This kicked off in the 1980s with the early Apple computers and the quick-following IBM PC, followed by the PC clones. Microsoft and Intel were the biggest winners. IBM was most prominent among the big losers, but there were many others — basically, any company that thought computing would remain exclusively in the hands of a few huge computers stored in a data center somewhere. By the end, Microsoft's audacious dream of "a computer on every desk and in every home" was real.
• 
  The internet revolution. This kicked off in the mid 1990s with the standardization of various internet protocols, followed by the browser war and the dot-com boom and bust. Amazon and Google were the biggest winners. Industries that relied on physical media and a distribution monopoly, like recorded music and print media, were the biggest losers. By the end, everybody was online and the idea of a business not having a website was absurd.
• 
  The mobile revolution. This kicked off in 2007 with the launch of the iPhone. Apple and Samsung were the biggest winners. Microsoft was among the big losers, as its 20-year monopoly on personal computing finally broke.

A couple of important points:

First, when a revolution ends, that doesn't mean the revolutionary technology goes away. Everybody still has a PC. Everybody still uses the internet. It simply means that the technology is so common and widespread that it's no longer revolutionary. It's taken for granted.

So: The mobile revolution is over.

More than a billion smartphones ship every year. Apple will probably sell fewer iPhones this year than last year for the first time since the product came out. Huge new businesses have already been built on the idea that everybody will have an internet-connected computer in their pocket at all times — Uber wouldn't make sense without a smartphone, and Facebook could easily have become a historical curiosity like MySpace if it hadn't jumped into mobile so adeptly. This doesn't mean that smartphones are going away, or that Apple is doomed, or any of that nonsense. But the smartphone is normal now. Even boring. It's not revolutionary.

The second thing to note is that each revolution decentralized power and distributed it to the individual.

The PC brought computing power out of the bowels of the company and onto each desk and into each home. The internet took reams of information that had been locked up in libraries, private databases, and proprietary formats (like compact discs) and made it available to anybody with a computer and a phone line.

The smartphone took those two things and put them in our pockets and purses.

Tomorrow and how we get there

This year's CES seemed like an "in-betweener." Everybody was looking for the next big thing. Nothing really exciting dominated the show.

There were smart cars, smart homes, drones, virtual reality, wearable devices to track athletic performance, smart beds, smart luggage (really), and, yeah, weird little robots with anime faces and little-girl voices.

But if you look at all these things in common, plus what the big tech companies are investing in right now, a picture starts to emerge.

• Sensors and other components are dirt cheap. Thanks to the mobile revolution creating massive scale for the components that go into phones and tablets, sensors of every imaginable kind — GPS, motion trackers, cameras, microphones — are unimaginably cheap. So are the parts for sending bits of information over various wireless connections — Bluetooth LTE, Wi-Fi, LTE, whatever. These components will continue to get cheaper. This paves the way for previously inanimate objects to collect every kind of imaginable data and send simple signals to one another.
• 
  Every big tech company is obsessed with AI. Every single one of the big tech companies is working on virtual assistants and other artificial intelligence. Microsoft has Cortana and a bunch of interesting behind-the-scenes projects for businesses. Google has Google Now, Apple has Siri, Amazon has Echo, even Facebook is getting into the game with its Facebook M digital assistant. IBM and other big enterprise companies are also making huge investments here, as are dozens of venture-backed startups.
• 
  Society is ready. This is the most important point. Think about how busy we are compared with ten or twenty years ago. People work longer hours, or stitch together multiple part-time jobs to make a living. Parenting has become an insane procession of activities and playdates. The "on-demand" economy has gone from being a silly thing only business blogs write about to a mainstream part of life in big cities, and increasingly across the country — calling an Uber isn't just for Manhattan or San Francisco any more. This is the classic situation ahead of a computing revolution — everybody needs something, but they don't know they need it yet.

So imagine this. In 10 years, you pay a couple-hundred bucks for a smart personal assistant, which you install on your phone as an app. It collects a bunch of information about your actions, activities, contacts, and more, and starts learning what you want. Then it communicates with dozens of other devices and services to make your life more convenient.

Computing moves out of your pocket and into the entire environment that surrounds you.

Your alarm is set automatically. You don't need to make a to-do list — it's already made. Mundane phone calls like the cable guy and the drugstore are done automatically for you. You don't summon an Uber — a car shows up exactly when you need it, and the driver already knows the chain of stops to make. (Eventually, there won't be a driver at all.)

If you're hungry and in a hurry, you don't call for food — your assistant asks what you feel like for dinner or figures out you're meeting somebody and orders delivery or makes restaurant reservations. The music you like follows you not just from room to room, but from building to building. Your personal drone hovers over your shoulder, recording audio and video from any interaction you need it to (unless antidrone technology is jamming it).

At first, only the wealthy and connected have this more automated lifestyle. "Have your assistant call my assistant." But over time, it trickles down to more people, and soon you can't remember what life was like without one. Did we really have to make lists to remember to do all this stuff ourselves?

This sounds like science fiction, and there's still a ton of work ahead to get there. Nobody's invented the common way for all these devices to speak to each other, much less the AI that can control them and stitch them together. So this revolution is still years away. But not that far.

If you try to draw a comparison with the mobile revolution, we're still a few years from the iPhone. We're not even in the BlackBerry days yet. We're in the Palm Pilot and flip-phone days. The basic necessary technology is there, but nobody's stitched it together yet.

But when they do — once again — trillion-dollar companies and industries will rise and fall, habits will change, and everybody will be blown away for a few years. Then, we'll all take it for granted.

Source: Business Insider

Innovatieorganisatie TNO ziet kansen voor big data en Internet of Things-technologie (IoT) om de bereikbaarheid van de metropoolregio Amsterdam te vergroten. “Met big data kunnen we meerdere oplossingen aan elkaar koppelen om de infrastructuur van een stad optimaal te benutten”, zegt Leo Kusters, Managing Director Urbanisation bij TNO.

Binnen enkele decennia woont 70 procent van de wereldbevolking in grote steden of in sterk verstedelijkte regio’s. Het economische en culturele succes van regio’s als de Randstad trekt veel mensen. De infrastructuur van deze steden wordt daardoor steeds meer belast. Infrastructuur en mobiliteit zijn daarom bepalende factoren voor het succes van de grootstedelijke regio’s.

Slimme mobiliteit

Kusters wijst op het project Praktijkproef Amsterdam (PPA), waarin TNO samenwerkt met ARS Traffic & Transport Technology aan het verminderen van files in de regio Amsterdam. “Aan dit project zijn 15.000 automobilisten verbonden”, zegt Kusters. Door weggebruikers beter te informeren over de verkeerssituatie in de stad, verwacht TNO dat het aantal files in de regio Amsterdam afneemt.

De deelnemers hebben de beschikking over een app waarmee ze op individueel niveau geïnformeerd worden over de beste reiskeuzes die ze kunnen maken. Daarnaast kunnen gebruikers via de app ook zelf incidenten en vertragingen op de weg melden. Hierdoor komen de automobilisten sneller op hun bestemming en kunnen ze rekenen op een betrouwbare reistijd.

Bijzonder aan dit project is volgens Kusters dat de app ook advies geeft op basis van verkeerslichten die op rood staan. Vervolgens houdt het systeem rekening met deze verkeerslichten om een opstopping op de weg te voorkomen.

TNO voert een vergelijkbaar project uit met vrachtverkeer in Helmond. Kusters: “Door de stad Helmond loopt een snelweg waar veel vrachtauto’s overheen rijden. Hierdoor is er in de stad veel belasting voor het milieu en de luchtkwaliteit.” In dit project experimenteert TNO met data-analyse om de doorstroming voor de betrokken vrachtwagens te optimaliseren. De chauffeurs krijgen doorlopend snelheidsadviezen om de doorstroming in de stad te verbeteren. Hierdoor hoeven chauffeurs minder te stoppen in de stad. Vrachtwagens verbruiken daardoor minder brandstof.

Twee vliegen in één klap

_{Een grote kans van big data en de toepassing van IoT-technologie ligt volgens Kusters in het combineren van meerdere oplossingen voor optimale benutting van bestaande infrastructuur. Big data kan ook bijdragen aan besparingen in het onderhoud van de infrastructuur, waar Nederland jaarlijks € 6 mrd aan uitgeeft.}

TNO richt zich bijvoorbeeld op het verlengen van de levensduur van bruggen. ”Een essentieel onderdeel van de infrastructuur”, zegt Kusters. “Als bruggen niet werken, staat alles stil.” TNO meet met sensoren de haarscheurtjes in bruggen. “Zo kunnen we precies weten wanneer een brug onderhoud nodig heeft of moet worden vervangen. Dit maakt het mogelijk om de levensduur van de brug ‘op maat’ te verlengen. Dus precies op tijd met een minimum aan overlast voor het verkeer.”

De levensduur van infrastructuuronderdelen wordt meestal bepaald op basis van theoretische modellen. Kusters: “Omdat de werkelijkheid altijd anders is, ontwikkelt TNO met Rijkswaterstaat nieuwe meetmethodes. Het gebruik van infrastructuur kan in de praktijk intensiever of juist minder intensief zijn in vergelijking met de inschatting uit theoretische modellen, en de schade dus ook. Door big data in te zetten, kunnen we nauwkeurige voorspellingen maken voor het onderhoud van de brug en daarmee kosten besparen.”

De coöperatieve auto

Bij deze projecten is de betrokkenheid van verschillende partijen van groot belang, meent Kusters. “Mobiliteit is allang niet meer het alleenrecht van de overheid. De overheid neemt een andere rol aan bij de verduurzaming van infrastructuur en mobiliteit. Ook technologiebedrijven worden steeds belangrijker. Dat zijn bedrijven als TomTom en Google, maar ook een partij als chipleverancier NXP, die kunnen bijdragen aan de ontwikkeling van technologie om voertuigen met elkaar te laten communiceren.”

De TNO-directeur spreekt over de ‘coöperatieve auto’. “Dat betekent dat alle diensten en modaliteiten waar je als automobilist gebruik van wil maken, aan elkaar worden gekoppeld. Het systeem gaat dan als het ware met je mee denken.”

De coöperatieve auto maakt gebruik van IoT-technologie om rechtstreeks met andere voertuigen of de infrastructuur te communiceren. Hierdoor houdt de auto continu rekening met de huidige verkeerssituatie en de voertuigen die in dezelfde omgeving rijden. Kusters: “Dat is een grote doorbraak, een efficiënte deels-zelfrijdende auto die altijd oplet en altijd wakker is. Zo kunnen we de wegcapaciteit stevig laten toenemen en een flink deel van de fileproblemen oplossen.”

Toekomstvisie

De Managing Director Urbanisation ziet de IoT-toepassingen voor mobiliteit in rap tempo toenemen. "De autonoom zelfrijdende auto in de stad is misschien wel minder ver weg dan we denken”, zegt Kusters. “We hebben al auto’s die zelf kunnen parkeren. In de toekomst betekent dit dat de parkeerproblemen in de grote steden ten einde lopen.”

Naast de IoT-toepassing voor coöperatieve auto’s, ziet Kusters ook kansen voor verbeteringen aan de infrastructuur. “Het verbonden zijn van mensen en van apparaten zal ook terug te zien zijn op het straatbeeld, zoals wifi op straat, wifi voor auto’s, en slimme LED-verlichting. Dat betekent overigens niet dat al die informatie over één en hetzelfde netwerk zal gaan. De informatie die tijdkritisch is en de verkeersveiligheid beïnvloedt, zal bijvoorbeeld gebruikmaken van een apart netwerk. Dit gaan we in steden en op snelwegen binnen een paar jaar in de praktijk zien.”

In de toekomst ziet de directeur leefomgeving van TNO ook meer veranderingen in het aanzicht van de binnenstad. “In de stad gaan we meer en meer elektrisch rijden. Dat zien we al in recente openbaar vervoersaanbestedingen.” Ook fietsersaantallen zullen volgens Kusters nog verder groeien. “In een stad als Amsterdam is er dan meer ruimte nodig voor de fiets”, zegt Kusters. “Dit is de enige vorm van mobiliteit die in Amsterdam toeneemt. Meer ruimte voor fietsers is daarom belangrijk. Dat gaat wel ten koste van de parkeerplaatsen van de auto’s, maar hoeft dan niet zomaar ten koste te gaan van de bereikbaarheid.”

Source: DuurzaamBedrijfsleven

TU Delft lanceert nieuw IoT fieldlab

Het nieuwe fieldlab Do IoT (Delft on Internet of Things) is eerder deze week officieel van start gegaan tijdens een bijeenkomst op de TU Delft Campus. Het Do IoT Fieldlab richt zich op het ontwikkelen van toepassingen van het Internet of Things, waarbij 5G een belangrijke rol speelt.

Tijdens de bijeenkomst op de TU Delft Campus verrichtte wethouder Saskia Bruines van de gemeente Den Haag namens de MRDH (Metropoolregio Rotterdam Den Haag) de openingshandeling: een demonstratie van het tactiele internet. De MRDH investeert 200.000 euro in het Do IoT Fieldlab.

Bij het Internet of Things (IoT) wisselen grote aantallen apparaten informatie uit via een draadloos netwerk. Dat vraagt om zeer snelle verbindingen met een hoge betrouwbaarheid en korte reactietijd. 5G, de nieuwe generatie mobiele communicatienetwerken, biedt deze mogelijkheid.

Binnen de TU Delft werken meerdere faculteiten, EWI, Techniek, Bestuur en Management (TBM) en Industrieel Ontwerpen (IO), samen om zo niet alleen de technische uitdagingen op te lossen, maar ook kwesties rondom governance, data management, design en sociale aspecten aan te pakken.

Ook voor het zogeheten tactiele internet, waarbij we via het internet ook tastzin willen kunnen overbrengen, is 5G een voorwaarde. Daarbij kan bijvoorbeeld een chirurg op afstand een operatie uitvoeren met een robotarm, legde Fernando Kuipers, associate professor van de faculteit EWI en één van de trekkers van het Do IoT fieldlab, uit tijdens de drukbezochte bijeenkomst.

Het nieuwe fieldlab biedt de mogelijkheden om al deze toepassingen uit te proberen, zodat snel duidelijk wordt welke innovaties werken en welke niet.

Het Do IoT Fieldlab is opgericht door de provincie Zuid-Holland, TNO, gemeente Delft, MRDH), gemeente Katwijk, Holland Rijnland en de TU Delft.

Bron: Emerce

What are smart cities and what to expect from them?

At B2B International, the latest innovations and emerging ‘megatrends’ shaping industries and markets are an important topic.

So, for every month in 2019 they decided to delve a little deeper into each of these trends and produce a mini-guide looking at what it is, how big it will be and the opportunity it presents for b2b companies.

This month’s topic is smart cities. Enjoy!

What is it?

Smart cities are urban areas that use information and communication technologies to increase operational efficiency, optimise the use of limited resources, share information with the public and improve the quality of public services and citizen welfare.

How big will it be?

By the year 2050, over half the world’s population will be living in cities (Source: IEC). This rapid urbanisation creates the need for smarter living as it becomes more difficult to supply these growing populations with resources such as safe food, clean water and sufficient energy.

Urbanisation is prominent in Asia where 17 of the world’s 31 megacities are located, but equally as critical in Europe, where municipalities are striving towards meeting the climate goals.

What’s the opportunity?

Examples of the many current and likely future disruptive innovations are:

Internet of things (IoT): This technology is key to the development of smart cities, as it enables a more connected and efficient infrastructure, such as smart energy meters in residential and commercial buildings, smart streetlights which automatically brighten with people in the area, parking sensors that indicate the nearest available parking spots, etc.

The IoT also has the potential to help with traffic management. Cities are well-known for traffic congestion and as populations grow, the problem will undoubtedly escalate. Smart city devices, connected by technologies such as the IoT and 5G networks, will be able to monitor traffic flow levels and respond accordingly by adjusting traffic light sequences and lane prioritisation.

5G wireless technologies: 5G advancements, which are already underway in various locations around the world, are expected to accelerate smart city benefits due to the powerful combination of improved connectivity speed, responsiveness and reach in locations previously unprecedented.

Robotics: Automation is a growing trend behind smart city implementation and improvements. Combined with IoT, robots are being explored and trialled for automating the likes of city waste management, as well as addressing infrastructure problems such as potholes, leaking water pipes, faulty street lights, etc.

Conclusion

The need for smart cities will become much greater in the coming years as a way of addressing the challenges that come with growing urbanisation.

Larger populations bring specific issues that smart cities will need to overcome, such as traffic management, waste management and healthcare management.

All this will put greater strain on energy use, and therefore smart cities will also need to make full use of smart energy grids which automatically monitor and optimise city-wide energy consumption to ensure a steady and efficient distribution of power.

The development of smart cities will also greatly increase demand for new technologies across a wide range of industries, presenting a massive opportunity for b2b technology and manufacturing brands to capitalise and win more business.

Source: B2B International

Why your business should consider outsourcing its Internet of Things R&D

There's a lot to know about the benefits of outsourcing your IoT (Internet of Things) R&D (research and development), especially as internet of things technology becomes more complex.

The Internet of Things is changing almost every industry. Gartner reports that the number of IoT endpoints will increase to 5.8 billion by the end of next year. This will be a 22% increase from 2019.

The growing demand for new IoT solutions is putting a lot of pressure on companies in various industries. They are going to need to adapt new R&D options to bring IoT technology to the market.

Some of the biggest challenge that IoT developers face include increasing adaptability and promoting flexibility. Many companies lack the talent to handle these tasks in-house. This may require a greater number of companies to outsource their IoT development services.

IoT outsourcing becomes necessary in 2019

The IoT market is becoming incredibly complex, which is driving a growing number of companies to outsource their development.

It is very challenging to get all of the variables and logistics for Internet of Things hardware devices precisely right and ready for market. The process is complex. Without the right expertise, those who go it alone tend to find that they make bad decisions that lead to new risks to their business.

The risks are especially high in some fields where the logistics are even more complicated. The automotive IoT market is one example. The demand for automotive IoT solutions has risen dramatically. Over 220 million cars will be connected to the IoT next year and 90% will be on the IoT by 2040. This has made it more important than ever to make sure that cars are properly equipped for the IoT.

Companies can minimize complications by partnering with an outsourcing collaborator with the right expertise in R&D. They can find a number of benefits, such as:

• This enables them to hire up to five times the number of staff for design and engineering at a lower cost.
• The expertise will lead to many new creative ideas beyond what was possible without their knowledge making for increased business opportunities.
• Throughout the lifespan of the product, they will have much greater access to resources with greater flexibility.
• They will have greater control over project expenses and the results.

When you think of outsourcing your IoT for R&D, it seems this would be a costly venture to your business, but it would actually save you money and generate more revenue adding to your bottom line.

Advantages of outsourcing

When you choose to outsource your IoT R&D with a partner, the designers and engineers bring expertise to the table allowing creative ideas beyond the scope of what was available to you without their knowledge. With this comes many benefits. Let’s break them down:

• You can hire up to five times the number of staff members for design and engineering at a lower cost.  How is this possible? When you outsource your R&D to a team of experts, it doesn’t take them long to get on board with what’s happening and they are knowledgeable in a multitude of varying disciplines. In order to have this type of skill and talent in-house, you would need to pay a several-hundred-thousand-dollar salary yearly with outsource staffing agencies resulting in generally the same fees.
• You can anticipate a ‘to-market’ scenario much faster than you would have been able to by going it alone.  When you work with a team of multi-disciplined experts, the product development moves quickly, particularly if they are going to go along with a predictable process. Within a few weeks, it should be possible to move from the idea stage to where there is a concept for a high-level product manufacturer.
• The expertise will generate creative ideas beyond what was possible without their knowledge making for increased business opportunities. There is nothing better than receiving the constructive feedback on your product from a team of experts in the industry who bring extensive knowledge on IoT development. This gives you a massive competitive advantage. There are opportunities for new features and applications for the product, new IP, and expense-saving, all discovered by way of this organic program.

Choosing to outsource your IoT R&D with experts allows you to have better control over costs for the project as well as the outcomes for your business. With this type of assistance and know-how, the prototype ultimately developed will be of the caliber to bring investments and many more customers all while keeping the costs at a manageable level for the business, something that would have been difficult to do solely in-house.

Outsourcing is the key to a thriving IoT market

The IoT market is growing rapidly. A number of new challenges are on the horizon. Companies will need to start outsourcing their IoT R&D processes to make the most of it.

Author: Annie Qureshi

Source: SmartDataCollective