A business intelligence implementation should act as an impetus rather than a delay in the acquisition of useful...
information. To respond rapidly to this information need, we at Yes Bank realized a while back that an in-house developed BI solution may be the best bet. Here are some pointers on how you can develop an efficient in-house BI solution by leveraging internal resources.
1) Articulate the need: Today, BI is used not only for operational reporting and dash-boards, but also to get profitable returns from an organization’s data. So there needs to be a clear view of what is expected from this data. BI implementation should help to acquire useful information to deliver the desired results.
The requirements of a company vary, depending on its size and scale. These requirements play an important role in BI implementation. The role of data in every organizational function should be carefully analyzed to ensure that the solution meets its requirements. The person in-charge of the function should get precisely what he needs to base his business decisions on, and not be loaded with excess data. If this need does not coincide with the provided data, the BI implementation should lend itself to minute scrutiny of the issue. Here, development of an in-house BI can provide greater value than having a generic platform.
2) Define short, medium and long-term goals: Technology should be driven by business. Based on existing demand, your BI implementation should be quick enough to equip the business to respond to market changes promptly. This does not mean that you can expect plans to deliver in the next three to four months. In Yes Bank’s case, our in-house BI solution has been very agile in responding to business needs. It took us around 18 months to deploy the process, and more than 200 reports were put in place.
The short, medium and long-term goals should be well-defined. It’s recommended to have a short-term need that can be managed in-house and later merged into a long-term goal. These two can also run parallel to each other. The solution should have scope to evolve with business needs. We need to move from point A to point B; for this, an in-house BI solution is what you may need.
3) Measure the return on investment (RoI): We deployed the BI solution at a cost of USD 2,00,000. If the cost of BI implementation is going to overbear the profit to be derived from it, an in-house developed and managed BI does the trick. For example, if a taxi ride does the job for me, why should I buy an expensive car for the same? Thus, measure return on investment (RoI) of the deployment with respect to your in-house capabilities. Industrial knowledge is key to successful BI implementation — not expensive and fancy technology.
4) Evaluate the potential: BI cannot be restricted to only data warehousing. Business intelligence is not just about extraction, cleaning and storing information in a data mart to be produced as per requirement. Delivering information is one part of BI, but putting that information into action is the crucial part. We wanted an action-oriented BI implementation that records action taken on the generated information since it will show as an error or exception otherwise. The level of customization depends on the action to be taken from the BI data. This kind of role-based business intelligence approach can very well be developed in-house.
Define important BI implementation milestones as per your needs. This will help you clearly chart the progress of your in-house development team.
5) Select the right vendor: In-house BI implementation also requires technology partners and hence, it is necessary to thoroughly evaluate vendors for the same. You could choose new vendors or partners you have had a past association with. However, this need not be the cornerstone of BI implementation. Vendor choice should be more of a wisdom pool. It should help you adapt your own solution in such a way that you adapt best practices while avoiding the faux pas of available solutions.
We used the programming platform LogiXML for Yes Bank’s BI implementation. The first phase – Hindsight – involved getting our processes in place. The second phase – Insight – involved system abilities to analyze data. These sufficed our needs at the time.
As we have grown since then, we aim to completing the fourth phase of our BI deployment – Line of Sight — by next year. This phase will provide us with forecasting capabilities through the use of certain templates. The third phase – Foresight – involves systems and advanced analytics that we do not currently need. So we will not implement it.
About the author: Umesh Jain is the President and CIO at Yes Bank. He has over 15 years of experience in banking technology, along with an extensive experience in managing multimillion dollar, strategic global/regional projects and programs.
(As told to Sharon D’Souza)