The American economic slowdown and subsequent global financial crisis were perhaps the biggest economic disruptors

in recent past. Who can ignore the shock waves about Goldman Sachs, Merrill Lynch and twin failures of Freddie Mac and Fannie Mae and the biggest – Lehman Brothers Fall Out? While the financial system seems to have regularized; and a number of economic policies (like the Dodd Frank Act, etc.) are being positioned for growth, the overall economic environment remains highly volatile. Recently I heard the Japanese economy is in recession. The Indian stock market also lost half of its value and the FDI and FII cashed out and took away 12 Billion USD from the Indian market. Organizations worldwide are seeking a trajectory for recovery and success in the post-crisis environment.

A relevant posturing in this milieu is - what can organizations do to help successfully manage uncertainty and complexity to foster growth?

Some answers to these questions will involve the management of information which is also known as Data Management or Business Intelligence. Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information.

BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or from operational data.

It is also described as a Decision Support System (DSS) since the purpose of Business Intelligence is to support better business decision making. If implemented properly and supported by data, the technology exists to mine the offers and consider factors such as demand, location, and temperature and other product requirements, warehouse space and price. Technology also exists to suggest a recommended decision with confidence. Without compounding bad decisions, companies that make informed decisions based on information from their BI systems have healthy bottom lines.

industry chAllenges: PrOBleMs with finAnciAl institutiOns

Financial institutions have been slower to adopt such technology. Enthused to simply get mortgages out the door, financial institutions repeatedly lowered standards. Agents pushed paper in the market knowing well that many of the loans had little chance of being repaid.

Now, the question arises how did such bad mortgages become so prevalent in financial institution? Why these financial houses weren’t motivated to receive the payments? Why did they sell the mortgages for present values that approximate full payment? I think we know the answer. In other words, these institutions were letting others to hold the bag. The problem is, while the clever bankers on the inbound side were writing the mortgages and they were selling them, there had to be buyers and on the outbound side many of the buying

institutions also happened to be the same selling institutions! While toxicity was going out the back door, it was coming in the front door. In BI language we call it lack of data governance and data management.

The complicated packaging of loans that had been subdivided into little pieces elevated the valuation of some pieces to that of some of the better loans in the package. This made it difficult to tell what was actually being bought. You would think prudence would be in order, given that the financial buying institutions were selling in the same fashion, but within the companies, the back room didn’t know what the front room was doing and vice versa, or maybe these financial behemoths had grown so large, huge and important on paper that they believed they could not fail and even if they fail, they would be saved somehow. Some institutions won at this level, and others did not.

whAt cOuld Be dOne next And hOw Business intelligence cOuld helP?

We should realize that information/data/business intelligence has increased visibility and transparency in our financial institutions.

It starts with getting the data together first because if clean data is not captured and integrated, there is nothing valuable for the technology to work on.

In the context of acceptable risk determination, institutions will need to bring data to bear in two areas of the financial business: 1) the business (mortgages) they write and 2) the packages they buy. The mortgages written by the institution will need to be tighter as the buying market for toxic loans dries up. Supported by data, toxic loans will no longer be able to be sold. Institutions

will hold the bag. However, I believe this all starts with the market controlling itself in terms of the packages they buy. Full loan lineage within each package must be made accessible. In BI terms, we call this metadata.

Full visibility into exposure via Information Management/Business Intelligence tools and liquidity is going to be a necessity. Getting the data act together in financial institutions will not be an overnight sensation, but neither was this mess created overnight. With realigned priorities, the financial industry competitive battlefield will move to the information front, where most industries are today. This Data Management, Governance and Data Profiling will all add value to economies and hopefully manage uncertainty and complexities that may arise in the future.

Adding Value with Business Intelligence tecH.toMM.>>

May 20112

Arvind Agarwal Vice President & DWBI Global Practice Head