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, 

De impact van 5G op de ontwikkelingen in moderne technologie

Van opvouwbare 5G-telefoons tot operaties die worden uitgevoerd op een patiënt op kilometers afstand, en van vroege supersnelle netwerkuitrol in de VS tot tactiele internet en Internet of Things, en natuurlijk robots... het lijkt wel alsof 5G altijd en overal onderwerp van gesprek is.

Voor telecom operators of communicatie-providers (CSP's) is dit een tijd vol kansen, van het upgraden van capaciteit tot het leveren van nieuwe services, content en interactie op manieren die voorheen simpelweg niet mogelijk waren. Door 5G te gebruiken om Internet of Things (IoT) en edge computing mogelijk te maken, hebben ze nu een geweldige kans die een paar jaar geleden nog volledig ondenkbaar was.

Maar dat betekent dat 5G zich op een enorm buigpunt bevindt. Van het kunnen verslaan van concurrenten met nieuwe diensten die echt waarde toevoegen, tot de prestaties en operationele efficiëntie die hun netwerken aanzienlijk verbeteren. De beslissingen die vandaag worden genomen, zullen verregaande financiële en operationele implicaties hebben voor CSP's.

Bereik bedrijfsdoelen met 5G

Maar als je 5G slechts ziet als een volgende mogelijkheid voor telecom, dan mis je de boot. Iedere organisatie in iedere sector die netwerken gebruikt (kortom iedereen) moet plannen maken voor de 5G implementatie en nadenken over hoe ze deze kunnen gebruiken om hun bedrijfsdoelen te bereiken.

Het maakt niet uit of ze actief zijn in de detailhandel, logistiek, in een stad, in een landelijke omgeving, in de publieke of private sector. 5G belooft enorme kansen om nieuwe diensten en toepassingen te leveren, de automatisering en de mogelijkheden die dit met zich meebrengt te vergroten en om bedrijven te helpen om met klanten om te gaan op manieren die nog nooit eerder waren bedacht.

Dat wil niet zeggen dat het altijd even gemakkelijk is, zoals Åsa Tamsons, hoofd new businesses bij Ericsson, al zei in een interview met CN. Want velen beschouwen 5G nog steeds als gewoon 'een ander netwerk'. Het is een houding die het aanvankelijk voor sommige organisaties moeilijker zou kunnen maken om de vooraf benodigde investeringen rond te krijgen. Ondernemingen zullen moeten werken om zowel interne als externe belanghebbenden ervan te overtuigen dat de kosten gerechtvaardigd zijn.

Eén netwerk, een wereld van kansen

Terug naar de initiële vraag: is 5G niet gewoon een andere G? Simpel gezegd, 5G levert veel hogere snelheden en biedt veel kortere latency en een aanzienlijk hogere dichtheid dan 4G. Maar wat betekent dit eigenlijk?

Snelheid is relatief eenvoudig. Het 5G-voorbeeld dat hierin vaak wordt gebruikt, is dat het straks mogelijk is om in tien seconden een HD-film te downloaden in vergelijking met de (op zijn best) ongeveer twintig minuten die er momenteel voor nodig zijn (afhankelijk van de lokale breedbanddiensten).

De latency, de tijd die nodig is om gegevens tussen twee punten te laten reizen, is bij 5G minder dan een milliseconde, wat bijvoorbeeld belangrijk kan zijn voor chirurgie, maar gecombineerd met snelheid is het ook een factor voor veel gamers die willen betalen voor dit type snelle, low latency-service.

Op dit moment bevinden we ons al in een wereld waar meer dan 23 miljard apparaten zijn aangesloten op netwerken en die dichtheid blijft, dankzij grotere mobiliteit en IoT-use-cases, groeien. We zijn allemaal al eens in de situatie geweest waarin de snelheden drastisch afnemen als iedereen inlogt en naarmate we steeds meer verbonden raken, hebben we netwerken nodig die geschikt zijn voor aanzienlijk meer apparaten dan ooit tevoren.

Telco-cloud

Echter, om dit alles te kunnen leveren, is een aanzienlijke investering in de netwerkinfrastructuur nodig. Voor CSP's is dit een grote onderneming. Daarom is het waarschijnlijk zo dat in plaats van een puur 5G-netwerk, de meerderheid van de mensen een gemengde aanpak zullen zien, waarbij 4G beschikbaar is om basisdiensten te leveren en 5G wordt geïntroduceerd voor specifieke taken. Het is daarom van cruciaal belang om de zogenaamde telco-cloud te hebben. Dit is een software defined technologie die zowel de huidige 4G ondersteunt als het grondwerk voor 5G is, iets wat erg wordt gewaardeerd door operators zoals bijvoorbeeld Vodafone.

'Het vermogen om flexibel en agile te zijn terwijl we onze netwerkactiviteiten en -beheer blijven automatiseren, kon alleen worden bereikt door een software defined infrastructuur', zegt Johan Wibergh, chief technology officer van Vodafone. 'We zijn blij met de versnelde time-to-market en de bijbehorende economische voordelen van onze transitie naar NFV en, in toenemende mate, een telco-cloud infrastructuur'.

Met 5G hebben bedrijven toegang tot de niveaus en snelheden van connectiviteit die ze nodig hebben om te profiteren van de game changing technologieën zoals IoT, edge computing en AI (artificial intelligence) die de volgende fase van de digitale revolutie gaan vormgeven. Gecombineerd met deze software defined-infrastructuur, en meer in overeenstemming met de specificaties en ambities, heeft 5G de kracht om bedrijfsmodellen van gevestigde organisaties ongekend te transformeren.

Kapitaliseren om te gedijen

We beseffen ons nog niet eens wat de mogelijkheden van 5G nu al zijn. Er moet nog zoveel gebeuren voordat we volledige acceptatie zien, maar bedrijven moeten nu gaan nadenken over hoe ze de kracht van deze nieuwe netwerken kunnen benutten voor hun eigen concurrentievermogen. Er over denken als 'gewoon een andere G' dreigt er voor te zorgen dat men niet voorbereid is en de enorme kansen mist die beschikbaar zijn.

5G is het netwerk en de basis die de beloftes van veel andere nieuwe technologieën waar gaat maken. Elke organisatie die er niet in slaagt om hiervan te profiteren, zal heel hard moeten werken om te overleven in de digitale wereld.

Auteur: Jean Pierre Brulard

Bron: CIO

Edge computing in a Nutshell

Edge computing (EC) allows data generated by the Internet of Things (IoT) to be processed near its source, rather than sending the data great distances, to data centers or a cloud. More specifically, edge computing uses a network of micro-data stations to process or store the data locally, within a range of 100 square feet. Prior to edge computing, it was assumed all data would be sent to the cloud using a large and stable pipeline between the edge/IoT device and the cloud.

Typically, IoT devices transfer data, sometimes massive amounts, sending it all to a data center, or cloud, for processing. With edge computing, processing starts near the source. Once the initial processing has occurred, only the data needing further analysis is sent. EC screens the data locally, reducing the volume of data traffic sent to the central repository.

This tactic allows organizations to process data in “almost” real time. It also reduces the network’s data stream volume and eliminates the potential for bottlenecks. Additionally, nearby edge devices can “potentially” record the same information, providing backup data for the system.

A variety of factors are promoting the expansion of edge computing. The cost of sensors has been decreasing, while simultaneously, the pace of business continues to increase, with real-time responses providing a competitive advantage to its users. Businesses using edge computing can analyze and store portions of data quickly and inexpensively. Some are theorizing edge computing means an end to the cloud. Others believe it will complement and support cloud computing.

The Uses of Edge Computing

Edge computing can be used to help resolve a variety of situations. When IoT devices have a poor connectivity, or when the connection is intermittent, edge computing provides a convenient solution because it doesn’t need a connection to process the data, or make a decision.

It also has the effect of reducing time loss, because the data doesn’t have to travel across a network to reach a data center or cloud. In situations where a loss of milliseconds is unacceptable, such as in manufacturing or financial services, edge computing can be quite useful.

Smart cities, smart buildings, and building management systems are ideal for the use of edge computing. Sensors can make decisions on the spot, without waiting for a decision from another location. Edge computing can be used for energy and power management, controlling lighting, HVAC, and energy efficiency.

A few years ago, PointGrab announced an investment in CogniPointTM, and its Edge Analytics sensor solution for smart buildings, by Philips Lighting and  Mitsubishi UFJ Capital. PointGrab is a company which provides smart sensor solutions to automated buildings.

The company uses a deep learning technology in developing its sensors, which detects the occupant’s locations, maintains a head count, monitors their movements, and adjusts its internal environment using real-time analytics. PointGrab’s Chief Business Officer, Itamar Rothat stated:

“CogniPoint’s ultra-intelligent edge-analytics sensor technology will be a key facilitator for capturing critical data for building operations optimization, energy savings improvement, and business intelligence.”

Another example of edge computing is the telecommunication companies’ expansion of 5G cellular networks. Kelly Quinn, an IDC research manager, predicts telecom providers will add micro-data stations that are integrated into 5G towers, or located near the towers. Business customers can own or rent the micro-data stations for edge computing. (If rented, negotiate direct access to the provider’s broader network, which can then connect to an in-house data center, or cloud.)

Edge Computing vs. Fog Computing

Edge computing and fog computing both deal with processing and screening data prior to its arrival at a data center or cloud. Technically, edge computing is a subdivision of fog computing. The primary difference is where the processing takes place.

With fog computing, the processing typically happens near the local area network (but technically, can happen anywhere between the edge and a data center/cloud), using a fog node or an IoT gateway to screen and process data. Edge computing processes data within the same device, or a nearby one, and uses the communication capabilities of edge gateways or appliances to send the data. (A gateway is a device/node that opens and closes to send and receive data. A gateway node can be part of a network’s “edge.”)

Edge Computing Security

There are two arguments regarding the security of edge computing. Some suggest security is better with edge computing because the data stays closer to its source and does not move through a network. They argue the less data stored in a corporate data center, or cloud, the less data that is vulnerable to hackers.

Others suggest edge computing is significantly less secure because “edge devices” can be extremely vulnerable, and the more entrances to a system, the more points of attack available to a hacker. This makes security an important aspect in the design of any “edge” deployment. Access control, data encryption, and the use of virtual private network tunneling are important parts of defending an edge computing system.

The Need for Edge Computing

There is an ever-increasing number of sensors providing a base of information for the Internet of Things. It has traditionally been a source of big data. Edge computing, however, attempts to screen the incoming information, processing useful data on the spot, and sending it directly to the user. Consider the sheer volume of data being supplied to the Internet of Things by airports, cities, the oil drilling industry, and the smart phone industry. The huge amounts of data being communicated creates problems with network latency, bandwidth, and the most significant problem, speed. Many IoT applications are mission-critical, and the need for speed is crucial.

EC can lower costs and provide a smooth flow of service. Mission critical data can be analyzed, allowing a business to choose the services running at the edge, and to screen data sent to the cloud, lowering IoT costs and getting the most value from IoT data transfers. Additionally, edge computing provides “Screened” big data.

Transmitting immense amounts of data is expensive and can strain a network’s resources. Edge computing processes data from, or near, the source, and sends only relevant data through network to a data processor or cloud. For instance, a smart refrigerator doesn’t need to continuously send temperature data to a cloud for analysis. Instead, the refrigerator can be designed to send data only when the temperature changes beyond a certain range, minimizing unnecessary data. Similarly, a security camera would only send data after detecting motion.

Depending on how the system is designed, edge computing can direct manufacturing equipment (or other smart devices) to continue operating without interruption, should internet connectivity become intermittent, or drop off, completely, providing an ideal backup system.

It is an excellent solution for businesses needing to analyze data quickly in unusual circumstances, such as airplanes, ships, and some rural areas. For example, edge devices could detect equipment failures, while “not” being connected to a cloud or control system. Examples of edge computing include:

Internet of Things

• Smart streetlights
• Home appliances
• Motor vehicles (Cars and trucks)
• Traffic lights
• Thermostats
• Mobile devices

Industrial Internet of Things (IIoT)

• Smart power grid technology
• Magnetic resonance (MR) scanner
• Automated industrial machines
• Undersea blowout preventers
• Wind turbines

Edge Computing Compliments the Cloud

The majority of businesses using EC continue to use the cloud for data analysis. They use a combination of the systems, depending on the problem. In some situations, the data is processed locally, and in others, data is sent to the cloud for further analysis. The cloud can manage and configure IoT devices, and analyze the “Screened” big data provided by Edge Devices. Combining the power of edge computing and the cloud maximizes the value of Internet of Things. Businesses will have the ability to analyze screened big data, and act on it with greater speed and precision, offering an advantage against competitors.

Data Relationship Management

Device Relationship Management (DRM) is about monitoring and maintaining equipment using the Internet, and includes controlling these “sensors on the edge.” DRM is designed specifically to communicate with the software and microprocessors of IoT devices and lets organizations supervise and schedule the maintenance of its devices, ranging from printers to industrial machines to data storage systems. DRM provides preventative maintenance support by giving organizations detailed diagnostic reports, etc. If an edge device is lacking the necessary hardware or software, these can be installed. Outsourcing maintenance on edge devices can be more cost effective at this time than hiring an in-house maintenance staff, particularly if the maintenance company can access the system by way of the internet.

Author: Keith D. Foote

Source: Dataversity

Eerste 5G testfrequentie Eindhoven in gebruik

VodafoneZiggo stelt zijn 3,5GHz testfrequentie voor 5G beschikbaar aan de gemeente Eindhoven voor experimenten. De gemeente, VodafoneZiggo en netwerkpartner Ericsson hebben dit vastgelegd in een intentieverklaring.

VodafoneZiggo benut 5G testfrequenties in Groningen en Utrecht, maar die in Eindhoven was tot nu toe nog niet gebruikt.

Centrale vraag van de samenwerking is: hoe kun je maatschappelijke uitdagingen in steden (zoals stijgende zorgbehoeftes of verkeersproblemen) oplossen met behulp van nieuwe technologieën?

Een concreet project in voorbereiding is Connected Ambulances, een samenwerking van VodafoneZiggo en Ericsson met Philips, GGD ambulancedienst en het Catharina Ziekenhuis in de regio. Inzet is om met supersnelle 5G-verbindingen al tijdens de rit van een ambulance ‘hulp op afstand’ te bieden bij de diagnose en voorbereiding op een behandeling.

Op de High Tech Campus Eindhoven komt een 5G-lab waar bedrijven, studenten en wetenschappers nieuwe 5G-toepassingen en prototypes kunnen testen en onderwerpen aan een praktijkproef. Hierbij zal LUMO Labs, de in Eindhoven gevestigde ‘vroege fase’ investeerder en accelerator, als partner een aanjagende rol hebben.

In het Philips Stadion van PSV zullen mogelijkheden worden onderzocht om met 5G 360-graden camera’s de verrichtingen van de voetballers te laten zien vanuit een zelfgekozen blikveld. En voor de Eindhovense poptempel Effenaar zijn plannen om een popconcert of event ook virtueel ‘live’ bij te wonen.

De ondertekening van de 5G-intentieverklaring vond plaats op de High Tech Campus in Eindhoven tijdens het werkbezoek van de Europese Commissie, de Vereniging Nederlandse Gemeenten en 70 afgevaardigden van Europese steden. Deze vertegenwoordigers zagen hoe de 5G verbintenis tussen de gemeente Eindhoven, VodafoneZiggo en Ericsson werd gesymboliseerd door een virtuele ontmoeting van de algemeen directeuren Edwin van der Sar van Ajax en Toon Gerbrands van PSV. Vanuit Amsterdam was Van der Sar als hologram in Eindhoven aanwezig om zijn opponent succes te wensen voor het topduel Ajax-PSV dat gisteren gespeeld werd. Daarbij onderstreepten zij het belang van nieuwe technologie in relatie tot de voetbalfans.

Als u meer wil weten over 5G technologie en de mogelijke kansen die het bedrijven zal bieden, lees dan dit artikel.

Bron: Emerce

IBM investeert meer dan 3 miljard dollar in IoT-campus en innovatiecentrum.

De campus zal naar verwachting duizend IBM ontwikkelaars, adviseurs, wetenschappers en ontwerpers gaan huisvesten. Met de opening gaat een investering gepaard van meer dan 3 miljard dollar: de grootste investering van IBM in Europa in 20 jaar.

'Het Internet of Things vormt op korte termijn de grootste databron ter wereld, terwijl we momenteel met 90 procent van die data helemaal niets doen', zegt Harriet Green, general manager van IBM's nieuwe Watson IoT divisie. “Door deze data te koppelen aan IBM’s Watson technologie met haar unieke vaardigheden van op het gebied van waarneming, argumentatie en zelflerend vermogen, opent dit deuren voor bedrijven, overheden en individuen om verbanden uit hun data te halen die tot nieuwe inzichten leiden.'

Naast het nieuwe hoofdkantoor lanceert IBM tevens vier nieuwe Watson API’s op de Watson IoT cloud:

•Natural language processing (NLP) API. Stelt gebruikers in staat om te communiceren met toestellen en systemen via menselijke taal. Watson linkt taal aan andere databronnen die voor de juiste context zorgen. Zo kan een onderhoudsmonteur aan Watson vragen wat een bepaalde trilling in een toestel veroorzaakt. Watson zal automatisch de bedoeling achter de vraag herkennen en linken aan databronnen over bijvoorbeeld het onderhoud van het toestel en een aanbeveling doen om de trilling weg te werken.

•Machine Learning Watson API. Automatiseert dataprocessen en monitort datasets op continue basis. Daardoor kan het systeem trends waarnemen en aanbevelingen doen wanneer een probleem zich voordoet.

•Video and Image Analytics API. Doorzoekt ongestructureerde datasets van afbeeldingen en video’s en kan hieruit trends en patronen identificeren.

•Text analytics API. Biedt de mogelijkheid om grote hoeveelheden tekst door te ploegen en patronen te herkennen. Denk bijvoorbeeld aan de transcripten van call centers, tweets en blogs.

Source: Adformatie

Data is at the very core of the business models of the future – and this means wrenching change for some organizations.

We tend to think of our information systems as a foundation layer that support the “real” business of the organization – for example, by providing the information executives need to steer the business and make the right decisions.

But information is rapidly becoming much more than that: it’s turning into an essential component of the products and services we sell.

Information-augmented products

In an age of social media transparency, products “speak for themselves”– if you have a great product, your customers will tell their friends. If you have a terrible product, they’ll tell the world. Your marketing and sales teams have less room for maneuver, because prospects can easily ask existing customers if your product lives up to the promises.

And customer expectations have risen. We all now expect to be treated as VIPs, with a “luxury” experience. When we make a purchase, we expect to be recognized. We expect our suppliers to know what we’ve bought in the past. And we expect personalized product recommendations, based on our profile, the purchases of other people like us, and the overall context of what’s happening right now.

This type of customer experience doesn’t just require information systems; the information is an element of the experience itself, part of what we’re purchasing, and what differentiates products and services in the market.

New ways of selling

New technologies like 3D printing and the internet of things are allowing companies to rethink existing products.

Products can be more easily customized and personalized for every customer. Pricing can be more variable to address new customer niches. And products can be turned into services, with customers paying on a per-usage basis.

Again, information isn’t just supporting the manufacturing and sale of the product – it’s part of what makes it a “product” in the first place.

Information as a product

In many industries, the information collected by business is now more valuable than the products being sold – indeed, it’s the foundation for most of the free consumer internet. Traditional industries are now realizing that the data stored in their systems, once suitably augmented or anonymized, can be sold directly. See this article on the Digitalist magazine, The Hidden Treasure Inside Your Business, for more information about the four main information business models.

A culture change for “traditional IT”

Traditional IT systems were about efficiency, effectiveness, and integrity. These new context-based experiences and more sophisticated products use information to generate growth, innovation, and market differentiation. But these changes lead to a difficult cultural challenge inside the organization.

Today’s customer-facing business and product teams don’t just need reliable information infrastructures. They need to be able to experiment, using information to test new product options and ways of selling. This requires not only much more flexibility and agility than in the past, but also new ways of working, new forms of IT organization, and new sharing of responsibilities.

The majority of today’s CIOs grew up in an era of “IT industrialization,” with the implementation of company-wide ERP systems. But what made them successful in the past won’t necessarily help them win in the new digital era.

Gartner believes that the role of the “CIO” has already split into two distinct functions: Chief Infrastructure Officers whose job is to “keep the lights on”; and Chief Innovation Offers, who collaborate closely with the business to build the business models of the future.

IT has to help lead

Today’s business leaders know that digital is the future, but typically only have a hazy idea of the possibilities. They know technology is important, but often don’t have a concrete plan for moving forward: 90% of CEOs believe the digital economy will have a major impact on their industry. But only 25% have a plan in place, and less than 15% are funding and executing a digital transformation plan.

Business people want help from IT to explain what’s possible. Today, only 7% of executives say that IT leads their organization’s attempts to identify opportunities to innovate, 35% believe that it should. After decades of complaints from CIOs that businesses aren’t being strategic enough about technology, this is a fantastic new opportunity.

Design Thinking and prototyping

Today’s CIOs have to step up to digital innovation. The problem is that it can be very hard to understand — history is packed with examples of business leaders that just didn’t “get” the new big thing.  Instead of vague notions of “disruption,” IT can help by explaining to business people how to add information into a company’s future product experiences.

The best way to do this is through methodologies such as Design Thinking, and agile prototyping using technologies should as Build.me, a cloud platform that allows pioneers to create and test the viability of new applications with staff and customers long before any actual coding.

Conclusion

The bottom line is that digital innovation is less about the technology, and more about the transformation — but IT has an essential role to play in demonstrating what’s possible, and needs to step up to new leadership roles.

Source: timoelliot.com, November 14, 2016

• Sales and strategic planning teams see IoT as the most valuable.
• IoT advocates are 3X as likely to consider big data critical to the success of their initiatives & programs.
• Amazon and Cloudera are the highest ranked big data distributions followed by Hortonworks and Map/R.
• Apache Spark MLib is the most known technology on the nascent machine learning landscape today.

These and many other excellent insights are from Dresner Advisory Services’ 2016 The Internet of Things and Business Intelligence Market Study published last month. What makes this study noteworthy is the depth of analysis and insights the Dresner analyst team delivers regarding the intersection of big data and the Internet of Things (IoT), big data adoption, analytics, and big data distributions. The report also provides an analysis of Cloud Business Intelligence (BI) feature requirements, architecture, and security insights. IoT adoption is thoroughly covered in the study, with a key finding being that large organizations or enterprises are the strongest catalyst of IoT adoption and use. Mature BI programs are also strong advocates or adopters of IoT and as a result experience greater BI success. IoT advocates are defined as those respondents that rated IoT as either critical or very important to their initiatives and strategies.

Key takeaways of the study include the following:

• Sales and strategic planning see IoT as the most valuable today.The combined rankings of IoT as critical and very important are highest for sales, strategic planning and the Business Intelligence (BI) Competency Centers. Sales ranking IoT so highly is indicative of how a wide spectrum of companies, from start-ups to large-scale enterprises, is attempting to launch business models and derive revenue from IoT. Strategic planning’s prioritization of IoT is also driven by a long-term focus on how to capitalize on the technology’s inherent strengths in providing greater contextual intelligence, insight, and potential data-as-a-service business models.

• Biotechnology, consulting, and advertising are the industries that believe IoT is the most important to their industries.Adoption of IoT across a wide variety of industries is happening today, with significant results being delivered in manufacturing, distribution including asset management, logistics, supply chain management, and marketing. The study found that the majority of industries see IoT as not important today, with the exception of biotechnology.

• Location intelligence, mobile device support, in-memory analysis, and integration with operational systems are the four areas that most differentiate IoT advocates’ interests and focus.Compared to the overall sample of respondents, IoT advocates have significantly more in-depth areas of focus than the broader respondent base. The four areas of location intelligence, mobile device support, in-memory analysis, and integration with operational systems show they have a practical, pragmatic mindset regarding how IoT can contribute greater process efficiency, revenue and integrate with existing systems effectively.

• An organization’s ability to manage big data analytics is critically important to their success or failure with IoT. IoT advocates are 3X as likely to consider big data critical, and 2X as likely to consider big data very important. The study also found that IoT advocates see IoT as a core justification for investing in and implementing big data analytics and architectures.

• Data warehouse optimization, customer/social analysis, and IoT are the top three big data uses cases organizations are pursuing today according to the study. Data warehouse optimization is considered critical or very important to 50% of respondents, making this use case the most dominant in the study. Large-scale organizations are adopting big data to better aggregate, analyze and take action on the massive amount of data they generate daily to drive better decisions. One of the foundational findings of the study is that large-scale enterprises are driving the adoption of IoT, which is consistent with the use case analysis provided in the graphic below.

• IoT advocates are significantly above average in their use of advanced and predictive analytics today. The group of IoT advocates identified in the survey is 50% more likely to be current users of advanced and predictive analytics apps as well. The study also found that advanced analytics users tend to be the most sophisticated and confident BI audience in an organization and see IoT data as ideal for interpretation using advanced analytics apps and techniques.

• Business intelligence experts, business analysts and statisticians/data scientists are the greatest early adopters of advanced and predictive analytics. More than 60% of each of these three groups of professionals is using analytics often, which could be interpreted as more than 50% of their working time.

• Relational database support, open client connectors (ODBC, JDBC) and automatic upgrades are the three most important architectural features for cloud BI apps today. Connectors and integration options for on-premises applications and data (ERP, CRM, and SCM) are considered more important than cloud application and database connection options. Multitenancy is considered unimportant to the majority of respondents. One factor contributing to the unimportance of multi-tenancy is the assumption that this is managed as part of the enterprise cloud platform.

• MapReduce and Spark are the two most known and important big data infrastructure technologies according to respondents today. 48% believe that MapReduce is important and 42% believe Spark is. The study also found that all other categories of big data infrastructure are considered less important as the graphic below illustrates.

 Forbes, 4 oktober 2016

• Enabling real-time integration across on-premise and cloud platforms often involves integrating SAP, Salesforce, third-party and legacy systems. 2017 will be a break-out year for real-time integration between SAP, Salesforce, and third party systems in support of Internet of Things and Industrial Analytics.
• McKinsey Global Institute predicts that the Internet of Things (IoT) will generate up to $11T in value to the global economy by 2025
• Predictive and prescriptive maintenance of machines (79%), customer/marketing related analytics (77%) and analysis of product usage in the field (76%) are the top three applications of Industrial Analytics in the next 1 to 3 years.

Real-Time Integration Is the Cornerstone Of Industrial Analytics

Industrial Analytics (IA) describes the collection, analysis and usage of data generated in industrial operations and throughout the entire product lifecycle, applicable to any company that is manufacturing and selling physical products. It involves traditional methods of data capture and statistical modeling. Enabling legacy, third-party and Salesforce, SAP integration is one of the most foundational technologies that Industrial Analytics relies on today and will in the future. Real-time integration is essential for enabling connectivity between Internet of Things (IoT) devices, in addition to enabling improved methods for analyzing and interpreting data. One of the most innovative companies in this area is enosiX, a leading global provider of Salesforce and SAP integration applications and solutions. They’re an interesting startup to watch and have successfully deployed their integration solutions at Bunn, Techtronic Industries, YETI Coolers and other leading companies globally.

A study has recently been published that highlights just how foundational integration will be to Industrial Analytics and IoT. You can download the Industrial-Analytics-Report-2016-2017.pdf. This study was initiated and governed by the Digital Analytics Association e.V. Germany (DAAG), which runs a professional working group on the topic of Industrial Analytics. Research firm IoT Analytics GmbH was selected to conduct the study. Interviews with 151 analytics professionals and decision-makers in industrial companies were completed as part of the study. Hewlett-Packard Enterprise, data science service companies Comma Soft and Kiana Systems sponsored the research. All research and analysis related steps required for the study including interviewing respondents, data gathering, data analysis and interpretation, were conducted by IoT Analytics GmbH. Please see page 52 of the study for the methodology.

Key Takeaways:

• With real-time integration, organizations will be able to Increase revenue (33.1%), increase customer satisfaction (22.1%) and increase product quality (11%) using Industrial Analytics. The majority of industrial organizations see Industrial Analytics as a catalyst for future revenue growth, not primarily as a means of cost reduction. Upgrading existing products, changing the business model of existing products, and creating new business models are three typical approaches companies are taking to generate revenue from Industrial Analytics. Integration is the fuel that will drive Industrial Analytics in 2017 and beyond.

• For many manufacturers, the more pervasive their real-time SAP integration is, the more effective their IoT and Industrial Analytics strategies will be. Manufacturers adopting this approach to integration and enabling Industrial Analytics through their operations will be able to attain predictive and prescriptive maintenance of their product machines (79%). This area of preventative maintenance is the most important application of Industrial Analytics in the next 1 – 3 years. Customer/marketing-related analytics (77%) and analysis of product usage in the field (76%) are the second- and third-most important. The following graphic provides an overview of the 13 most important applications of Industrial Analytics.
  

• 68% of decision-makers have a company-wide data analytics strategy, 46% have a dedicated organizational unit and only 30% have completed actual projects, further underscoring the enabling role of integration in their analytics and IoT strategies. The study found that out of the remaining 70% of industrial organizations, the majority of firms have ongoing projects in the prototyping phase.
  
• Business Intelligence (BI) tools, Predictive Analytics tools and Advanced Analytics Platforms will be pivotal to enabling industrial data analysis in the next five years. Business Intelligence Tools such as SAP Business Objects will increase in importance to industrial manufacturing leaders from 39% to 77% in the next five years. Predictive Analytics tools such as HPE Haven Predictive Analytics will increase from 32% to 69%. The role of spreadsheets used for industrial data analytics is expected to decline (i.e., 27% think it is important in 5 years vs. 54% today).

• The Industrial Analytics technology stack is designed to scale based on the integration of legacy systems, industrial automation apps and systems, MES and SCADA systems integration combined with sensor-based data. IoT Analytics GmbH defines the technology stack based on four components inclouding data sources, necessary infrastructure, analytics tools, and applications. The following graphic illustrates the technology stack and underscores how essential integration is to the vision of Industrial Analytics being realized.

• Industrial Internet of Things (IIoT) and Industry 4.0 will rely on real-time integration to enable an era of shop-floor smart sensors that can make autonomous decisions and trade-offs regarding manufacturing execution. IoT Analytics GmbH predicts this will lead to smart processes and smart products that communicate within production environments and learn from their decisions, improving performance over time. The study suggests that Manufacturing Execution System (MES) agents will be vertically integrated into higher level enterprise planning and product change management processes so that these organizations can synchronously orchestrate the flow of data, rather than go through each layer individually.
  

Source: business2community.com, 19 december 2016

As part of our agenda of looking at the impact of technology and innovation on economic growth and development, we’ve written about the internet if things (IoT) for several years. In January 2013, we said:

“‘Internet of things’ will have a huge impact on how businesses look at their business operations, efficiency and productivity – in the next few years it could actually help restore confidence after the global economic crisis, providing a leap forward in global productivity. Combine this with the need to interpret the huge number of data points that will be generated – the so-called ‘big-data – and automated business intelligence and reporting systems also become increasingly important. The next wave of innovation will therefore come around the internet of things, big data and business intelligence.”

Fast forward to 2017, writing in the World Economic Forum blog, Dominic Gorecky and Detlef Zühlke of the German Research Centre for Artificial Intelligence (DFKI) say the internet of things (IoT) is already starting to affect environments of all kinds – homes, cities, travel, logistics, retail and medicine, to name just a few – and it will not stop at our factory gates, either.

According to a recent estimation by McKinsey, the potential economic impact of IoT applications in 2025 is between US$ 3.9 and $11.1 trillion, of which $1.2 to $3.7 trillion is allotted to IoT applications within the factory environment. Also known as smart manufacturing, or Industrie 4.0 in Germany, these are fully networked manufacturing ecosystems driven by the IoT.

Gorecky and Zühlke add that in smart manufacturing, where all ‘factory objects’ will be integrated into networks, traditional control hierarchy will be replaced by a decentralized self-organization of products, field devices and machines. Production processes have to become so flexible that even the smallest lot size can be produced cost-effectively and just in time to the customer’s individual demands. Customers are driving this development too, as they can design and order products at the click of a mouse. They can also expect products to be delivered within a few days – or even hours – and don’t want to wait weeks for goods to travel from far regions of the world where labor costs are lower.

Despite this huge potential, the introduction of IoT technologies in the rather traditional domain of manufacturing will not happen abruptly: investment cycles are long, and robustness of processes and technologies outweigh the striving for innovation. Too many questions have to be answered first.

As IoT technologies penetrate ever more deeply into our factories, down to the smallest piece of equipment, technology providers and factory planners must find solutions to four main problems:

• How to assure the interoperability of systems
• How to guarantee real-time control and predictability, when thousands of devices communicate at the same time
• How to prevent disruptors, or competitors, taking control of highly networked production systems
• How to determine the benefit or return on investment in IoT technologies.

To compensate for technological uncertainty and financial risks, adequate pilot environments are needed. Here, smart manufacturing technologies and strategies can be implemented, evaluated and showcased for the first time. Smart manufacturing is a network paradigm affecting wide-ranging areas from automation to IT, from digital planning of a product to its recycling, and from smart sensors to business applications.

There is no single-solution provider that can cover all of these aspects at once. So, for holistic solutions to emerge, there has to be a network of technology providers joining forces and competences to develop compatible solution blocks that fit the future requirements of technology users.

As a result of all of this in a broader context beyond just manufacturing, Mike Krell, an analyst at Moor Insights & Strategy, says that 2017 will be another year of growth for the IoT — and potentially some contraction. IoT is still in its infancy in terms of dollars and deployments, and that can’t last much longer, before market frustration sets in. He says 2017 must become the year where the focus on real deployment and monetization of IoT systems, including both software and hardware.

2017 will also be a year of contraction. There are way too many ‘platform’ and hardware providers trying to gain traction in the market. Small platform providers will either disappear or get swallowed up by ‘bigger fish’, and 2017 is likely to end with many fewer players than today. Hardware will continue to expand, but companies that only provide single or very narrow solutions will get swallowed up or disappear altogether, depending on the quality and security of their products.

Writing in Forbes magazine, he says these are key things to watch for 2017 in IoT:

• IoT semiconductor and sensor volumes will skyrocket. 2016 was a year of consolidation for IoT semiconductor makers. There was Broadcom and Avago (and then Broadcom jettisoning their IoT business to Cypress) and then Qualcomm swallowing up NXP (and the former Freescale in the process). Krells says that NXP is one of the best positioned IoT semi vendors, and if a deal with Qualcomm is completed, the combined company has the breadth of technologies and capacity to dominate the IoT market. ARM cores will continue to dominate IoT, and sensor manufacturers will continue to see volumes rise. He adds that 2017 may see more consolidation –Silicon Labs could grow bigger or be gobbled up.
• The IoT platform shakeout will begin. The year started with lots of noise from new vendors with a definite slowing as 2016 came to a close. There are just too many vendors trying to push undifferentiated solutions.
• Changes in ‘edge’ or ‘fog’ architectures will become critical to implement. The edge or the fog is the part of the network between the devices (where the data originates) and the cloud. Traditionally this had been a simple aggregation point or gateway, but that just won’t cut it for IoT. The massive amount of data generated by IoT devices will put strains on the network, requiring edge devices to get much smarter. Mainstream vendors such as Cisco Systems, Dell EMC and Hewlett Packard Enterprise recognize this and are pushing smarter IoT edge devices. These devices are, in simple terms, a combination of a server and gateway. With the increase in computation, storage and networking capabilities edge-based analytics will become a critical element in the success of IoT.
• Telco and communication choices will continue to be messy. Telecom operators’ strategies and business models for generating revenues from IoT will continue to develop through 2017—and won’t be set by this time next year. For telcos, the battle will continue between NB-IoT and LTE-M based on region and monetization models through 2017. Infrastructure providers such as Ericsson and Huawei will increase in importance, with strong portfolios of IoT hardware and software solutions. Alternative LPWAN (lo power wide area network) technologies will become increasingly strong in niches where the bandwidth, capacity and security of 3GPP standards aren’t necessary (or cost affective). These include LoRa, Ingenu and Sigfox.
• Regulation and standardization continue to come into focus but will evolve continuously. 2016 brought us a little closer to standards interoperability with the merger of OIC and the Allseen Alliance into the Open Connectivity Foundation. Other collaborations including ZigBee, Thread Group and Z-wave continue to move the market toward more cohesive and simpler solutions. However, there will still be a great amount of fragmentation and no clear-cut winners. Apple HomeKit and Google Weave will play a role, but how this fleshes out is anyone’s guess going into and coming out of 2017. On the regulatory front, expect to see more government interest in 2017, as IoT becomes more pervasive in smart cities, the public sector and energy.
• Security gets its due. Security was finally taken seriously in 2016, largely because of real IoT hacks. The big denial-of-service attack in October, and the potential of a drone injecting a malicious virus via lights (from outside a building), caused great concern throughout the industry. With all the new vulnerable devices now being put into service, 2017 will see hackers continue to exploit IoT systems. Expect large scale breaches, as hackers look for newly connected devices in the energy and transportation areas.
• Smart cities will lead the charge in IoT deployments. The awareness of what ‘smart city’ means has begun to come to the attention of residents. They value safety (smart lighting), convenience (transportation, parking) and potential cost savings (smart meters, on-demand trash pickup), and cities can deliver. Cities will continue to be strained by the need for money to support the deployment of sensors (to gather data) and the integration of citywide systems.
• Smart home technology will become, smarter, more secure and easier to use. Amazon Echo and Google Home made great strides in 2016, both becoming more mainstream appliances in the home. However, networking bandwidth and connectivity between devices and systems is still a major problem for consumers. Networking continues to be painful. Streaming video rules the home entertainment market, and the need for increased bandwidth has put strains on home network bandwidth. Expect new announcements on mesh or mesh-like products with simpler network management in 2017. There continue to be too many applications and too many technologies to choose from to make it all work together seamlessly. Hopefully there will be some real advancements in interconnection in 2017.

Source: thenextsiliconvalley.com, January 8, 2016

Seven insights summarizing the Industrial IoT market

The industrial Internet of Things (IIoT) is projected to become a large-scale, high-growth market that will have a transformative impact on a broad range of industry sectors, from healthcare and retail to automotive and transport.

Here is a brief overview of the key factors to know about this important market, based on expert insights from the report Industrial Internet of Things Market Outlook and Forecasts 2021-2028 by Mind Commerce, a specialized market research firm focused on digital technologies and the telecommunications industry.

1. What Is industrial IoT?

IIoT is considered the fourth industry revolution, with the power to radically change industrial operational processes from start to finish. Other revolutions included mechanical (1784), moving assembly lines (1923), and computerization and the foundation of the Internet (1969).

“Just as industrial production was transformed by steam power in the nineteenth century and electricity in the early twentieth century, so will industry be profoundly transformed by IIoT and related initiatives such as Industry 4.0 (the so called fourth wave of technological advancement in industry) in the early part of this century,” according to Mind Commerce.

Industrial IoT offers a vision of the future based on intelligent manufacturing. Today machines are designed to operate in sync with each other in a production line. In the future, machines will communicate and coordinate with each other within smart factories, drawing on the Internet, interconnected sensors, and other cutting-edge technologies to enhance production and take industrial automation to the next level.

2. Industrial IoT Market Growth

The global industrial IoT market is expected to near $1,119.4 billion by 2028, increasing at a 17.0% compound annual growth rate (CAGR) from 2021 to 2028.

Software is the largest market segment, accounting for 44% of the total market in 2021. Europe is the largest region, with 41% of the total market in 2021.

3. IIoT Technologies

Industrial IoT will rely on a powerful blend of interrelated technologies such as artificial intelligence, augmented reality, data analytics, machines-as-a-service, robotics, self-driving vehicles, self-organizing production, sensor-driven computing, and virtual reality.

4. Industrial IoT Market Drivers

IIoT will help manufacturers optimize their operations by providing real-time monitoring and the ability to conduct predictive maintenance. Companies that leverage IIoT effectively will have the tools and systems to track asset performance, predict failures, increase efficiencies, and reduce unplanned downtime.  

“Successful companies will be those that understand how and where IoT technologies and solutions will drive opportunities for operational improvements, new and enhanced products and services, as well as completely new business models,” according to Mind Commerce.

5. IIoT Market Challenges

While industrial IoT holds great promise, challenges remain. Industrial IoT lacks standardization — some manufacturers rely on a small set of proprietary, incompatible technologies.

Data security and privacy breaches is another serious concern. Because IoT devices carry out physical tasks, a data breach could have grave consequences.

6. IIoT Industry Verticals

IIoT solutions are poised to transform a variety of industry verticals including automotive and transportation, cargo and logistics, healthcare, manufacturing, oil and gas, smart cities, and utilities.

7. Top Market Players

Key IIoT companies include ABB, Accenture, AGT International, ARM Holdings, ATOS, B+B SmartWorx, Bosch, C3, Inc., Cisco System Inc. Digi International, Echelon Corporation, Elecsys Corporation, General Electric, Hitachi, IBM, Oracle, PTC, Real Time Innovation, Rockwell Automation, SAP, Sensata Technologies, Siemens, Wind River, Worldsensing, and Wovyn LLC.

Author: Sarah Schmidt

Source: Market Research Blog

The emergence of the Internet of Things and its possible impact on the fashion industry

The Internet of Things (IoT) is slowly but indisputably changing all the aspects of the fashion industry. This includes smart clothes, engaging and interactive customer experience, combining fashion and health, wearable technology and generating power through solar cells or kinetic energy. The possibilities are endless as this technology is being implemented in our daily clothing items providing us with many benefits even outside the fashion world.

Health benefits

Probably one of the most significant contributions our society can notice in the fashion industry is health-related. Smart clothing has an enormous potential to monitor and measure the health of a person who is wearing these items. We've already scratched the surface with smartwatches which are able to measure heart rate and diabetes, detect a seizure, help with posture, and much more. Besides accessories, some fashion brands have focused on developing lines of smart clothes that will include an ECG and heart rate sensor. This smart clothing will send data to smartphones through an app which will then help you to analyze your health and seek medical advice if needed.

Retail space customization

The power of the IoT can even create a unique shopping experience for customers. In other words, the physical experience can be improved by leveraging technologies which use shoppers' data on online platforms to use it in the actual stores. With a deeper understanding of customer behavior, companies can increase their sales results by giving their customers exactly what they need. With this technology, companies can track customer movements in the store once they log into the app. This way, they can understand their interest across various pieces. We can expect the technology in this area will only grow, and customers will be able to enjoy a more focused, customized, and simpler shopping experience.

Improved supply chain

The ability to improve the supply chain and make it more effective is vital for ethical companies. With help of the IoT, companies can uniquely tell their own stories allowing their customers to even connect with the people who created the items they're wearing and say thank you to them. Moreover, this technology enables companies to tap into their shopper's values and use it to improve the supply chain. However, the IoT has the potential to solve yet another common challenge in fashion: inventory. Finding an efficient way to manage inventory and dispose of the headstock is a major problem, but with the IoT, they can optimize new technologies and make large quantities to order.

Implementing emotions

Fashion communicates emotions. It was only a matter of time until these two worlds become connected in one with the help of technology. However, hardly anybody expected to see new functionalities like regulating body temperature, detecting and relieving stress in our clothing items. When talking about emotions, the real challenge for these companies is to find apps that their consumers actually want and need. After all, we can't talk about full integration of the IoT in the fashion industry without emotions.

Understanding which emotions consumers connect to a brand is what can tremendously improve your communication with them and, consequently, sales results. For instance, what do people feel when they see a picture of Swiss watches? Is it loyalty? Tradition? Security? Or something else? If loyalty is the most common emotion, how to use this emotion and implement it in all stages of the customer journey? Finding a specific emotion is the bridge between a brand and its customers.

Sports player insight

Sports apparel is a big part of the modern-day fashion industry, so it was only a matter of time until sports brands started to realize how much they can benefit from technological solutions. For example, there has been a rise in data analytics in football which provides extremely useful information on players' fitness level during a match. This way, coaches can get an insight into their players’ work rate and decide whether they need to be substituted or not.

Football boots could be another item in sports fashion which has the ability to provide useful data thanks to the IoT. With embedded sensors that would measure every step of a player, coaches would also have data on the strength and angle of impact on the ball. This would be crucial when preparing football teams for big competitions as coaches would have vital information on time to make the right strategic decision.

Conclusion

There is no telling what other areas of the fashion industry will be affected by the development of such powerful technology, but we can only assume it will be revolutionized completely. Having the ability to get information from consumers without wasting their time and adjusting the customer experience accordingly creates endless opportunities. This can improve our life quality as we gain valuable information on our health in such an easy and non-intrusive way. When talking about what the IoT can do for the fashion industry, the profit will significantly increase for various companies as their brands will be completely adjusted to customer's needs and customers will appreciate that.

Source: Datafloq

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

The role of machine learning in making homes smart

Smart home automation has become quite popular in recent years, moving from a luxury for the rich to a staple in many homes. The most popular smart home devices are speakers and thermostats, but a growing number of people are adopting other smart devices like door locks and security cameras.

Residential smart home automation has become a massive industry, and it’s not hard to implement. For those who only want a couple of devices, it’s easy to set up without a professional.

However, for those who want an entire network of connected devices, it’s better to hire a professional. A pro will seamlessly connect everything and recommend the best products.

Smart homes are getting smarter with machine learning

While smart homes are becoming more prevalent, the technology itself is also becoming ‘smarter.’ That’s because smart devices collect and send data back to the company for analysis to fine-tune performance.

Not all smart devices are tools you simply turn on and off with an app on your phone. Some smart devices can ‘learn’ your preferences and run on autopilot by predicting your routines. This is made possible by machine learning.

What is machine learning?

As a component of artificial intelligence (AI), machine learning algorithms collect and use data to imitate the way humans learn. The more a program learns, the more accurate the results.

For instance, a software program using machine learning can figure out when you turn off your lights to go to bed and can be programmed to turn them off automatically at that time every day. If your routine varies by the day, the program can learn those variations as well.

Machine learning algorithms are what power recommendations for videos on YouTube and Netflix. When it comes to smart home automation, machine learning algorithms are designed to make life as convenient as possible by doing most, if not all, of the work.

How smart homes are connected

At a basic level, smart homes are connected over a control network, usually the homeowner’s existing wireless network. Each smart device has some kind of sensor that monitors events (like when the device is turned on or off). Other sensors can be worn to measure vital signs, like the wearer’s heartbeat (think Fitbit). Some sensors can even detect temperature, light, and the presence of a person.

As all of these sensors collect data, the data is sent to a remote server, where it’s used to train machine learning algorithms to predict and respond to certain input to produce a desired result. A simple example of this is having a sensor that beeps when the wearer’s heart rate reaches a certain number of beats per minute.

As more data is collected and processed, the algorithm changes to accommodate what it has learned about the user’s habits and routines. For instance, if the user brews a pot of coffee at 6am, a smart coffee pot can begin the brew cycle at exactly 6am every morning, provided the user sets it up with coffee grounds and water the night before.

Smart automation makes life easier

Everyone wants to make their life more convenient, and that’s why smart home automation is so popular. One of the most useful ways this works is with lighting. A network of smart lights can ‘learn’ when you change the brightness and automatically adjust on time.

Say you start off with 100% brightness in the morning and by 7pm, you turn your lights down to about 70% and then by 11pm you turn them off and go to bed. You won’t have to manually dim your lights because the program will dim them automatically.

Advanced machine learning and conditional triggers

More complex learning can work in conjunction with conditional triggers, too. These triggers and actions are called “smart home scenes.”

For instance, machine learning can teach software to execute various functions only if certain conditions (triggers) are met. For example, if a user turns on the television after brewing their morning coffee, a smart home system can ‘learn’ to turn on the TV automatically right after the coffee begins to brew. In other words, the status of one smart device can determine what another smart device does.

Since smart devices can hold programs like the memory buttons on a radio, users can create multiple scenes and activate them at different times.

Machine learning will drive the next phase of smart home automation

People are no longer satisfied with simply controlling their thermostat with an app on their phone. Today’s automation is driven by machine learning, bringing a whole other dimension to the convenience of smart home automation.

Author: Larry Alton

Source: Smart Data Collective

Welke aanpassingen vraagt een toekomst met 5G internet?

Het snelle vijfde generatie mobiele internet (5G) is over een aantal jaar realiteit. Het belooft snellere download- en uploadsnelheden, meer capaciteit en stabielere verbindingen. Hoewel velen de voordelen zien, moeten we de maatschappelijke transformatie die daarmee gepaard gaat niet onderschatten.

Naast de impact die 5G voor bedrijven zal hebben, zal de hele maatschappij zich moeten aasnpassen. Bij 5G en de toepassingen die we hierbij voor ogen hebben is het niet eens de snelheid die cruciaal is, maar de betrouwbaarheid en de consistentie van de verbinding. Dit vraagt het een en ander aan infrastructuur. Met meer dan 75 miljard apparaten die wereldwijd aan het internet verbonden zijn in 2025, neemt de hoeveelheid data gigantisch toe, en de benodigde capaciteit dus ook. De implicaties van het nieuwe netwerk zijn groter dan op het eerste oog zichtbaar is. Wat brengt 5G nog meer met zich mee?

1. Organische bediening

We gebruiken onze smartphone om te sporten, lezen, muziek te luisteren etc. Maar we verbeteren er ook onze gezondheid mee. Wat echter niet gezond is, is dat we gerust uren per dag naar een scherm staren. De apps om een tijdslimiet in te stellen vinden dan ook gretig aftrek, de liefde voor het oneindig scrollen begint te bekoelen. 5G is hierbij een welkome innovatie. Door de vermindering in latency, en dus de verbetering van de reactiesnelheid, krijgt onze duim meer rust: we bedienen onze telefoon meer met stem en gebaren.

Met een volledig naadloos en onzichtbaar netwerk dat alle apparaten draadloos met elkaar verbindt, worden gegevens met hoge snelheden overgedragen en opgeslagen. Zo blijft de techniek ons nog steeds ondersteunen in praktisch alles wat we doen, maar in een meer natuurlijke vorm. De smartphone zal zeker blijven, maar waarschijnlijk op een meer organische, onzichtbare manier.

2. Meer data, meer datacenters

Wanneer we massaal overstappen op 5G, stelt dat ook bepaalde eisen aan de infrastructuur van het netwerk. Een 5G verbinding kan data bijna duizend keer sneller verplaatsen dan het glasvezelnetwerk van nu. En met de verwachte hoeveelheid verbonden apparaten die elk jaar blijft groeien, is er binnen korte tijd aanzienlijk meer data in omloop. Om deze gegevens met hogere snelheden betrouwbaar te verzenden, is flink meer capaciteit nodig.

Vergelijk de verwerking van deze data bijvoorbeeld eens met watertoevoer. Wanneer je vijftig liter water nodig hebt, bedenk je je eerst hoe ver de waterbron is. Daarna stel je de vragen; hoe dik is de slang en hoeveel constante druk wordt er uitgeoefend om het water eruit te pompen? Zo kun je data ook bekijken. Voldoet het huidige netwerk nog wel aan de benodigde hoeveelheden data en de druk die daarvoor nodig is?

De huidige en toekomstige netwerkinfrastructuur moet evolueren om 5G de ondersteuning te kunnen bieden die het nodig heeft: betrouwbaar, consistent en snel. Het antwoord hierop ligt deels in edge computing; met micro datacenters kan een deel van de druk van de ketel worden gehaald. Hiermee verplaats je in feite de verwerking van data deels richting het eindstation, wat ruis en vertraging vermindert. Hoewel edge computing niet voor elk doel geschikt is, kan het zeker bijdragen aan de verdeling van de lasten. Vooral in een wereld vol IoT (Internet of Things).

3. Privatisering van de netwerken

Wanneer het over 5G en alle bijbehorende mogelijkheden gaat, worden vaak de meest extreme voorbeelden genoemd, zoals de zelfrijdende auto. Maar denk ook eens aan de chirurg die op afstand een operatie uitvoert met een robotarm, of de brandweerman die branden bestrijdt met behulp van een supersnelle, real-time internetverbinding. Die laatste is essentieel om met de juiste snelheid te reageren en anticiperen.

Bij zulke gevallen, waarin het letterlijk om een kwestie van leven of dood kan gaan, is het voorkomen van jitter (ruis) cruciaal. Wanneer de verbinding ook maar een seconde hapert, kan dat voor de patiënt, tegenligger of het slachtoffer te laat zijn. Wat hier nodig is, is een gesloten, speciaal voor zulke doeleinden ingericht netwerk, ver verwijderd van de storingen vanuit andere applicaties.

Om dit te realiseren is mogelijk zelfs nieuwe regelgeving vereist, waarin het onderscheid moet worden gemaakt tussen technologie die invloed heeft op gezondheid en veiligheid en technologie die er puur voor entertainment is. De risico's zijn nou eenmaal niet hetzelfde.

Auteur: Petran van Hugten

Bron: CIO