Many organizations have sung the praises of business intelligence for years, but many of those firms were not actually realizing the full benefits of it. That picture is beginning to change, as advanced analytics tools and techniques mature.

The result is that 2016 will definitely be the ‘year of action’ that many research firms have predicted when it comes to data analytics. That, at least, is the view of Shawn Rogers, chief research officer at Dell Statistica, who believes “we are at a tipping point with advanced analytics.”

If Rogers sounds familiar, it may be due to his early connection to Information Management. Rogers was, in fact, the founder of Information Management when it was originally called DM Review Magazine. He is now in his second year as chief research officer for Dell Statistica. “Prior to that I was an industry analyst. I worked for Enterprise Management Associates and I covered the business intelligence, data warehousing and big data space.”

Rogers believes there are a number of key trends driving business intelligence today that are making it more useful for a greater number of organizations.

“The maturity in the market has helped everyone evolve to a much more agile and flexible approach to advanced analytics. I think there are four things that are driving that which make it exciting,” Rogers says.

“One of them is the new sophistication of users,” Rogers notes “Users have become very comfortable with business intelligence. They want advanced insights into their business so they’re starting to look at advanced analytics as that next level of sophistication.”

“They’re certainly not afraid of it. They want it to be more consumable. They want it to be easier to get to. And they want it to move as fast as they are. The users are certainly making a change in the market,” Rogers says.

The market is also benefitting from new technologies that are enhancing the capabilities of advanced analytics.

“It now functions in a way that the enterprise functions,” Rogers explains. “Now the technology allows advanced analytics on all of the data within your environment to work pretty much at the speed of the business.”

Certainly not insignificant is the economic advantage of more competition from data analytics tool vendors.

“There are all kinds of solutions out there that are less money. It has opened the door for a much wider group of companies to leverage the data in their enterprise and to leverage advanced analytics,” Rogers observes.

“Lastly, the data is creating some fun pressure and opportunities. You have all these new data sources like social and things of that nature. But even more importantly we’re able to incorporate all of our data into our analysis,” Rogers says.

“I know that when I was in the press and as an analyst I use to write a lot about the 80/20 rule of data in the enterprise – the 20 percent we could use and the 80 percent that was too difficult. Now with all these new technologies and their cost benefits we’re not ignoring this data. So we’re able to bring in what use to look like expensive and difficult to manage information, and we’re merging it with more traditional analytics.”

“If you look at more sophisticated users, and economic advantage, and better technology, and new data, everything is changing,” Rogers says. “I think those four pieces are what are enabling advanced analytics to find a more critical home in the enterprise.”

Finally, the other key trend driving the need for speed when it comes to analytics and business intelligence return on investment is where those investments are coming from. Increasingly they are not from IT, Rogers stresses.

“I think there has been a big shift and most of the budgets now seem to be coming from the line of business – sales, marketing, finance, customer service. These are places where we’re seeing budgets fly with data-driven innovation,” Rogers says.

“When you shift away from the technology side of innovation and move toward the business side, there is always that instant demand for action. I think that saturation of big data solutions, the saturation of analytics tools, and a shift from IT to the business stakeholder standpoint is creating the demand for action over just collecting data,” Rogers concludes.

Source: Information Management

7 strategies to discover new market opportunities

From supply chain bottlenecks to inflationary pressures, businesses across industries continue to grapple with a world defined by relentless change. In this unstable market landscape, business-as-usual approaches are nowhere near sufficient for survival or long-term success.

Due to the impact of the COVID-19 pandemic, U.S. corporate bankruptcies reached their worst levels in 10 years in 2020, and we aren’t out of the woods yet. Companies must adapt or risk getting left behind, but how do you find the right opportunities for business growth in a noisy, adrenaline-drenched environment?

Based on our experience providing market research to more than 10,000 clients per year across the globe, serving top management consulting firms, investment banks, and Fortune 500 companies, we’ve identified seven strategies to help you pinpoint new market opportunities for your business.

1. Look for shifts in customer behavior

First, understand how your customers are using your product. Are they doing something different with it now than before? This is how makeup removing wipes and toilet paper alternative wipes came about. By talking to their customers, companies realized that there were other ways people were using baby wipes.

Consumer goods and food companies use this strategy frequently to create new product lines. To find ideas, you can start by scanning the internet and social media pages for people talking about “hacks” for your product, or creatively improvising to use your product in a new way.

2. Consider where waste exists

Sustainability is now a core concern to most demographics, including your customers. Can you innovate to reduce waste in production, transportation, packaging, or at end-of-life? Can you make your (or someone else’s) product last longer? Can you make it modular so that broken or worn pieces can be easily replaced?

3. Investigate the pain points

Understanding pain points is an obvious starting point for most businesses, but these gaps must be addressed because they can give your competitors a welcome edge.

Sometimes a pain point creates literal pain or added friction in a work process. Ask what causes potential injury or stoppage with the product or the workplace around it. For example, professional users began to adopt cordless tools at higher rates when they realized that they reduced job site injuries (no cords to trip over).

Another approach is to see where customers want to save time. Robots and automation show up where repetitive tasks can be reduced, freeing people to do other things. For instance, many people don’t like or don’t have time to mow their lawns, but they want to have cut grass—enter robot lawn mowers.

4. Track trends in your market

Track general societal trends related to your industry and see if you can adapt a product line to take advantage of customer interest in that trend.

A recent example come from big changes in food packaging. Several years ago food bloggers, especially in health food circles, started coming up with bowl meals. Grain bowls. Veggie bowls. All the ingredients put in one bowl for a meal. Soon it spread to restaurants, often healthy or Asian-themed. Then mainstream restaurants, even fast food like KFC, came out with their own versions.

Seeing the continued popularity, the trend finally moved into retail food with a number of brands coming out with frozen dinners in bowls. Now bowls are starting to move to other food products as well.

Take a larger trend in your market or an adjacent market and make it work for your customers with your product.

5. Get ideas from a related industry

Instead of duplicating your competitors, look at what the most successful companies in a functionally related industry are doing, and see if you can apply similar approaches to your business.

Wearable technology like Fitbit took off among consumers, and now pet companies have launched similar trackers for pets with products like FitBark and Whistle. Similarly, at-home DNA testing gained traction with 23andMe and AncestryDNA, and now several at-home DNA testing kits have been designed for dogs to provide information about a dog’s breed and genetic background.

6. Think bigger when it comes to your target consumer

Take a holistic approach to product development and sales by targeting the entire customer base for your product or service, not just the obvious or traditional one.

How does your product meet the needs of your entire potential customer base, not just a certain type of company or one job title within a company (such as the purchaser versus the end user versus the decision-maker)?

Focus on the total benefit of your products to the company as a whole and not one particular customer group and communicate that to those involved in purchase decisions.

Take for example packaging machinery. Machinery producers may deal primarily with purchasing departments to sell new machinery. However, they could broaden their outreach efforts by targeting users and top executives:

• Targeting engineers and production managers could provide supporting arguments on how new machinery could improve work flow and efficiency
• Targeting executives such as chief financial officers to promote the long term cost savings of new machinery could help with approval of new machinery purchases

7. Look for products or services that complement your existing business

What are your customers doing that requires your product, and what do they do after they purchase your product? Are there things you can offer that make that entire process better for your customer?

For example, movie theatres added in-seat dining with restaurant-type meals because they understood that many customers went out to eat before going to the movies. Some parking garages added car washes or car detailing services since customers were already bringing their cars to the location.

Re-imagine your business

The COVID-19 pandemic and all the changes that entailed has caused a great re-thinking of so many things we did before. Take advantage of these changes that started so many movements, shot others forward, and nipped others in the bud.

One of the gifts the pandemic presented to businesses is the chance to find new opportunities and new markets in old places.

Author: Robert Granader

Source: Market Research Blog

7 trends that will emerge in the 2021 big data industry

“The best-laid plans of mice and men often go amiss”– a saying by poet Robert Burns.

In January 2020, most businesses laid out ambitious plans, covering a complete roadmap to steer organizations through the months to follow. But to our dismay, COVID-19 impacted the world in ways we could never imagine, proclaiming pointless many of these best-laid plans.

And to avert the crisis, organizations had to become more adaptable seemingly overnight.

As the pandemic continues to disrupt lives, markets, and societies at large, organizations are seeking mindful ways to pivot and weather all types of disruptions.

Big data trends in 2021

Big data has been and will continue to be a crucial resource for both private and public enterprises.

A report by Statista estimated the global big data market to reach USD 103 billion by 2027.

Despite the benefits big data promised over these past years, it is only now that those promises are coming to fruition. Here are seven top big data trends organizations will need to watch to better reinforce and secure disrupted businesses. Have a look at the summary of those trends:

1. Cloud automation

Capturing big data is easy. What’s difficult is to corral, tag, govern, and utilize it.

NetApp, a hybrid cloud provider, sees cloud automation as a practice that enables IT, developers, and teams to develop, modify, and disassemble resources automatically on the cloud.

Cloud computing provides services whenever it is required. Yet, you need support to utilize these resources to further test, identify, and take them down when the requirement is no longer needed. Completing the process requires a lot of manual effort and is time-consuming. This is when cloud automation intervenes.

Cloud automation mitigates the burden of cloud systems – public and private.

Artificial intelligence (AI), machine learning, and artificial intelligence for IT operations (AIOps) also help cloud automation to review swaths of data, spot trends, and analyze results.

Cloud automation, along with AI, is revolutionizing the future of work by offering:

• Security
• Centralized governance
• Lower total cost of ownership (TCO)
• Scalability
• Continued innovation with the latest version of cloud platform

2. Hybrid cloud

Hybrid cloud is paramount to improve business continuity.

Most organizations are skeptical about sharing data on the cloud for multiple reasons: poor latency, security, privacy, and much alike. But with the hybrid cloud, components and applications from multiple cloud services can easily interoperate across boundaries and architectures. For instance, cloud vs on-premises and traditional integration vs modern digital integration. The present big data industry is converging around hybrid clouds. Therefore, making it an intermediate point for enterprise data to have a structured deployment in public clouds.

One of the major benefits hybrid cloud offers is agility. The ability to adapt quickly is the key to success for current businesses. Your organization might need to facilitate both private and public clouds with on-premise resources to become agile.

Hybrid clouds can:

• Build efficient infrastructure
• Optimize performance
• Improve security
• Strengthen regulatory compliance system

3. Hyperautomation

Listed as one of Gartner’s Top 10 Strategic Technology Trends for 2020, the term ‘hyperautomation’ will continue to be significant in 2021.

“Hyperautomation is irreversible and inevitable. Everything that can and should be automated will be automated,” says Brian Burke, Research Vice President, Gartner.

Automation, when combined with technologies such as AI, machine learning, and intelligent business processes, achieves a new level of digital transformation. Moreover, it helps businesses automate countless IT and decision-making processes.

The core components of hyperautomation are:

• Robotic process automation (RPA)
• Business process management (BPM)
• Advanced analytics
• AI and machine learning

RPA is also referred to as the foundation stone of hyperautomation, and the technology is anticipated to grow to USD 25.56 billion by 2027, according to Grand View Research.

With remote work on the rise, organizations have been pushed to the brink to adopt a digital-first approach. This instilled fear among employees since it started impacting the way they work, leading to a spike in security concerns:

Further use of hyperautomation can easily resolve 80% of threats even before any user can report them, says Security Boulevard.

4. Actionable data

There is no reward for an organization owning large amounts of data that are not useful. You need to transform raw data into actionable insight to help businesses make informed decisions. This can be possible through ‘actionable data.’

“What big data represents is an opportunity; an opportunity for actionable insight, an opportunity to create value, an opportunity to effect relevant and profitable organizational change. The opportunity lies in which information is integrated, how it is visualized and where actionable insight is extracted.” – CIS Wired

The need to glean accurate data and information that further establishes relevant insights for decision-makers is critical for business impact.

Big data will continue its rise in 2021. This might be the first year where we will experience the potential of actionable data.

5. Immersive experience

The immersive web is already undergoing a sudden change we believe will shape 2021.

“Everything that is on a smartphone will soon be possible in XR, and in addition, a range of new applications will be invented that are only possible using VR/AR,” says Ferhan Ozkan, co-founder of VR First and XR Bootcamp.

The future of the immersive web is set to take flight by virtual reality (VR) and augmented reality (AR), also called immersive experience.

In 2020, we experienced a year with a drastic impact on digital entertainment, on apps like Discord, TikTok, and Roblox. Despite being early iterations of immersive web, this trend will be further driven by Gen Z.

Lockdown measures implemented in 2020 have accentuated this drastic shift, more so bringing forth an opportunity for businesses to take charge of the interests of society.

6. Data marketplace and exchanges

By 2022, most of the online marketplace will attract nearly 35 percent of large organizations to stay connected by making them become sellers or buyers of data, predicts Gartner. Top companies like Acxiom, White Pages, and ZoomInfo were already selling data for decades. But with emerging data exchanges, you can easily find platforms to integrate data offerings even from a third-party, e.g. SingularityNET.

This trend will definitely accelerate the rise of technologies like data science, machine learning, deep learning, and the cloud.

7. Edge computing

Edge computing will go mainstream in 2021, predict Gartner and Forrester.

“Edge computing is entering the mainstream as organizations look to extend cloud to on-premises and to take advantage of IoT and transformational digital business applications. I&O leaders must incorporate edge computing into their cloud computing plans as a foundation for new application types over the long term.” – Gartner 2021 Strategic Roadmap for Edge Computing

Many organizations are pushing toward implementing edge computing, to gain benefits like greater reliability, increased scalability, improved performance, and better regulatory compliance options.

The continued rise in utilizing data by technologies like VR, AR, and 5G networks will further drive the growing demand for edge computing.

With organizations switching to remote work globally, many have shifted from traditional servers to cloud computing services to boost security, while some have started turning to edge computing to reduce latency, increase internet speed, and boost network performance.

Stay certified and get ready for the big data change in 2021!

Source: Dasca

Big data: key trends in analytics, technologies and services

There is no doubt that we produce more data in a day than we did in decades of history. We most likely don’t even realize that we produce such a large amount of data simply by browsing on the Internet, so you will be surprised. Keep an eye out for the future trends in Big data analytics and you won’t be caught off guard by future technologies.

Over the past decade, global data has been growing exponentially, and it continues to do so today. It is mainly aggregated via the internet, including social networks, web search requests, text messages, and media files. IoT devices and sensors also contribute huge amounts of data propelling Big data analytics trends.

Throughout various industries, Big data has evolved significantly since it first entered the technical scene in the early 2000s. As Big data has become more prevalent, companies must hire experts in data analytics, capable of handling complex data processing to keep up with the latest trends in Big data analytics.

Data fabric

On-premises and cloud environments are supported by data fabrics, which provide consistent functionality across a variety of endpoints. Using Data Fabric, organizations can simplify and integrate data storage across cloud and on-premises environments, providing access to and sharing of data in a distributed environment to drive digital transformation & new trends in Big data analytics.

Through a data fabric architecture, organizations are able to store and retrieve information across distributed on-premises, cloud, and hybrid infrastructures. Enterprises can utilize data fabrics in an ever-changing regulatory environment, while ensuring the right data is securely provided in an environment where data and analytics technology is constantly evolving.

As opposed to being generated by real-world events, synthetic data is information created artificially. Synthetic data is produced algorithmically, and it can be used as a substitute for production or operational data as well as to validate mathematical models and, more often than not, to train machine learning algorithms.

As of 2022, more attention is being paid to training machine learning algorithms using synthetic data sets, which are simulations generated by computers that provide a wide variety of different and anonymous training data for machine learning algorithms. In order to ensure a close resemblance to the genuine data, various techniques are used to create the anonymized data, such as general conflicting networks and simulators.

Although synthetic data concepts have been around for decades, they did not gain serious commercial adoption until the mid-2000s in the autonomous vehicle industry. It is no surprise that synthetic data’s use in autonomous vehicles began there. It is often the sector that is catalyst for the development of foundational technologies like synthetic data because it attracts more machine learning talent and investment dollars than any other commercial application of AI, further accelerating Big data analytics and the future of marketing and sales.

AI developers can improve their models’ performance and robustness by using synthetic data sets. In order to train and develop machine learning and artificial intelligence (AI), data scientists have developed efficient methods for producing high-quality synthetic data that would be helpful to companies that need large quantities of data.

Data as a service

Data was traditionally stored in data stores, which were designed for particular applications to access, however, when SaaS (software as a service) gained popularity, DaaS was a relatively new concept. As with Software-as-a-Service applications, Data as a Service uses cloud technology to provide users and applications with on-demand access to information, regardless of where the users or applications are located.

In spite of the popularity of SaaS for more than a decade, DaaS has only recently begun to gain broad acceptance. The reason for this is that generic cloud computing services were not originally built to handle massive data workloads; instead, they were intended to host applications and store data (instead of integrating, analyzing, and processing data).

Earlier in the life of cloud computing, when bandwidth was often limited, processing large data sets via the network was also challenging. Nonetheless, DaaS is just as practical and beneficial as SaaS today, thanks to the availability of low-cost cloud storage and bandwidth, combined with cloud-based platforms designed specifically for managing and processing large amounts of data quickly and efficiently.

Active Metadata

The key to maximizing a modern data stack lies in the enrichment of active metadata by machine learning, human interaction, and process output. In modern data science procedures, there are several different classifications of data, and metadata is the one that informs users about the data. To ensure that Big data is properly interpreted and can be effectively leveraged to deliver results, a metadata management strategy is essential.

A good data management strategy for Big data requires good metadata management from collection to archiving to processing to cleaning. As technologies like IoT, cloud computing, etc., advance, this will be useful in formulating digital strategies, monitoring in the purposeful use of data, & identifying the sources of information used in analyses to accelerate the Big data analytics future scope. Data governance would be enhanced by the use of active metadata, which are available in a variety of forms.

Edge Computing

This term describes the process of running a process on a local system, such as the system of a user, an IoT device or a server, and moving that process there. Edge computing allows data to be processed at the edge of a network, reducing the number of long-distance connections between a server and a customer, making it a major trend in Big data analytics.

This enhances Data Streaming, such as real-time data streaming and processing without causing latency; devices respond immediately as a result. Computing at the edge is efficient because it consumes less bandwidth and reduces an organization’s development costs. It also enables remote software to run more efficiently.

Many companies use edge computing to save money alone, so cost savings are often the driving force for their deployment. In organizations initially embraced the cloud, bandwidth costs may have been higher than anticipated, and if they are looking for a less expensive alternative, edge computing might be a good fit.

In recent years, edge computing has become increasingly popular as a way to process and store data faster, which can allow companies to create more efficient real-time applications. The facial recognition algorithm would have to be run through a cloud-based service if a smartphone scanned a person’s face for facial recognition before edge computing was invented, which would take a lot of time and effort.

Hybrid clouds

With the orchestration of two interfaces, a cloud computing system combines a private cloud on-premises with a public cloud from a third party. With hybrid cloud deployment, processes are moved between private and public clouds, which allows for great flexibility and more data deployment options. For an organization to be adaptable to the aspired public cloud, it needs a private cloud.

This requires building a data center, which includes servers, storage, a LAN, and load balancers. VMs and containers must be supported by a virtualization layer or hypervisor. A private cloud software layer must also be installed, enabling instances to transfer data between the public and private clouds through the implementation of software.

A hybrid cloud setup uses traditional systems as well as the latest cloud technology, without a full commitment to a specific vendor, and adjusts the infrastructure accordingly. Businesses work with a variety of types of data in disparate environments and adjust their infrastructure accordingly. The organization can migrate workloads between its traditional infrastructure and the public cloud at any time.

Data center infrastructure is owned and operated by an organization with a private cloud, which is associated with significant capital expenditures and fixed costs. In contrast, public cloud resources and services are considered variable and operational expenses. Hybrid cloud users can choose to run workloads in the most cost-effective environment.

Data service layer

An organization’s data service level is critical to providing data to customers within and across organizations. Real-time service levels enable end-users to interact with data in real-time or near-real-time changing the Big data analytics future scope.

In addition to providing low-cost storage to store large quantities of raw data, the data lakehouse system implements the metadata layer above the store in order to structure data and improve data management capabilities similar to a data warehouse. A single system lets multiple teams access all company data for a variety of projects, such as machine learning, data science, and business intelligence, using one system.

Data mesh

An enterprise data fabric is a holistic approach for connecting all data within an organization, regardless of its location, and making it accessible on demand. A data mesh, on the other hand, is an architectural approach similar to and supportive of that approach. With a data mesh, information about creating, storing, and sharing data is domain-specific and applicable across multiple domains on a distributed architecture.

Using data mesh approaches is a great way for businesses to democratize both data access and data management by treating data as a product, organized and governed by experts. Taking a data mesh approach is a great way to increase scalability of the data warehouse model as well as democratize both data access and data management.

Natural language processing

Among the many applications of artificial intelligence, Natural Language Processing (NLP) enables computers and humans to communicate effectively. It is a type of artificial intelligence that aims to read and decode human language and create meanings. The majority of the software developed for natural language processing is based on machine learning.

By applying grammar rules, algorithms can recognize and extract the necessary data from each sentence in Natural Language Processing. The main techniques used in natural language processing are syntactic and semantic analysis. A syntactic analysis takes care of sentences and grammatical problems, whereas a semantic analysis analyzes the meaning of the text or data.

XOps

A key objective of XOps (data, machine learning, model, platform) is to optimize efficiency and achieve economies of scale. XOps is achieved by adopting DevOps best practices. This will reduce technology, process replication, and automation, ensuring efficiency, reusability, and repeatability. These innovations would allow prototypes to be scaled, with flexible design and agile orchestration of governed systems.

A growing number of algorithms for solving specific business problems is being deployed as AI continues to increase, so organizations will need multiple algorithms for attacking new challenges. By removing organizational silos to facilitate greater collaboration between software engineers, data scientists and IT staff, companies can effectively implement ModelOps and ensure it becomes an integral part of AI development and deployment.

Summary

As the name implies, Big data refers to a large amount of information that needs to be processed in an innovative way to improve insight and decision-making. With the use of Big data technologies, organizations can gain insight and make better decisions, leading to greater ROI for their investments. It is critical to understand the prospects of Big data technology, however, to decide which solution is right for an organization given so many advancements.

Organizations that use data-driven strategies are those that succeed in today’s digital age and are looking to invest in data analytics. As a result of digital assets and processes, more data is being gathered than ever before, and data analytics is helping businesses shape themselves. Here are the latest trends in Big Data Analytics for 2022 and beyond.

Data analytics: questions answered

What are the future trends in data analytics?

AI and machine learning are being embraced heavily by businesses as a means of analyzing Big data about different components of their operations and strategizing accordingly. This is especially the case when it comes to improving customer service and providing a seamless customer experience.

What will be the future of Big data industry?

The future of Big data may see organizations using business analytics to create real-world solutions by combining analyses from the digital world with the analyses from the physical world.

What is the next big thing in data analytics?

Using artificial intelligence, machine learning, and natural language processing technologies, augment analytics automates the analysis of large amounts of data for real-time insights.

What is the next big thing after Big data?

Several sources claim that Artificial Intelligence (AI) will be the next big thing in technology, and we believe that Big Data will be as well.

What are the top trends of data analytics in 2023?

• AR; VR
• Driverless Cars
• Blockchain
• AI
• Drones.

What are the key data trends for 2023?

• Using Big data for climate change research
• Gaining traction for real-time analytics
• Launching Big Data into the real world

What is the scope of Big data analytics?

In today’s world, there is no doubt that Big data analytics is in high demand due to its numerous benefits. This enormous progress can be attributed to the wide variety of industries that use Big data analytics.

Is Big Data Analytics in demand?

The wide range of industries that are using Big data analytics is undoubtedly a major reason for the growth of the technology.

What are the critical success factors for Big data analytics?

• Establishing your mission, values, and strategy,
• Identifying your strategic objectives and “candidate” CSFs
• Evaluating and prioritizing them
• Communicating them to key stakeholders
• Monitoring and measuring their implementation.

Author: Zharovskikh Anastasiya

Source: InData Labs

Analyst and consulting firm, Business Application Research Centre (BARC), has come out with the top BI trends based on a survey carried out on 2800 BI professionals. Compared to last year, there were no significant changes in the ranking of the importance of BI trends, indicating that no major market shifts or disruptions are expected to impact this sector.

• Data discovery and visualization,
• Self-service BI and
• Data quality and master data management
• Data labs and data science, cloud BI and data as a product were the least important trends for 2017.

• “With areas such as accounting and finance more or less under control, companies are moving to other areas of the enterprise, in particular to gain a better understanding of customer, market and competitive dynamics,” said Carsten Bange.
• Many BI trends in the past, have become critical BI components in the present.
• Many organizations were also considering trends like collaboration and sensor data analysis as critical BI components. About 20% respondents were already using BI trends like collaboration and spatial/location analysis.
• About 12% were using cloud BI and more were planning to employ it in the future. IBM's Watson and Salesforce's Einstein are gearing to meet this growth.
• Only 10% of the respondents used social media analysis.
• Sensor data analysis is also growing driven by the huge volumes of data generated by the millions of IoT devices being used by telecom, utilities and transportation industries. According to the survey, in 2017, the transport and telecoms industries would lead the leveraging of sensor data.

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

Interpreting market data during the COVID-19 pandemic

Business and economic activity fluctuates during the course of the year. Some of this fluctuation is idiosyncratic, but much is tied to the time of year and the change of seasons.

Outdoor construction projects are easier to do in dry, warm weather, so especially in the northern half of the United States, construction spending and housing starts tend to be higher during the second and third calendar quarters than they are during the other two quarters.

The volume of retail sales for clothing and electronics is higher in December because of seasonal gift-giving. Economy-wide, clothing and electronics merchandisers can expect to record about one-sixth of their annual sales in the month of December.

In contrast, grocery stores experience much less monthly variance in the level of sales. Employment, especially for contract and temporary workers, also exhibits seasonal changes.

Identifying “True” Trends for a Business or Economic Sector

When a businessperson is trying to assess the trend of a company’s sales (or when an economist is trying to assess the health of economic activity), he or she will want to filter out these expected seasonal effects to get a better view of the “true” trends. A quick and easy way to abstract from seasonal effects is to compare current activity with the activity in the same period the previous year.

When a CEO discusses financials, he or she will compare sales in the recent months to sales in the same calendar months a year and two years earlier. When the CFO of a public company presents the latest quarterly results to a group of analysts, he or she will compare revenues not just to the previous quarter, but also to the same quarter the previous year.

Common Distortions in the Data to Watch

The “comparable period” approach is not foolproof for abstracting from seasonal effects. For example, the Lunar New Year is a big driver of economic activity in China because it is associated with gift-giving, entertainment, and travel. Measured on the solar calendar, it is a moveable holiday: some years it falls in January, others in February. When comparing economic activity in either of those two months to economic activity a year earlier, one must be aware of when the Lunar New Year occurred in each of those two years.

For economic statistics, a mathematical variant of the “comparable period” approach is employed to “seasonally adjust” the data. A seasonally adjusted statistic for retail sales, motor vehicle production, or gross domestic product is reported at an annual rate that assumes that the seasonal component of activity was at its typical rate during the period.

Thus, if December clothing sales are usually twice as high as in November, and sales in December 2020 are exactly twice as high as those in November 2020, the seasonally adjusted level of sales will be identical for November 2020 and December 2020. If sales in December 2020 are more than twice as high as those in November 2020, then the seasonally adjusted level of sales in December 2020 will exceed that in November 2020.

Seasonal Echoes of the COVID-19 Pandemic

It is pretty ease to see that the COVID-19 pandemic will disrupt the “comparable period” approach to account for seasonal changes. With much economic activity constrained in the second quarter of 2020 because of lockdowns and shelter-in-place orders, second quarter 2021 levels will certainly be higher, but what will we learn from that?

Use of comparable periods from 2019 will be one way to try to abstract from the distortions generated by the pandemic. Eagle-eyed analysts will want to pay attention to what comparable periods companies and journalists are using during this year.

For example, I read a newspaper article last month that discussed growth prospects for electric-powered motor vehicles. To bolster the argument that electric vehicle sales were poised for takeoff, the reporter noted that in China, sales of electric vehicles in January 2021 were six times those of the previous year. The reporter did not mention that sales in January 2020 were depressed by the pandemic, which affected activity in China earlier than in Western Europe and the Western Hemisphere.

A recent blog post from David Lucca and Jonathan Wright of the New York Fed pointed out that the pandemic will likely disrupt the more sophisticated seasonal adjustment mechanisms as well. The large economic disruption of the 2007-2009 Great Recession led to persistent seasonal echoes in seasonally adjusted data in the following years, Lucca and Wright said.

Because seasonal adjustment routines use a weighted average of recent comparable periods to estimate the “normal” seasonal relationship, a large disruption to economic activity, such as with the pandemic or the Great Recession, will introduce spurious seasonal patterns in the historical data. In the case of years soon after the end of the Great Recession, seasonally adjusted data for the first quarter of year typically indicated accelerating economic activity that then seemed to decelerate when the seasonally adjusted data for the second quarter became available.

Making Inferences Will Require Extra Diligence 

While statistical agencies can and have stepped in with manual adjustments to try to mitigate the problems caused when a large, nonseasonal shock appears, the resulting seasonally adjusted series may not be completely “fixed.” Lucca and Wright argue that "There are no easy answers to seasonal adjustment in this environment. The virus changed the economy and seasonal patterns, in some cases temporarily and perhaps permanently in other cases."

When available, analysts should also look at unadjusted data to get a handle on how the economy is progressing, but be aware that inferences about “true” behavior will be more difficult to draw for a number of years.

Author: Thomas Browne

Source: Market Research Blog

Quick Service Restaurants: An Overview of the Major Industry Trends

The quick-service restaurant (QSR) industry is just that, quick. Not only is it a fast-paced industry by nature, but it’s also fast-growing and fast-changing. The last year alone is indicative of how quickly things can shift, but in reality, the last five years have seen tremendous evolution in the QSR sector.

According to Allied Market Research, the global fast food market is expected to reach $931.7 billion by 2027, rising at a CAGR of 4.6% from 2020 to 2027.

In this article, we’ll explore the top three trends influencing the QSR industry today, so you can start implementing changes to your own brand, to stay on top of trends and not fall behind the competition.

1. Location just isn’t that important anymore

The physical footprint for QSRs has transformed rapidly, predominantly in response to the COVID-19 pandemic that has reshaped many industries over the last 18 months.

During this time, one of the biggest things we’ve learned is that QSR brands wield the powers of innovation and creativity, proving that they can adapt quickly to major changes and disruptions in order to not only survive, but prosper.

The new brick-and-mortar model

The ways that QSRs are serving their customers in person has seen a dramatic shift in recent years, but the industry’s evolution has been greatly accelerated in response to the pandemic. QSRs have quickly implementing changes such as:

• Adding more new drive-thru systems with multiple lanes and app-only lanes
• Creating more flexible dining with smaller indoor eating areas and a much larger (if not entirely new) outdoor * space
• Limiting in-store interaction by creating more options such as curbside pickup, walk-up pickup windows and app-based ordering to cut down on close-up interactions between guests and staff members

The rise of ghost kitchens

Ghost kitchens are restaurants without a physical store that a customer can walk into and order food from. Spooky? Not so much. Instead, brands hire the space of an existing restaurant or food preparation facility to make their food, which is available only for delivery (or occasionally for pickup).

Ghost kitchens can give big and small brands the ability to experiment at a low risk because:

• Big brands can leverage these to innovate and build a virtual brand concept
• Smaller brands can go to market without the investment of a brick-and-mortar footprint, and can benefit from partnerships with other brands

This shift in physical restaurant requirements means that brands will have to work to keep consumers engaged digitally, meeting them not only where they eat, but also where they live.

Accelerating delivery options

An increase in hybrid or permanent work-from-home options, coupled with more people moving out of urban areas and into the suburbs, is creating a shift in where restaurants are setting up shop and a demand for different locations than before.

Location does still matter – but not in the way it used to. Restaurants who are closer to their customers can boast faster delivery times which can translate into more frequent deliveries. And quicker, and more frequent deliveries translates to more return customers!

This brings us back to the importance of investing in accelerating more app and delivery options, because the people are just simply not where they used to be.

2. Taking business online

Let’s be honest, we are all extremely reliant on technology and probably spend more time than ever scrolling through our smartphones. In the US alone, people spend an average of three hours a day on their smartphones (more than on TV).

The power of virtual brands and partnerships

It’s not just ghost kitchens that are giving big brands the opportunity to experiment more in this space, but the digital landscape also gives them the ability to launch entire virtual brands with zero physical presence.

Here’s a few examples of what some brands have been doing:

• Pizza Express launched its first (delivery only) virtual brand a year ago under the name of Mac & Wings
• Bad-Ass Breakfast Burritos, created by Dog Haus, focused on creating a virtual brand entirely around breakfast items

Online-only brands are also leaning into platforms like Instagram to connect, present menu items and build a foodie community with customers that historically wouldn’t have happened in a more traditional QSR environment.

All in all, there's a massive opportunity to connect and partner with smaller brands, build excitement around menu innovation, new items and more with less overhead than ever before.

Major chains will continue to experiment with online-only offerings to leverage the power of digital branding and partnerships. Adding virtual brands has benefited many QSRs, and it’s a trend that will likely stick around even after dining in becomes ‘normal’ again.

3. Plant-based and sustainable menu innovation

We’ve talked about the physical store locations and we’ve talked about the digital advertising space — but what about the actual product is changing in the QSR industry?

Topics like health and sustainability are things people say they want more of. But in practice, once those initiatives are launched, customers (especially in the quick-service space) can be quick to reject them because they still want to indulge in that fast-food environment.

You can have your fast food (and stay healthy too!)

The plant-based trend started a few years ago and has been growing since. One familiar example may be Burger King’s Impossible Burger in 2019. Now in 2021, the exponential shift in consumer behavior has moved other brands to start integrating these options within their menus.

In 2018 when the “Rebel Whopper” came onto the scene as the first plant-based menu item, people were skeptical. Now, we’re experiencing a meat-free menu takeover — most fast food menus have at least one item (if not many more) that feature plant-based alternatives from burgers to burritos.

Brands who have embraced this trend are now finding a balance between giving customers their delicious fast food they crave while having the benefit of being able to feel like they are able to choose a healthier option in that same environment. Win, win.

Packaging that’s better for the planet

This is perhaps more of a challenge for QSRs than any other sector in the dining industry: how to be more environmentally friendly with packaging and recycling initiatives.

In a LinkedIn Pulse article by Rosann Ling, Founder & Creative Director of Prism Creative, she shares that 87% of consumers worldwide would like to see significantly less packaging on products, and in the US and the UK, over 70% of them are very concerned about waste production.

But, what are brands doing about it?

• Burger King is currently testing green packaging across the US
• Taco Bell has recently launched an initiative to recycle hot sauce packaging

In a sector where it’s difficult to bring “reduce, reuse, recycle” to life, it takes a commitment on behalf of QSRs to solve environmental challenges. But it is something that is certainly worth investing in, for both our planet and your consumer.

Time to get sustainable

The increase in demand for plant-based menu options is not only so customers can feel like they’re making healthier choices, but also to support a less harmful impact on the environment.

Consumers are becoming more and more aware of the environmental impact of food, and fast food is seen as a major culprit with a large carbon footprint. Legacy perspectives that fast food is poorly farmed, cheap and has less-than-ideal supply chains still exist.

Brands will have to continue to work hard to overcome that reputation from consumers by offering transparency into how they are not only environmentally friendly, but what work they’re doing to combat existing issues from single-use packaging to poor farming practices.

Why? Because consumers are pushing brands to be accountable and open about what they’re doing to help the environment, reduce waste and cut down on their carbon footprint, and brands today have no choice but to listen.

So what's next?

These three areas of change have given the QSR industry a lot to think about, without much time to pivot. Now that the global environment is starting to find its footing again, quick-service restaurants need to look deeper into the opportunities and challenges to refine their strategies and assess long-term shifts.

Here’s a recap of where you can start:

• Get closer to customers through new dining formats such as curbside pick-up vs. third-party delivery, online-only products, brand partnerships, etc.
• Leverage consumer insights to evaluate the potential plant-based alternatives in your menu, and by using biodegradable, compostable or recycled materials in your packaging designs
• Use social media to grow the business’ fanbase, strengthen existing relationships and test responses to new on and off-menu ideas
• Keep your brand relevant and consistent by integrating your online and offline presence

Author: Elana Heffley

Source: Zappi

The BI trends your business cannot neglect in the near future

According to the World Economic Forum’s Future of Jobs Report, the top five trends set to positively impact business growth through 2022 are (1) the increasing adoption of new technology, (2) the increasing availability of big data, (3) advances in mobile internet, (4) advances in AI, and (5) advances in cloud technology.

This nexus of these and other trends, and their accelerated innovation and development (as an example, think of how fast we’ve gone from rotary phones to smartphones, to the dematerialization of other devices onto smartphones, and now to 5G), raises the imperative for organizations to focus their next-decade vision and investment strategy now.

Consider these 2020 and beyond assertions for enterprise analytics and mobility from Ventana Research:

• By 2020, analysis of streams of IoT event data will be a standard component of nearly all big data deployments.
• By 2021, two-thirds of analytics processes will no longer simply discover what happened and why, they will also prescribe what should be done.
• By 2022, one-half of organizations will re-examine the use of mobile devices and conclude the technology being used does not adequately address the needs of their workers, leading them to examine a new generation of mobile apps.

And that’s just a start. In '10 Enterprise Analytics Trends to Watch in 2019', Ventana Research CEO Mark Smith notes that in addition to 5G, enterprise organizations’ mobility strategies must absolutely address accelerating technologies and capabilities, such as:

• Device proximity features that can provide environmental context and suggest where to take action based on location.
• Gestures and camera-based input that make it even easier and faster to engage with business applications.
• Biometrics, from facial recognition to fingerprints, that enable significantly better device, data, and enterprise security.
• High-quality device cameras that make it easy to capture, share, and use photos and videos and their data within business processes.
• Augmented reality (AR) that enables the use of a mobile device’s camera to digitally interpose virtual objects to enhance work experiences.
• Speech recognition and voice assistants on mobile devices that make it simpler for users to access information and act quickly.

The future is here. Is your organization ready to take advantage of the accelerated innovation around enterprise analytics and mobility?

Source: MicroStrategy

The key developments expected in the beverage industry in 2022

Beverage sales rose during the COVID-19 pandemic, but it will be difficult for beverage companies to replicate the record-setting gains of 2020.

The beverage industry faces a variety of challenges including supply issues, increasing prices, and the mature nature of many beverage categories, according to the report U.S. Beverage Market Outlook 2021 by Packaged Facts, a leading market research firm and division of MarketResearch.com.

In the report, Packaged Facts describes six top beverage trends that accelerated during the pandemic and are expected to gain even more traction in the future.

1. Dairy-Free and Plant-Based Alternatives

Dairy-free, plant-based alternative beverages form a growing niche that continues to take share from traditional dairy beverages. Although dairy alternatives were developed for vegans, the market now targets a broader range of consumers including flexitarians and omnivores who are concerned about their health, animal welfare, and climate change.

Currently, almond milk dominates this segment, but oat milk is growing fast. “The biggest challenges and opportunities lie in delivering the taste and texture of dairy milk along with nutritional and dietary profiles that match or exceed it,” according to Packaged Facts.

2. The Next Wave of “Better-for-You” Beverages

“Better-for-you” beverages feature clean labels, natural ingredients, less sugar, fewer calories, and caffeine alternatives. Products with these qualities have been in high demand, and the pandemic only heightened people’s focus on wellness, immunity, and stress reduction.

The next wave of “better-for-you” drinks spotlight ingredients that have perceived health benefits such as superfoods, probiotics, prebiotics, antioxidants, vitamins, minerals, botanicals, adaptogens, and protein. This trend is impacting every beverage category, particularly tea and kombucha, energy and sports drinks, and bottled water.

3. Sugar Reduction

More consumers are working to reduce their sugar intake, driving beverage-makers to release new, reduced- or no-sugar varieties. Soda, juice, energy drinks, and RTD tea manufacturers have all jumped on the bandwagon. Recent product examples include Dr Pepper Zero Sugar, MTN DEW Zero Sugar, Better Juice, Red Bull Sugar Free, Monster Energy Zero, and Brisk Zero Sugar Lemon.

4. Hybrid Beverages

Beverage companies often develop new products by blending the best qualities of different drinks and blurring the lines between product categories.

For instance, energy drinks increasingly include electrolytes and advertise hydration, while sports drinks contain caffeine and other stimulants. Sparkling waters may contain caffeine from tea and flavor from juice, as seen in Ocean Spray Wave. Sodas may cross over into coffee—take Coke with Coffee as one example.

5. CBD Beverages

CBD is another ingredient to watch. Although the FDA has not approved the use of CBD in foods and beverages sold in interstate commerce, some marketers are bringing products to market. Molson Coors recently introduced Verywell, a line of sparkling waters with hemp-derived CBD and adaptogens, through a subsidiary. The drink is only available in Colorado.

6. More Sustainable Packaging

While beverage trends are often driven by what's inside a drink, the package itself is another critical element that is increasingly top of mind.

According to Packaged Facts, single-use plastic packaging continues to be the leading concern and challenge for beverage manufacturers. All the major beverage companies have pledged to phase in bottles made from recyclable, compostable, and biodegradable materials by specific target years in the near future.

In addition, many beverage manufacturers have been transitioning some of their plastic packaging to 100% PET plastic, which has a lower carbon footprint than virgin PET.

Author: Sarah Schmidt

Source: Market Research Blog

The most important Business Intelligence trends for 2020

Companies are in the midst of many profound changes: The amount of data available and the speed of producing new data has been increasing rapidly for years, and business models as well as process improvements increasingly rely on data and analytics.

Against this backdrop, a key challenge is emerging: the efficient and, at the same time, innovative use of data is only possible when capabilities for, and the operationalization of, both analytics and data management are ensured. Many companies are already reaching their limits with a ‘the more data the better‘ approach and cannot fully leverage the benefits they expect due to a lack of data quality or analytical skills.

In addition, there has been an increased focus on data protection since the GDPR came into effect in 2018. Amid a huge flood of information, companies will have to find ways to handle data in a way that not only complies with legal requirements, but also helps to improve processes and make day-to-day business easier.

This year we asked 2,865 users, consultants and vendors for their views on the most important BI trends. The BARC BI Trend Monitor 2020 illustrates which trends are currently regarded as important in addressing these challenges by a broad group of BI and analytics professionals. Their responses provide a comprehensive picture of regional, company and industry specific differences and offer up-to-the-minute insights into developments in the BI market and the future of BI. Our long-term comparisons also show how trends in business intelligence have developed, making it possible to separate hype from stable trends.

BARC’s BI Trend Monitor 2020 reflects on the business intelligence and data management trends currently driving the BI market from a user perspective.

Importance of Business Intelligence trends in 2020 (n=2,865)

1. MD/MQ management. Importance (1-10 scale): 7.3
2. Data discovery/visualization. Importance (1-10 scale): 6.9
3. Establishing data-driven culture. Importance (1-10 scale): 6.9
4. Data governance. Importance (1-10 scale): 6.8
5. Self service BI. Importance (1-10 scale): 6.5
6. Data prep. business users. Importance (1-10 scale): 6.3
7. Data warehouse modernization. Importance (1-10 scale): 5.9 
8. Agile BI development. Importance (1-10 scale): 5.8
9. Real-time analytics. Importance (1-10 scale): 5.6
10. Advanced analytics/ML/AI. Importance (1-10 scale): 5.5
11. Big data analytics. Importance (1-10 scale): 5.5
12. Integrated platforms BI/PM. Importance (1-10 scale): 5.2
13. Embedded BI and analytics. Importance (1-10 scale): 5.1
14. Data storytelling. Importance (1-10 scale): 5.1
15. Mobile BI. Importance (1-10 scale): 5.1
16. Analytics teams/data labs. Importance (1-10 scale): 5.0
17. Using external/open data. Importance (1-10 scale): 4.9
18. Cloud for data and analytics. Importance (1-10 scale): 4.9
19. Data catalogs. Importance (1-10 scale): 4.2
20. Process mining. Importance (1-10 scale): 4.1

The most (and least) important BI trends in 2020

We asked users, consultants and software vendors of BI and data management technology to give their personal rating of the importance of twenty trending topics that we presented to them.

Data quality/master data management, data discovery/visualization and data-driven culture are the three topics BI practitioners identify as the most important trends in their work.

At the other end of the spectrum, cloud for BI and analytics, data catalogs and process mining were voted as the least important of the twenty trends covered in BARC’s survey.

What do these results tell us?

While the two most important trends remained the same as last year with master data and data quality management in first position and data discovery in second, third spot is now occupied by establishing a data-driven culture. This trend, which was newly introduced last year and went straight into fifth place in the rankings, is seen as even more important this year. Self-service BI, on the other hand, went down to fifth place this year whereas data governance remains in fourth.

All in all, these five top trends represent the foundation for organizations to manage their own data and make use of it. Furthermore, it demonstrates that organizations are aware of the relevance of high quality data and its effective use. These trends stand for underlying structures being changed: Organizations want to go beyond the collection of as much data as possible and actively use data to improve their business decisions. This is also supported by data warehouse modernization, which is once again in seventh place this year.

Some trends have slightly increased in importance since last year (e.g., real-time analytics an integrated platforms for BI and PM). However, they all climbed just one rank with the exception of establishing a data-driven culture, which jumped two places. Therefore, no huge shift can be observed in terms of upward trends.

The opposite is the case for downward trends: Mobile BI fell from twelfth to fifteenth place this year, continuing its downward trend that started in 2017. It seems as if the mobile application of BI functions is not seen as important anymore, either because it is available now or because requirements have shifted. Advanced analytics/machine learning/AI is ranked one place lower than last year (down from 9 to 10).

More important than the difference of one rank however is the tendency behind this slight downward trend: In 2018, many hopes were based on new tools using machine learning and artificial intelligence so this topic might have been expected to rise. However, even if we refer to it as a stagnation in perceived importance rather than a 'real' downward trend, this result is surprising.

Source: BI-Survey

The Real Business Intelligence Trends in 2022

Many companies are still adapting to changed requirements due to the COVID-19 pandemic. Although the situation now seems less acute and more long-term changes toward a ‘new normal’ are on the horizon, day-to-day business is far from settled. Some companies are dealing with last year’s decline in orders, while others are coping with the ongoing supply chain disruptions or are still in the midst of adapting their business model to the changed requirements or better equipping themselves for possible future crises.

A look at this year’s business intelligence trends reveals that companies are still working to position themselves well for the long term and are working on the foundation of their data usage. Instead, companies are addressing the root causes of their challenges (e.g., data quality) and also tackling the holistic establishment of a data-driven culture.

The BARC Data, BI and Analytics Trend Monitor 2022 illustrates which trends are currently regarded as important in addressing these challenges by a broad group of BI and analytics professionals. Their responses provide a comprehensive picture of regional, company and industry specific differences and offer insights into developments in the BI market and the future of BI.

Our long-term comparisons also show how trends have developed, making it possible to separate hype from stable trends. BARC’s Data, BI and Analytics Trend Monitor 2022 reflects on the business intelligence, analytics and data management trends currently driving the market from a user perspective.

The Most (and Least) Important Business Intelligence Trends in 2022

We asked 2,396 users, consultants and vendors for their views on the most important BI, analytics and data management trends, delivering an up-to-date perspective on regional, company and industry-specific differences and providing comprehensive insights on the BI, analytics and data management market.

Data quality/master data management, data-driven culture and data governance are the three topics that practitioners identified as the most important trends in their work.

At the other end of the spectrum, mobile BI, augmented analytics and IoT data and analytics were voted as the least important of the twenty trends covered in BARC’s survey.

Master data and data quality management in first position has retained this ranking over the last five years while the second most important trend, establishing a data-driven culture, has steadily increased in importance. 

The significance of these two topics transcends individual regions and industry sectors. Establishing a data-driven culture is a trend that was newly introduced to the BARC Trend Monitor three years ago. Starting from fifth position in the first edition, it made its way up to third place in the last two years and is now ranked number two. 

Data governance has also increased in importance. Having held down fourth position for several years, it rose to number three this year. Data discovery and visualization and self-service analytics (ranked four and five respectively) have been equally consistent trends, but both have now taken a back seat to data-driven culture.

Our View on the Results

Master data and data quality management in first position  has been ranked as the most important trend for five years in a row now. The stability of this trend shows the relevance of having good quality data to be significantly higher than other trend topics with a much broader presence in the media. It also reflects the fact that many organizations place high emphasis on their master data and data quality management because they have not reached their goals yet.

This is in line with findings of other BARC Surveys that repeatedly show that companies are constantly battling with insufficient data quality as a hurdle to making better use of data. Hence, master data and data quality management will remain very important and is also linked to the equally stable significance of data governance, which was ranked in fourth position for four consecutive years before climbing to third place this year.

Establishing a data-driven culture has increased in importance and is now ranked as the second most important trend. Since its introduction to the Trend Monitor in 2019, this trend has always ranked among the top five and is constantly gaining in prominence. This can be explained by the rising awareness that fostering a data-driven culture is vital to realizing the full data potential of a company. 

Data discovery and data visualization and self-service BI have slipped down the rankings slightly this year. However, being ranked four and five in our list of 20 topics underlines their importance to organizations. All the top trends combine organizational and technological elements. They act as a solid foundation on which most companies are keen to put great emphasis.

The top five trends represent the foundation for organizations to manage their own data and make good use of it. Furthermore, they demonstrate that organizations are aware of the relevance of high quality data and its effective use. Organizations want to go beyond the collection of as much data as possible and actively use data to improve their decision making processes. This is also supported by data warehouse modernization, which holds on to sixth position this year. 

Some trends have slightly increased in importance since last year (e.g., data catalogs and alerting). However, most have stayed the same or just changed one rank.

There are some major shifts in the downward trends. Data preparation by business users dropped from rank seven to rank ten due to alerting and agile BI development climbing the rankings. Mobile BI also fell three places to rank eighteen. In this case, a continuous downward trend can be observed over the last four years.

Source: Business Application Research Center (BARC)

Top artificial intelligence trends for 2020

Top AI trends for 2020 are increased automation to extend traditional RPA, deeper explainable AI with more natural language capacity, and better chips for AI on the edge.

The AI trends 2020 landscape will be dominated by increasing automation, more explainable AI and natural language capabilities, better AI chips for AI on the edge, and more pairing of human workers with bots and other AI tools.

AI trends 2020: increased automation

In 2020, more organizations across many vertical industries will start automating their back-end processes with robotic process automation (RPA), or, if they are already using automation, increase the number of processes to automate.

RPA is 'one of the areas where we are seeing the greatest amount of growth', said Mark Broome, chief data officer at Project Management Institute (PMI), a global nonprofit professional membership association for the project management profession.

Citing a PMI report from summer 2019 that compiled survey data from 551 project managers, Broome said that now, some 21% of surveyed organizations have been affected by RPA. About 62% of those organizations expect RPA will have a moderate or high impact over the next few years.

RPA is an older technology, organizations have used RPA for decades. It's starting to take off now, Broome said, partially because many enterprises are becoming aware of the technology.

'It takes a long time for technologies to take hold, and it takes a while for people to even get trained on the technology', he said.

Moreover, RPA is becoming more sophisticated, Broome said. Intelligent RPA or simply intelligent process automation (IPA), RPA infused with machine learning, is becoming popular, with major vendors such as Automation Anywhere and UiPath often touting their intelligent RPA products. With APIs and built-in capabilities, IPA enables users to more quickly and easily scale up their automation use cases or carry out more sophisticated tasks, such as automatically detecting objects on a screen, using technologies like optical character recognition (OCR) and natural language processing (NLP).

Sheldon Fernandez, CEO of DarwinAI, an AI vendor focused on explainable AI, agreed that RPA platforms are becoming more sophisticated. More enterprises will start using RPA and IPA over the next few years, he said, but it will happen slowly.

AI trends 2020: push toward explainable AI

Even as AI and RPA become more sophisticated, there will be a bigger move toward more explainable AI.

'You will see quite a bit of attention and technical work being done in the area of explainability across a number of verticals', Fernandez said.

Users can expect two sets of effort behind explainable AI. First, vendors will make AI models more explainable for data scientists and technical users. Eventually, they will make models explainable to business users.

Likely, technology vendors will move more to address problems of data bias as well, and to maintain more ethical AI practices.

'As we head into 2020, we're seeing a debate emerge around the ethics and morality of AI that will grow into a highly contested topic in the coming year, as organizations seek new ways to remove bias in AI and establish ethical protocols in AI-driven decision-making', predicted Phani Nagarjuna, chief analytics officer at Sutherland, a process transformation vendor.

AI trends 2020: natural language

Furthermore, BI, analytics and AI platforms will likely get more natural language querying capabilities in 2020.

NLP technology also will continue to evolve, predicted Sid Reddy, chief scientist and senior vice president at virtual assistant vendor Conversica.

'Human language is complex, with hundreds of thousands of words, as well as constantly changing syntax, semantics and pragmatics and significant ambiguity that make understanding a challenge', Reddy said.

'As part of the evolution of AI, NLP and deep learning will become very effective partners in processing and understanding language, as well as more clearly understanding its nuance and intent', he continued.

Among the tech giants involved in AI, AWS for example, revealed Amazon Kendra in November 2019, an AI-driven search tool that will enable enterprise users to automatically index and search their business data. In 2020, enterprises can expect similar tools to be built into applications or sold as stand-alone products.

More enterprises will deploy chatbots and conversational agents in 2020 as well, as the technology becomes cheaper, easier to deploy and more advanced. Organizations won't fully replace contact center employees with bots, however. Instead, they will pair human employees more effectively with bot workers, using bots to answer easy questions, while routing more difficult ones to their human counterparts.

'There will be an increased emphasis in 2020 on human-machine collaboration', Fernandez said.

AI trends 2020: better AI chips and AI at the edge

To power all the enhanced machine learning and deep learning applications, better hardware is required. In 2020, enterprises can expect hardware that's specific to AI workloads, according to Fernandez.

In the last few years, a number of vendors, including Intel and Google, released AI-specific chips and tensor processing units (TPUs). That will continue in 2020, as startups begin to enter the hardware space. Founded in 2016, the startup Cerebras, for example, unveiled a giant AI chip that made the news. The chip, the largest ever made, Cerebras claimed, is the size of a dinner plate and designed to power massive AI workloads. The vendor shipped some last year, with more expected to ship this year.

While Cerebras may have created the largest chip in the world, 2020 will likely introduce smaller pieces of hardware as well, as more companies move to do AI at the edge.

Max Versace, CEO and co-founder of neural network vendor Neurala, which specializes in AI technology for manufacturers, predicted that in 2020, many manufacturers will move toward the edge, and away from the cloud.

'With AI and data becoming centralized, manufacturers are forced to pay massive fees to top cloud providers to access data that is keeping systems up and running', he said. 'As a result, new routes to training AI that can be deployed and refined at the edge will become more prevalent'.

Author: Mark Labbe

Source: TechTarget

Using social media data to analyze market trends

Market trend analysis is an indispensable tool for companies these days. Social media gives analysts access to data that might otherwise be tough to collect. Rapidly changing business conditions require deep insight, and a market trend analysis report is a critical tool. Aside from future-proofing businesses, trend analysis reports also help companies tune into current dynamics and create better products or services.

There are many tools and data sources trend analysts use to prepare a market analysis report. However, social media data offers the most fertile ground. Today there are over 4.5 billion social media users worldwide. That’s over half the world’s population accessing social media and interacting with content.

Social media data is even more valuable because of the high costs of generating original research from scratch. In essence, social media platforms offer all the data companies need, and cost-effectively. Here are three major insights that market trend analysts can derive from social media data.

Consumer Preferences

Every business lives and dies with its customers, and assessing consumer preferences is a tough task. While existing customers often make their intentions clear with their purchase patterns over time, market trends often shift and push potential future customers away from a brand’s messaging.

“Usually, the first signs of a shift (in market trends) show themselves through social media or engagement metrics,” writes SimilarWeb’s Daniel Schneider in a recent post on market trend analysis. “This crucial rise or fall in traffic, engagement, or variation in demographics is what reveals your competitive advantage.” In this context, competitive advantage refers to a company or brand’s position in the market and its appeal to consumers, relative to how its competitors are perceived in “the conversation.”

Social media engagement data offers a wealth of insight in this regard. For instance, high-level data such as the number of comments or likes, and engagement per hashtag, provide companies insight into which topics niche consumers are interested in. Monitoring the trends in these metrics also reveals broader market shifts.

A company’s engagement rate trend and conversion ratios offer insight into marketing effectiveness over time. In the same way that a decreasing sales conversion rate over time points to a possible disconnect with consumers, so too does a falling follower or subscriber count.

Thanks to rising social awareness, companies are expected to take stands on important issues these days. Monitoring the usage of hashtags related to these issues, keeping an eye on trending topics, and tracking engagement metrics on content that addresses these issues helps companies easily tune into the current market climate.

When compared to conducting surveys or polls, there’s no doubt that social media data removes biases and presents user opinions in a useful manner.

Seasonal Trends

Many industries are subject to seasonal trends, and market analysts need to figure them out. The consequences of predicting an incorrect trend can be catastrophic, thanks to production and procurement schedules tied to seasonal demand. 

A market trend analysis that mines social media demographic data will uncover seasonal trends at multiple levels. At a high level, trend analysts can figure out who their customers are and what their tendencies look like. Platforms such as Facebook’s Ad Manager provide a wealth of information, right down to the type of devices the user prefers and even their political leanings.

Analysts can dig deeper into these data and uncover specific data points that help them segment their customer audience. For instance, customers older than 50 might prefer a product during fall, but a younger audience might prefer it during spring. By providing demographic data, trend analysts can help their companies meet demand intelligently.

Market trend reports informed by such data help companies anticipate trends that might develop in the future. As strategic business advisor Bernard Marr points out, “By practicing market analysis, you can stay on top of which trends are having the most influence and which direction your market is headed — before any major changes take place — leaving you well placed to surpass your competition.”

Social media data provides companies an easy way to access data that points to major trend changes. Demographic data allows companies to isolate audiences who might form a future customer base and figure out their preferences in advance. In turn, this helps them create production schedules that match that audience’s seasonal preferences.

Market Dynamics

The market a business operates in is subject to a variety of forces. Chief among these is competitor activity. Disruptive products introduced by competitors can seriously harm a company’s earning ability. A famous example of this is Apple eliminating the likes of Palm and Blackberry within a few years after the release of the iPhone.

Monitoring a brand’s social share of voice and comparing that to its competition helps trend analysts figure out who’s occupying the top of users’ minds in the market. Analysts can also correlate these trends to sales volumes and connect product improvements, marketing strategies, and discover broad market trends. These data also help companies build lasting relationships with their customers.

Given the fast pace with which consumer preferences change these days, traditional data-gathering techniques will leave companies playing catch-up. “Because so much of the world is sharing its opinions on every subject at all hours of the day, trends and markets can shift quickly,” says Meltwater’s Mike Simpson. “It is not just the customer of next year or next month that organizations need to consider — but the customer of the next day.”

Whether it’s trends in engagement, demographics, or competitor data, social media data helps analysts gain perspective on how the market is headed.

A Full Picture

Social media platforms offer a treasure trove of user data. Market trend analysts can mine these data continuously to connect business performance and consumer behavior. Social media gives companies a real-time, cost-effective look into their customers’ minds compared to traditional data-gathering methods.

Author: Ralph Tkatchuk

Source: Dataconomy

What to expect in data management? 5 trends

We all know the world is changing in profound ways. In the last few years, we’ve seen businesses, teams, and people all adapting — showing incredible resilience to keep moving forward despite the headwinds.  

To shed some light on what to expect in 2022 and beyond, let’s look at five major trends with regard to data. We’ve been watching these particular data trends since before the pandemic and seen them gain steam across sectors in the post-pandemic world.  

Trend 1: Accelerated move to the cloud(s) 

We’ve seen a rush of movement to the cloud in recent years. Organizations are no longer evaluating whether or not cloud data management will help them; they’re evaluating how to do it. They are charting their way to the cloud via cloud data warehouses, cloud data lakes, and cloud data ecosystems.  

What’s driving the move to the cloud(s)? 

On-prem hardware comes with steep infrastructure costs: database administrators, data engineering costs, flow sweeps, and management of on-prem infrastructure itself. In a post-pandemic world, that’s all unnecessarily cumbersome. Organizations that move their databases and applications to the cloud reap significant benefits in cost optimization and productivity. 

What to know about moving to the cloud(s) in 2022: 

Note that I’m saying “cloud(s)” for a reason: the vast majority of organizations opt for multi-cloud and hybrid cloud solutions. Why? To avoid putting all their data eggs in one cloud basket.  

While cloud data management services make it easy to move the data to their cloud, they also make it easiest to stay in their cloud — and sometimes downright hard to move data from it. Remember, a cloud vendor is typically aiming to achieve a closed system where you’ll use their products for all your cloud needs. But if you rely on a single provider in that way, a service change or price increase could catch you off-guard.  
 
To stay flexible, many organizations are using best-fit capabilities of multiple cloud providers; for example, one cloud service for data science and another for applications. Integrating data across a multi-cloud or hybrid ecosystem like this helps organizations maintain the flexibility to manage their data independently.  

Trend 2: Augmented or automated data management 

Every organization relies on data — even those without an army of data engineers or data scientists. It’s very important for organizations of any size to be able to implement data management capabilities.  

According to Gartner, “data integration (49%) and data preparation (37%) are among the top three technologies that organizations would like to automate by the end of 2022." 

What’s driving the shift to augmented or automated data management? 

Data management has traditionally taken a lot of manual effort. Data pipelines, especially hand-coded ones, can be brittle. They may break for all kinds of reasons: schema drifts when there are changes between source and target schema; applications that get turned off; databases that go out of sync; or network connectivity problems. Those failures can bring a business to a halt — not to mention that they are time-consuming and expensive to track down and fix.  

Automating data management also frees up engineering resources. Gartner also says that by 2023, AI-enabled automation in data management and integration will reduce the need for IT specialists by 20%. 

What to know about data management in 2022: 

By tapping into data services, even small and under-resourced data teams can implement data management and integration — by automating pipelines, quality, and governance on demand. Automation supports flexible pipeline creation, management, and retirement, granting organizations of any size or stage of growth the data observability they need in a continuous integration, continuous deployment (CICD) environment. 

Trend 3: Metadata management 

Since metadata is the glue that holds necessary data management pieces together, it’s no wonder that organizations are aiming to improve their handle on it.  

As different lines of business develop their own shadow IT, the ecosystem grows in complexity: many companies end up buying multiple solutions and tools and then often need to pay consultants to make them work together.  

What’s driving interest in metadata management? 

Business agility is a requirement in today’s chaotic business landscape, which creates enormous demand for analytics. Healthy data is now a must-have for users with varied levels of technical skill. It’s impossible to expect them to become data analysts and engineers overnight in order to find, share, clean, and use the data they need.  

What to know about metadata management in 2022: 

Many companies have multiple data integration tools, quality tools, databases, governance tools, and so on. As data ecosystems become increasingly complex, it’s more important than ever that all those tools can speak to each other. Applications must support bi-directional data exchange. According to Gartner, data fabric architecture is key to modernizing data management. It’s the secret sauce that allows people with different skill sets — like data experts in the business and highly skilled developers in IT — to work together to create data solutions. 

Trend 4: Real-time data access  

Real-time data is no longer a nice-to-have; it is vital to operations ranging from manufacturing to utilities to retail customer experience. In addition, every company needs operational intelligence.  

Any time an event is created, you should be able to provide that event in real time to support real-time analytics. 

What’s driving interest in real-time data access? 

We haven’t just seen the arrival of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) — businesses are now reliant on them. In a world fueled by real-time data, batch integration and bulk integration are no longer enough to keep up.  

What to know about real-time data access in 2022: 

Extract Transfer Load (ETL) has to be supported by other integration styles including streaming data integration to capture event streams from the logs, sensors, and events that power your business. Make sure you’re building an architecture that supports both batch streaming in real time, and also virtual data access such as data replication and change data capture. That way you won’t have to move the data when you don’t want to.   

Trend 5: Line of business ownership of data 

Data is no longer tightly controlled in the back end by a central IT or data organization. In more and more businesses, the organization reporting to a CDO or CIO focuses on governance and compliance while business users process data within their own lines of business.  

What’s driving line of business ownership of data? 

As data becomes the language of business, we’re seeing the proliferation of citizen data scientists, citizen data integrators, citizen engineers, citizen analysts, and more.  

What to know about line of business ownership of data in 2022: 

Low-code and no-code data preparation and self-service data integration tools equip data users on the front end to ingest, prepare, and model the data for their business needs. These new “citizen” data workers are business experts who don’t have a PhD in statistics or engineering. They don’t know R, Python, Scala, Java, C Sharp, or Spark — and they shouldn’t have to. On the other hand, decentralizing data management can create data governance, compliance, and security headaches.  

As more and more data software sits with the line of business, organizations should look for a data fabric that will enable central data engineering teams to monitor what the data preparation teams prepare. That way, data experts can improve data governance and compliance while lines of business maintain ownership of the data itself.   

Author: Jamie Fiorda

Source: Talend

Your company needs a unique vision on the future, on multiple levels

Does your organization have a unique point of view about the future?

If your answer to the question is no, Gary Hamel says that you do not have a strategy. So, how does one establish their own distinct vision for the future?

Working on trends and scenarios gives your organization opportunities to anticipate future events, to explore new possibilities and build optionality. Understanding business environment and industry trends is a crucial starting point for gaining all kinds of vital strategic foresight. But what are these trends and how do you get a hold of them?

The Four Levels of Trends You Need to Know

On level 1 are megatrends.

Megatrends are a part of a larger line of development, a recognizable whole consisting of phenomena with a distinct history and a development direction. These are things that we all know that do not entail any surprises. Examples of megatrends include climate change, digitalization, and urbanization. Megatrends should always be taken into consideration in strategic planning.

Recommended reading for making sense of megatrends: Hans Rosling: Factfullness (2018).

On level 2 are trends.

A trend is a change from something into some specific, clearly visible direction or a direction that has just started to emerge. Trends have a trajectory. Two rising trends can strengthen one another. Trends can be only local phenomena or tied to a single industry.

In a perfect situation, one company can be a trendsetter and, for example, shape the whole industry’s business logic. An example of this is Tesla who revolutionized both the technological solution (an electric car) and its sales (direct to consumer via online sales channel). Most organizations, however, just strive to notice relevant trends in time and to sufficiently answer and adapt to them.

Recommended reading for the COVID-19 pandemic’s trend context: Scott Galloway: Post Corona (2020).

On level 3 are weak signals.

A weak signal is a new and surprising event or phenomenon that can be seen as the first sign of change or a new course of development. Weak signals have no recognizable history, and they can remain singular. Discerning level 2 trends calls for a lot of work, but weak signals demand even more discipline due to their high signal-to-noise ratio. In a perfect situation, an organization practices systematic business environment monitoring that takes emerging events on the edges of the industry into account.

This sort of monitoring is the most cost-effective as a paid service and/or tool. For a smaller organization with a limited budget, this may mean collecting the signals themselves. Even this requires a systematic approach, and for example regularly reviewing observations made by employees in team meetings (for example, a peculiar comment from a customer, an interesting post on LinkedIn, etc.).

Recommended reading for getting better at spotting signals from peripheries: Rita McGrath: Seeing Around Corners (2019).

On level 4 are the so-called black swans.

Black swans are surprising and highly unlikely events that have significant influence and that change the course of development quickly, causing uncertainty. They are an “unknown unknown”, a radical context of ambiguousness where we cannot know things for sure.

A major part of decision-making in business involves uncertainty. Strategic choices must continuously be made in situations where there is only limited information available. There are no quick and easy formulas with which we can remove the uncertainty or identify the black swans waiting for us on the horizon. At worst, forecasts and Excel calculations taken from historical data give us a dangerous illusion of control.

Instead, amidst radical uncertainty, all of us on every level of our organizations must be able to say “I don’t know”. Admitting to not knowing something is a prerequisite for learning, renewing, and making discoveries. Combining things creatively, using abductive analysis and taking leaps of imagination, we can at least start to chart possible black swans, testing, preparing and building resilience.

Recommended reading for anticipating black swans: Nassim Taleb: Black Swan (2007) and John Kay & Mervyn King: Radical Uncertainty (2020).

Renew Your Business to Ensure Future Success

The coronavirus pandemic, when viewed in a trend context, is a black swan that has accelerated and strengthened many trends that were prominent before it (such as e-commerce, remote work, innovations in transportation). The pandemic made decades happen in weeks, as Scott Galloway aptly sums up.

In turbulent times, Gary Hamel sees strategy work as ever more significant in order for an organization to stay relevant. The most important question for management to ponder is: “How are we going to re-invent ourselves and the world around us during the next five years”? To answer this question, your organization needs to recognize and understand the trends that have an impact on your business environment, and what all this means to you.

Recommended reading for the new normal: Gary Hamel & Michele Zanini’s Humanocrazy (2020).

Author: Nora Kärkkäinen

Source: M-Brain