The Future of BI Tools

There are a number of analytics providers who have created business intelligence (BI) and analytics platforms.  Some of the big names are IBM's Watson, Microsoft's Power BI, Tableau, SAS, and dozens of other smaller players.  Business Intelligence tools offer interactive, visually-compelling dashboards of real time or near real-time data accessible from numerous sources and data storage environments.  The tools, which can have a desktop or cloud setting, allow for companies to be FAST and FLEXIBLE in developing and sharing business insights across all types of devices and operating systems, engaging more people than ever within the organization in gathering insights and analysis.  Some companies build one-size-fits-all tools while others have developed for niche use and focus on out-performing in a specific category of the BI market.  As with all new and high-growth markets, the competition for users is stiff and large companies are beginning to buy small businesses in areas where they wish to dominate.

Gartner recently published an article entitled "Critical Capabilities for Business Intelligence and Analytics Platforms." They address the next steps for companies wanting to innovate in this field and what factors should be considered by an IT leader when integrating BI into a business or upgrading ones' existing business intelligence capabilities.  Gartner points out that the responsibilities for analytics are shifting to lines of business and as a result satisfaction measures based on user experience, functionality, and integration with existing business practices will become key determinants for those IT leaders when identifying the right solutions for each business, industry, or department.  Gartner also offers very provocative speculations on how quickly BI tools will go from being the exclusive domain of data scientists to an inclusive community with 'citizen data scientists' throughout an organization.  Perhaps that is what Tableau was envisioning in their 2017 trend projection report that I reviewed in my last post.   Here are a few of Gartner's industry projections, which I am copying verbatim just for ease and accuracy. 

Gartner Research Predictions for Future Evolution of BI

• By 2020, smart, governed, Hadoop/Spark-, search- and visual-based data discovery capabilities will converge into a single set of next-generation data discovery capabilities as components of modern BI and analytics platforms.
• By 2021, the number of users of modern BI and analytics platforms that are differentiated by smart data discovery capabilities will grow at twice the rate of those that are not, and will deliver twice the business value.
• By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms.
• By 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be auto-generated.
• By 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not.
• Through 2020, the number of citizen data scientists will grow five times faster than the number of data scientists.

Perhaps I was showing my naivete in my last article, but if Gartner is correct, business intelligence and analytics have the capacity to evolve into a business-appropriate version of my Google Home, were my Google Home to work correctly, within 2.5-3 years.  This astounds me.  Particularly because my Google Home is supposed to give me voice-activated integration with the web and control over music, security, lights, and various other smart home devices without having to invest much time handling the mobile app. At the moment though, Google Home still has trouble understanding my questions, understanding my kids' voices, or responding consistently to the request, "Ok Google. Play the sound of crickets at night." Google Home does not having the learning yet to know what type of sound I am looking for: a sound bite, a relaxation sound track, or a music group.  To imagine that citizen data scientists could be working throughout an organization getting successful, real time insights, and doing so with natural language processing, voice direction, and AI only 3 years from now feels like such a stretch.  But we will see.  It may not be 3, but it may be 5 or 7. 

Regardless of the timeline, for business leaders and most marketers,  a specialty in understanding how the data is gathered may not be necessary for long.  If they are lucky, as Gartner states, it may matter most what questions we start asking and where we look for those answers, and how good we are at interpreting them.  I can hear your collective sigh of relief from across the wireless router.