InetSoft Technology: Financial Business Intelligence

Below is the transcript from a podcast from InetSoft Technology. The speaker is Mark Flaherty, CMO at InetSoft.

What does operational business intelligence mean?

Historically people viewed enterprise data warehousing and BI environments as a resting place for data. Now, operational is added to differentiate those types of business intelligence from a real-time environment that is aimed at driving data and analytics and ad hoc data exploration where in the organization that it needs to be, when it needs to be there. That may be in real-time, or really what is better called near-real-time. It may be to data delivered to event-driven systems and systems that require analytics and/or ad hoc queries.

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Is the trend towards operational business intelligence accelerating?

It is, especially in financial industries like banking with everything from risk analytics to being able to drive real-time decisioning for credit applications for mortgage or retail loans, for instance. A lot of things banks have done in the past have been driven by lines of business or regulatory compliance issues that they were trying to address. For instance the banking industry you have Basel II, for the insurance industry you have Solvency II. These are regulations that really cause infrastructure changes for the banks and the way they do business, the way they manage data, not only for reports but in the way they look at their business and drive their business.

They can look at these regulations from two perspectives. One is the shrug and the deep sigh. I have to comply with yet another regulation. The other way they can look at it is really the way that the regulations were intended, which is really driving best practice and in Basel’s case, risk management, and in Solvency’s case, risk management for the insurance industry. Take a look at those and be able to drive best practices around those so that they can improve their business and generate better outcomes, versus just complying with the regulatory mandate.

How do you go about bringing value to companies from the tremendous amount of data that they have?

There are two key things. One is our approach. Leveraging provisioning practices that we have come up with, which represents a comprehensive approach to data collection. The second piece of data provisioning is around data management. How to manage the quality, how to manage metrics around that data, how to certify the data for specific uses.

What are the biggest challenges to implementing an enterprise data warehouse and business intelligence environment?

The two main challenges we see are not technology oriented at all. There will always be glitches in the technology that get in the way, but it’s not a limiter at all these days. The real issue is more cultural. Being able to look at data, and the line of business’s desire to have their data the way they want it versus the need for enterprise level data manipulation and being able to manage data at that level. The real challenge becomes one that is cultural. And then figuring out how to measure the value of that data. A program around defining what the enterprise has, what they need, and what the value is, so you can manage data as a corporate asset.

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