“Business intelligence continues to be one of the fastest-moving areas in the enterprise,” writes David Weldon (@DWeldon646), Editor-in-chief at Information Management, “and the techniques that organizations are using to drive adoption and get value from their data are multiplying.”[1] James Richardson, Qlik‘s Business Analytics Strategist, reports Gartner has found, “BI and analytics were still the #1 investment priority for CIOs in 2015.”[2] Ellie Fields (@eleanorpd), Vice President of Product Marketing at Tableau, observes, “Norms about business intelligence are evolving, and, as they do, lead to cultural change at some workplaces. This change is driven not only by fast-moving technology, but also by new techniques used to get value from data.”[3] Among the cultural changes, she notes, “More organizations opened up data to their employees, and more people came to see data as an important tool to get their work done.”

Ryan Mulcahy writes, “Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting.”[4] He adds:

“Companies use BI to improve decision making, cut costs and identify new business opportunities. BI is more than just corporate reporting and more than a set of tools to coax data out of enterprise systems. CIOs use BI to identify inefficient business processes that are ripe for re-engineering.”

A lot has changed in the BI field since Mulcahy wrote that back in 2007; but, the basic definition still applies. One of the most important changes has been the emergence of artificial intelligence. More specifically, the maturation of cognitive computing is having a significant impact on generating insights businesses can use to increase sales, improve processes, and align corporate goals. Below are some of the business intelligence and analytics trends pundits expect to see in the coming year.

Data Visualization

We are all familiar with the old saw, “A picture is worth a thousand words.” That is certainly true in the field of business intelligence and big data analytics. Fields writes:

“Data is changing the conversation — in boardrooms, in the media, and in social media. People are visualizing their data to explore questions, uncover insights, and share stories with both data experts and non-experts alike. As data usage grows, … employers will look for candidates who can think critically with data. Visual analytics will serve as the common language, empowering people to reach insights quickly, collaborate meaningfully, and build a community around data.”

Data visualization is becoming important for individual users as well as businesses. Richardson observes, “By creating visual apps, users are expressing their views and learning about themselves through being actively engaged with the growing volumes of data. You can see this trend in the rise of the quantified-self movement at an individual level and data-driven journalism in the mass media, altering how people are using public data to understand how society works.”

Self-service Analytics

“Self-service BI is the new normal,” writes Richardson, “but that doesn’t mean anarchy.” He explains:

“With more data out there, users want to become more self-sufficient in creating their own analysis rather than relying on others, but this means they need to work in a managed data space. As such, governed data discovery is becoming a top priority. Within a framework of governance, users will focus their energy on getting insights from their analysis. They’re able to ask ‘why?’ multiple times, rather than question whether the data is correct. When everyone is using the same information, more efficient, accurate decisions are made.”

This is an area where cognitive computing really shines. Because cognitive computing systems can integrate both structured and unstructured data, providing access to the same data means the end of departmental data silos. Cognitive computing systems also greatly enhance the decision making process because they can handle most routine decision making leaving human decision makers free to tackle more challenging decisions. Even for those decisions, cognitive computing systems can provide insights to help. Fields observes, “Self-service analytics tools have changed people’s expectations for good. In 2016, people will seek empowerment across the data continuum, especially as more millennials enter the workforce.”

Data Integration

As noted above, data integration is essential if businesses want corporate alignment. Fields believes we are entering a new and exciting era of data integration. She explains:

“These days many companies want agile analytics. They want to get the right data to the right people, and quickly. This is no small challenge, because that data lives in many different places. Working across data sources can be tedious, impossible, or both. In 2016, we’ll see a lot of new players in the data integration space. With the rise of sophisticated tools and the addition of new data sources, companies will stop trying to gather every byte of data in the same place. Data explorers will connect to each data set where it lives and combine, blend, or join with more agile tools and methods.”

Cognitive computer systems are ideal for connecting, combining, blending, and joining data to derive actionable business insights.

Data Scientist in a Can

“Non-analysts across the organization are becoming more sophisticated,” Fields observes. “They’ve come to expect more than a chart on top of their data. They want a deeper, more meaningful analytics experience. Organizations will adopt platforms that let users apply statistics, ask a series of questions, and stay in the flow of their analysis.” Two keys for democratizing data analytics for non-analysts are natural language processing (i.e., the ability to ask questions in natural language and receive understandable answers using that same language) and embedding subject matter expertise in the system. To learn more about this subject, read my article entitled “Cognitive Computing Can Help with Data Scientist Shortage.”

Cloud Analytics

The cloud has made a dramatic impact on businesses and the move to the cloud continues apace. Fields writes, “In 2015, people began embracing the cloud. They realized putting data in the cloud is easy and highly scalable. They also saw that cloud analytics allows them to be agile. In 2016, more people will transition to the cloud thanks, in part, to tools that help them consume web data.” Getting analytics right is critical for making the correct business decision. Companies want to find causation not correlation; because correlations can be useless. For more on that topic, read my article entitled, “Big Data Analytics: Determining Causation rather than Correlation.” One way of ensuring that spurious correlations aren’t being made is to analyze as many confounding variables as possible. Cognitive computing systems are ideal platforms for this because they can handle many more variables than has been previously possible. Richardson explains:

“Enabling users to see a broad range of factors contributing to their business is becoming more important than ever. With the ability to combine both internal and external data sources, users now have access to more context around their data, which ultimately leads to more insights and better decisions. Adding sociodemographics or location data to analysis easily and quickly can help organizations de-risk some of their management choices.”

Mobile Devices

Mobile devices are becoming ubiquitous and their use in all aspects of business is growing. BI and analytics are no exceptions. Richardson elaborates:

“Mobility is becoming more important than ever for data users. This means that enabling multi-device lensing of BI and analytics will gain importance. For instance, 85 percent of respondents from the U.S. and 77 percent of respondents from the rest of the world complete their objectives by using multiple devices simultaneously. Having unlimited access to their data can help users ask ‘why?’ anytime, and find the answer quickly. BI and visualization solutions that don’t support users moving from device-to-device often and at speed will not deliver the kinds of experience that people want.”

Fields adds, “Mobile analytics has grown up and moved out. It’s no longer just an interface to legacy business intelligence products. In 2015, products with a fluid, mobile-first experience began to emerge. Working with data out in the world is going from being a chore to becoming a dynamic part of the analytics process.”

Conclusions

Business intelligence and analytics are growing in importance. Fields concludes, “Early adopters are already learning from this data, and others are realizing they should.” Richardson observes, however, that too few companies are moving fast enough. “Broader use of Predictive Analytics remains an aspirational goal for most companies,” he writes. “Suboptimal data quality continues to be problematic.” With the emergence of cognitive computing, companies no longer need to worry about whether big data is a passing trend. It isn’t. The name will likely pass into history (because “big” will be too quaint a term to describe the size of data being analyzed), but analytics are only going to become more important in the years ahead.

Footnotes
[1] David Weldon, “Ten Top Business Intelligence Trends to Expect in 2016,” Information Management, 30 November 2015.
[2] James Richardson, “Former Gartner Analyst Reveals 2016 Business Intelligence Market Predictions,” Solutions Review, 23 November 2015.
[3] Ellie Fields, “Top 10 Business Intelligence Trends for 2016,” ITProPortal, 19 November 2015.
[4] Ryan Mulcahy, “Business Intelligence Definition and Solutions,” CIO, 6 March 2007.