How “Business Intelligence” is Failing you

by | Jul 12, 2016 | Big Data, Business Intelligence (BI), Data Analysis, General BI and Data Management


“Business Intelligence.”

You’ve used the term in countless meetings. It started to gain momentum in the early 2000’s and has remained hot ever since.

The term clocked in at a 98 in Google Trends as recently as April of 2016 (the max is 100).

Your boss (and your boss’s boss) fell in love with beautiful data visualizations from Business Intelligence solutions like Tableau, Domo, Pentaho, and others. “Why haven’t our financial reports always looked this great??” they said.

Those solutions have been riding the wave, rolling out new ways to visualize big data, and capturing the hearts of financial executives everywhere.

So why in the world would we consider throwing it out?

Because it’s actually holding your team back.

How Today’s Business Intelligence Solutions Fall Short

Don’t get me wrong. Business Intelligence is here to stay. Today’s BI solutions just aren’t delivering on the promise of turning big data into intelligence.

Over the past few years, BI has become synonymous with “data visualizations.” We’ve stopped short of the “intelligence” part.

According to a recent SunGard Consulting Services study, only 13% of respondents go beyond visualization and use real business intelligence techniques like predictive analytics and alerts.

Thirteen percent!

Why is that number so low? Because today’s BI tools lack advanced modeling that helps you project forward. They still rely on the business analysts to extract the data, clean it, manually manipulate it in spreadsheets, and then push the data back into static PDFs or online dashboards. No wonder it’s only 13% – that’s a lot of work.

If a BI solution requires the analyst to do so much data prep, then they spend less time doing the part of their job that creates real value – the data analyzing.

But how much of your analyst’s time is really spent prepping data?

According to a recent study from Blue Hill Research, analysts spend an average of 28% of their time on data prep. When you layer in the average analyst’s salary, and an average team of 50 analysts, that’s over $1,000,000 going to data prep!

Rather than making the analyst work so hard at prepping data, why not give them a more complete BI solution that takes care of all the pulling, pushing, and manipulating of data for them? In doing so, you might just save a million dollars.

What Business Intelligence Should Mean

What it comes down to is visualizing vs forecasting. Looking at the pastor predicting the future.



That’s the true essence of business intelligence. Analytics models that use live data and predictive forecasting. Those are the ones that enable you to make smarter business decisions.

The ideal BI solution is something that encompasses both past and future. It should hold all your data together, directly from the database;it should enable analysts to apply calculations and business logic to it; analysts should be able to quickly and easily create scenarios that apply their models to the live data set; and it should produce the beautiful on-the-fly reports and visualizations that we’ve come to expect from today’s BI solutions.

BI tools should let your analysts spend time on what they do best – analyze, plan, optimize – instead of spending weeks manually extracting, manipulating, and reporting data just to get to the point where they can start their analysis.

If your BI tool only looks at historical data and doesn’t add that layer of predictive forecasting, then it’s forcing your highly-paid analysts to waste >a huge amount of time.

And is that intelligent for your business?