6 Best Practices for Successful Business Intelligence
February 11, 2008 | Leave a Comment
Business intelligence (BI) is not about technology. No doubt there is much technology involved, but a sound business intelligence strategy concentrates more on methods. The outcome of intelligence gleaned from a strategic reporting or decision support system should be an action or decision. The decisions made from business intelligence will likely lead to changes made in strategy and/or individual business process.
These changes in strategy and business processes will necessitate changes to one’s enterprise resource planning (ERP) or other transactional systems. For example, new business rules or codes may need to be added to the ERP in order to operationalize a decision that was made. This will require an understanding of the business rules engine of the ERP as well as the implications from a historical measurement perspective of changing or adding codes to the system. People will need to be trained and constituents may need to be informed of new rules.
BI is much more about organizational alignment or people and processes around a common set of strategies and goals then it is about technology. To that end, follow these 6 best practices to move your organization from silo-based planning to one that is aligned around a culture of evidence:
- Define areas for exploration – What subject areas need to be studied? Not all can be effectively studied at one time – not at the start – therefore, you will need to prioritize. The organizations leaders will need to set the priorities based on the key strategies that need to be affected.
- Articulate problem statements – Now you have identified the subject areas to be studied. What are the problems in that area? Simple year-over-year trend analysis will often highlight where the problems are lurking. The problem should be stated as follows: [Subject Area] is down by 15% compared to last year.
- Identify causal factors – Perhaps one of the most overlooked steps in the process. Your problem statements only tell you what is happening. It is critical that you find out why it is happening. Statistical models need to be employed to determine the key drivers influencing the problem area. Identification of the key drivers in the area will help you isolate the problem and determine the factors causing the problem.
- Determine corrective action – Once the causal factors have been identified decisions can be made and corrective action taken. True evidence-based decision making.
- Align people, process, and technology – Decisions inevitably lead to change. Most often the change comes in the form of a new business process. The new business process will need to be codified in the ERP system and people may need to be reorganized and/or retrained. This will be the hardest step in the process toward a culture of evidence, yet it is also the most critical.
- Measure outcomes – Now that people, process, and technology has been aligned to solve the problem, you must measure the effectiveness of this action. Be careful that you allow a sufficient amount of time for the impact of the change. In fact, decision makers should agree about the length of time they will permit for the action to take hold and pre-determine a point in time for re-evaluation.
As you can see from the diagram above, the process is cyclical. As decisions are made and corrective action is taken, key drivers will change. This will constantly cause the organization to reevaluate and revisit its strategies and tactics over the course of time.
Fair Lending Another Casualty of the Subprime Meltdown?
January 17, 2008 | 1 Comment
You’d have to do a lot to avoid discussion of the subprime mess and the effects that it is alleged to be having on the housing and financial markets (although we think there are more reasons for optimism than many, but more on that in another article). One area where we do have concerns is in the effect of the subprime cleanup on fair lending.
Fair lending compliance requires that regulated entitites (which includes essentially all retail mortgage lending institutions) provide government regulators specific facts on the mortgage applications they did and did not approve. These data are then examined statistically to determine if there is an observable pattern of discrimination in underwriting practices based on the treatment of members of protected groups.
In a recent article, the Washington Post describes the actions that lenders are taking to correct the underwriting excesses of the subprime boom. While the industry experts offer somewhat differing accounts of actions that are expected to be taken, all agreed that credit scores are going to lose some of their weight in the underwriting process, at least in the near term. To quote from the article,
But income matters now, and so does cash, said Sean O’Boyle, a vice president at SunTrust Mortgage in Chevy Chase. Lenders expect borrowers to have several months’ worth of mortgage payments in reserve and a steady job. ‘Job stability. Credit. Cash,’ O’Boyle said. ‘They’re all equally important. Not one of them overshadows the other.’
Unfortunately, moving to a broader set of measures of creditworthiness, while intended as a means of tightening underwriting, could achieve just the opposite, and cause fair lending compliance issues to boot. Remember that the pressure to maintain and increase loan volumes fueled the use of exotic mortgages to increase the number of eligible borrowers. In the same way there will be pressure to use additional information to cherry pick borrowers with borderline credit scores.
Here’s where fair lending compliance comes in – cherry picking borrowers based on any information not included in the compliance data provided to regulators introduces the risk that correlations will be found between protected group status and the probability of receiving a loan, even if no mortgage discrimination was ever intended. Is the value of cherry picking worth the potential compliance issues it might cause? We don’t think so.
Here’s a much less problematic solution to tightening underwriting requirements – demand higher credit scores from all borrowers and stick close to the information reported for fair lending compliance when making the underwriting decision. This would have none of the potential downside of including non-reported information and would require few changes to underwriting processes. It will be interesting to see if the pressure to maintain loan volumes or the need to assure regulatory compliance wins out.
ASR selected to assist with SAS Fair Banking implementation at major bank
December 15, 2007 | Leave a Comment
ASR has been selected to assist with the implementation of the SAS Fair Banking solution at a very large nationwide mortgage lender. ASR was selected for this assignment based on past success implementing the SAS Fair Banking solution at a midsized morgage lender earlier this year. This will be one of the first implementations of latest version of SAS Fair Banking (version 8). The SAS Fair Banking solution provides a complete set of tools for self-assessing compliance with the Home Mortgage Disclosure Act (HMDA) and the Community Reinvestment Act (CRA). SAS Fair Banking enables mortgage lending institutions to effectively and efficiently collect, validate, store, isolate, aggregate, and report on loan data and loan application data. This enables financial institutions to better understand how their organization is performing based on the key compliance concerns behind HMDA and CRA. As a result, the institutions are able to meet the rigorous HMDA and CRA filing requirements with greater accuracy and timeliness. ASR will be working as a subcontractor to Zencos Consulting LLC and SAS on this engagement.
ASR impelements SAS Fair Banking Solution for mid-sized mortgage lender
May 26, 2007 | Leave a Comment
ASR Analytics performed the first configuration of the SAS Fair Banking Solution at a mid-sized mortgage lender. This configuration included the development of custom filters to flag loans with potential problems as well as implementation of all of the standard data management and reporting features of the SAS Fair Banking Solution. The solution was completely installed in the development environment within 90 days of project start.
ASR to develop wireless PDA business intelligence application for a large commercial vehicle manufacturer
January 26, 2007 | Leave a Comment
ASR Analytics is awarded a contract with a Large Commercial Vehicle Manufacturer to build a wireless PDA business intelligence application. Development of the application used SAS Integration Technologies, including Metadata Server authentication and authorization, SAS Stored Processes, and HTML forms. The application provides real-time query capability to surface vehicle supply and demand databases, create email alerts, and reports on current inventory through a handheld PDA.
ASR teams with Open Blue Solutions, Inc. to research entrepreneurs for the Small Business Administration Office of Advocacy
December 26, 2005 | Leave a Comment
Together with Open Blue Solutions, Inc. (OBS), ASR is awarded a contract from the Small Business Administration Office of Advocacy. The Small Business Administration Office of Advocacy commissioned this study to help the Administration and Congress to better address key aspects of this Nation’s policy towards veteran entrepreneurship. This project uses the Current Population Survey (CPS) to examine cross-sectional statistics regarding the population of veterans, and service-disabled veterans in the United States. As a subcontractor to Open Blue Solutions, Inc. (OBS), a small business owned and operated by service-disabled veterans, ASR Analytics is responsible for designing and implementing research into the characteristics of veterans who are self-employed entrepreneurs.