Key Themes at the 2016 Gartner BI and Analytics Summit – Part 1


The Gartner BI and Analytics Summit is one of the most prestigious gatherings for vendors, customers and analysts with a very high focus on products that are setting trends in the BI and analytics. There were two key themes, which emerged from the summit:


There were two key themes, which emerged from the summit


a. The rise of the new cadre of analyst called the “Citizen Data Scientist” who is a blend of business acumen with a statistical know-how

b. Bi-Modal Model: Business drives the budgets while IT ensures data governance


This is a 2 Part blog series. In the first part, I will discuss about the two key themes that emerged during the first day of the summit.


Let’s first look at the first trend in greater detail. In the last few years an increase in digitization of assets meant an unprecedented rise in data. An increase in data meant an opportunity for stakeholders to study every aspect of their business and take data driven decisions rather than rely on gut feel. BI was too rigid and manual to extract golden nuggets from the data. Self-Service BI or Visual BI tools promised great flexibility and control to business users but were error prone and biased by the analysts doing the analysis.



Bi-Modal Model : Business drives the budgets while IT ensures #data governance #GartnerBI Click To Tweet



Enter the Data Scientist.


A perfect mix of programming knowledge with a heavy statistics background, this new breed of scientists seemed to have the muscle to derive insights and the acumen to establish business impact. However, as with any new area, their profiles were few and far between, which meant a massive demand-supply gap and hence, the solution became the problem. The only way out of this was to equip analysts who had the right amount of business and IT skills with a tool through which they can conduct self-service analytics. A tool, which is foolproof, can provide guided analytics and double up as a data scientist in a box.


Enter Smart Discovery tools.


When smart discovery tools were given to business analysts, suddenly there was a sea change in their ability to contribute towards business decision-making. They were now giving focused insights, backed by science, to the business users and not pages filled with dashboards. They were suddenly able to reduce the workload off the data scientists who could focus on bigger problems rather than cater to ad-hoc requests. This new breed of analysts with data science became the Citizen Data Scientist. Gartner has predicted that the number of Citizen Data Scientists is going to increase five fold as compared to the Data Scientists. Visual Self-Service BI tools will not be enough for them to scale. A right mix of analytics and self-service BI will be needed to solve their needs. Data Discovery will no longer be limited to drill-downs and drill-throughs. Machine powered pattern matching techniques will be used more and more to identify trends and anomalies, which will be extended to predict and prescribe future courses of action. The citizen data scientist will become an extremely important cog in driving business decisions, as his ability to derive information from data will become a crucial differentiator.



#Citizen DataScientists r reducing workload off #datascientists who could focus on bigger problems Click To Tweet



The second trend from the Gartner conference was the shift in mantel from IT driving all BI and analytics budgets to the business taking control. This could be seen in many ways:


a. Positioning of Analytics and BI products as business solutions than IT solutions


b. Mix of the floor – from business unit heads to CMOs and operational leads


c. Increase in the number of data science and analysts teams directly funded and controlled by business units.



In one of the floors there was an active discussion over a comment made by an analyst, which summed up the importance of this transition. She said – the role of CMO is not just to drive marketing initiatives, but also to use analytics to understand data and continuously rework on the right Positioning strategy. This meant, that the CMO was now looking at tools and people that can help him or her in taking data driven decisions. Tools thus had to be leaner, meaner and business oriented rather than focus primarily on data governance and security. The big change for the IT organization was that they were now limited to ensuring the backend is solid so that business gets to see the results they are looking for. All front-end tools became a weapon for the business to buy and use rather than rely on IT. Also the mix of the business organization was also no longer limited to only business experts but also included IT experts, analysts and data scientists who were helping out in furthering the business cause. Tools entering the fray were being paid for by the business user, validated by the data scientist and used by the business analysts. Thus, companies were now transitioning from centralized BI – controlled and governed by IT to a Bi-Modal model (controlled by business and governed by IT).



These are exciting times for the business intelligence and analytics space. As analytics becomes more embedded in the business process, it will become mission critical towards the growth and existence of companies.



In the next part, I will cover the transition of BI tools and the rise of Spark at the core of analytics.