InetSoft Webinar: The Problem with the Standard Business Intelligence Model

This is the continuation of the transcript of a Webinar hosted by InetSoft in May 2018 on the topic of "How Data Discovery Software Uncovers Stories in the Data." The speaker is Abhishek Gupta, Product Manager at InetSoft.

The problem with the standard business intelligence model is there are two other layers in here, performance reporting and discovery and analysis which aren’t well served by operational reporting and advance analytics. And so the problem is that this causes what we can call the cycle of pain. What we often see at our clients is custom report requests. End-users submit requests to IT, and it ends up in the backlog that may take five sometimes 10, 15 days to get an answer.

The answer comes back and begs another question so it goes back through the cycle which causes frustration on both sides. Here’s an example from a recent account who was doing planning for a fund raising event in New York for 130 people. You make a request to find alumni who live in New York, who have given over $75,000 and express and interest in financial aid. You get a list of 15 people.

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It's too little, so you’ll beg, adjust your request and say, well, let’s look at everybody who’s given over $10,000.00. Now you’re up with 600 people, and you start iterating this, at some point you’ve got to invite people to the events so you just send out against the list whatever you think before you get the data back. That’s sort of the problem there.

The other side of this is Excel where teams will download extracts out of the core systems and slice and dice them in excel. It’s a time consuming and challenging process. You don’t get all the data because you start by getting one part of one table and realize later it’s not all that you need. It’s hard to show management because it's is a spreadsheet. Another problem is it creates shadow data systems because people then start modifying what's in the spreadsheet and it sort of creates its own life.

So one of our visions and missions is to break the cycle of pain, and the new technology that we and others have adopted to do that is data visualization. We’ll talk about that. Data visualization helps you see the data. This helps you get access to the data faster, better and more flexibly. This helps you understand the data better because you can see it. And predictive analytics applies mathematical modeling to help you understand patterns in it.

And when you apply these facts in an organization, it's not one size fits all. Operational reporting can span the organization. It is actually frontline reports for operational people. There are top line reports, performance scorecards and dashboards. They tend to focus on the top because they are summary related. Data Discovery and Analysis, as you see, spans the organization. It has got a wide range of users but is fundamentally different than these others, too. And then advanced analytics tend to be a capability for power users business analysts. So one model here in business intelligence is you need a mosaic of these things to fit the needs of your organization, and it's not a monolithic one size fits all.

And we would argue that the staff’s tactical decisions are critically important because the bottom of pyramid is more people. They make more decisions. They do it more often. Strategies are implemented through tactics. So making the right tactical decisions everyday leads to the success of the strategy. But this support for the staff has been largely underfunded. Why?

It’s because the management strategy gets the focus. They’re at the top, and they are the ones with the vision. They want a clean summary, not tactical detail. So the focus has been here, and then there is a tendency to leverage the legacy systems to solve these problems. We’ve heard many times the reporting systems will be ready in six months, and 18 months later the warehouse is starting to come together and starting to work.

There is a lot of fear of information democracy, also known as The Unknown. If you enable the end-users to work with the data to make their own decisions, it's enabling them to make more decisions, but some of them may not be right. So you’ve got that balance we would argue it's better to make 10 fact based decisions where you get eight of them right rather than only using data for fact based decisions on two of your ten decisions. You are better off pushing it out there enabling people and letting the results prove out.


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