6 min. read

AI meets BI

Bringing AI to BI to give leaders more relevant, timely answers to their most important questions.

What’s the most frustrating aspect of using current enterprise solutions for a senior executive? It is the inability to find timely and reliable answers to their questions. Often, answers do lie somewhere – it is just too challenging and time consuming to get to them.

Imagine how cool it will be if you can get the most important information and insights before every tactical decision you can make during your workday?

Too much data, insufficient, unreliable metrics

Consider the case of a global consumer goods company we work with. In a large growing market, their sales team has little or no idea how their trade promotions are performing (even though they spend significantly more than $100 million/year).

At a macroscopic level, they do have data on their overall shipments and revenues, but if they want to know whether a specific promotion generated incremental sales or how it performed across different regions, the answer is so excruciating to find that they have stopped asking the question!

There are just too many data sources (from their distributors) that are highly inconsistent as well as complex – and there’s no agreement in defining true incrementality.

In such a situation, they just go with their gut and experience when they decide which promotions to repeat, which to drop and which new ones to introduce. This is the equivalent of driving in an unknown city by asking passersby for directions. It’s time to get a map with real time traffic information and turn by turn directions.

Information overload, too little time

There are other cases where companies have great data but they are still deluged with information overload. A bank we work with expected its senior executives to read an 800-page document, ironically called “at-aglance” report to understand how the bank was doing!

The idea here is this – since we don’t have a clue what’s really important, let’s just get everything together so that senior executives can find whatever they may be looking for. This is like printing out the map of the whole country because you don’t know exactly where you are going.

A bank we work with expected its senior executives to read an 800page document, ironically called “at-a-glance” report to understand how the bank was doing!

BI/Data discovery platforms overpromise, underdeliver

BI & “data discovery” platforms that promise answers aren’t working either. These platforms suffer from GIGO (garbage in garbage out) syndrome and have performance constraints.

In a healthcare company we recently worked with, their report takes forever to load as it attempts to load hundreds of GB of data. Most importantly, these tools don’t even make an attempt to understand their users, like the banking example we discussed above. Whether you are the CEO, Chief Sales Officer, marketing director or finance manager; whether you are in Mexico or look after Western Europe as a region; the reports look more or less the same.

The BI system expects you to learn it and find your own answers and not the other way round. Why is that acceptable?

BI & “data discovery” platforms that promise answers aren’t working either. These platforms suffer from GIGO (garbage in garbage out) syndrome and have performance constraints.

Your Facebook seems to know you quite well, why can’t your BI report understand you likewise and anticipate your questions?

Can AI transform BI?

The answer, I believe, is to bring AI to BI. We need to rethink BI dashboards in light of the advances we are making in AI. Thanks to big data & AI techniques in text analytics, it is easier than ever before to bring together disparate, messy, inconsistent data and fill in the missing gaps. AI algorithms in knowledge representation have made it possible to connect fluid data points into probabilistic but consistent, highly accurate understanding of what’s happening in the business (KPIs, competitive intelligence, etc.).

Most importantly, AI transforms our understanding of the user, helping us serve information, recommendations and insights that the user really needs to know, even before she “wants” to know. That’s when BI truly becomes personalized and “anticipatory”. Additionally, by instrumenting how the user is interacting with these insights/recommendations and acting on them, the AI within BI can learn to be even more relevant, actionable and dare I say, addictive. Eventually, managers and executives will spend more engaged time with their BI than with their Facebook feed. (OK, the last line went too far :-), but I am optimistic).

AI transforms our understanding of the user, helping us serve information, recommendations and insights that the user really needs to know, even before she “wants” to know.

Returning to the example of the consumer goods company, thanks to an AI plus BI solution, the company executives are now beginning to get a clear, in market read of their trade promotion performance. Machine learning algorithms make recommendations to the design team on what promotions to retain, what to drop and predict how a new promotion will perform. The sales team, including the distributor sales representatives will soon have, on their smartphone, information they “need to know”. The National sales director will have real-time understanding of performance of trade promotions and the same app (Cuddle.ai) recommends the right promotion for the right channel to each sales representative.

BI/Data discovery platforms will benefit by embracing this “AI meets BI” thinking to move from data discovery (by the user) to user/insights discovery by the platform

What do you think? Will this be a game changer for your business? Will BI platforms incorporate AI soon enough?

About Author

Srikanth Velamakanni

Co-Founder, Group Chief Executive & Executive Vice-Chairman, Fractal Analytics

Srikanth is a co-founder of Fractal Analytics. In his role as Group Chief Executive & Executive Vice-Chairman, he is responsible for all four entities, inorganic growth and the long-term future of the business.

At Fractal, he has played a role in the evolution of the analytics industry. Long before big data became a buzzword, Fractal evangelized the idea of using advanced analytics and data assets of the company to make better decisions.

Over the last 16 years, he has been a thought partner to global corporations as they have embraced analytics to improve the quality and execution of their decisions. He also believes in building a great place to work that attracts the best minds in the world and creating a trusting environment where people are respected and are free to do creative problem solving.

Srikanth considers himself a lifelong student of mathematics, probability & AI and is interested in consumer behavior, behavioral economics and deep learning.