BFSI VISION | Analytics


Sunder Krishnan
Chief Risk Officer- Reliance Life Insurance and Chairman - ISACA India Growth Task Force

Analytics stdriver srategy likely to succeed when aligned to long term business strategy

Business for Analytics

Vision Clarity
An enterprise desiring to leverage business analytics to achieve its long term goals needs a clear long term strategy. The long term strategy should obviously be a data strategy which is a part of the long term business strategy. Thus the Analytics strategy is likely to succeed only when it is aligned to the long term business strategy. Few businesses may even use business analytics as an enabler rather than a catalyst. Enterprises with a clear initial focus are able to achieve much more when deploying their initial business intelligence solution which speeds up deployment provides a clear and visible "victory" for the team and makes change management easier. Data governance and validation at the source – data entry stages is key. Often all the informative features of a customer are not captured in the customer facing screens / forms and the information that is captured is not well harnessed. I believe research into this aspect would reveal that at least 25% of the information about the customer captured during the initial customer engagement is not adequately harnessed in the systems. On the other hand, an expensive Analytics Solution bought at later stages of an evolved business enterprise is grounded much before the take-off stages. This is due to inadequate data available in the systems. The Enterprise then wonders what is to be done with the Mercedes in the dusty & rough village road. To conclude, to lay a solid foundation (road ahead) and enterprise needs to think through the long term business strategy, data strategy, governance strategy and the Analytics Strategy.

Smart and Flexible
No doubt long term orientation and governance is important, however, being flexible is equally important. Data is like water and flows all over the IT architecture and various application systems. A smart entrepreneur is one who adapts to the changes and deploys powerful application systems that can process even unstructured data. Due to advancements in technology it is no longer expensive to integrate and pull data out of disparate systems.

Iterative Approach
Analytics is, by nature, iterative and business enterprises that have embraced this simple fact by establishing an iterative design and deployment process, achieve greater success than those that attempt a "static and rigid" solution. IT solutions need to be adaptive to changes in processes, technology and personnel (culture). Astonishingly, even personnel could create a major impact; an IT savvy entrepreneur is likely to be more data hungry and spread a culture of smartness in the usage of data that the required strategic and tactical approach is automatically adopted.

Management Commitment
A clear executive sponsor provides more urgency in IT deployment and focuses the project on the business value the sponsor is looking to achieve. Analytics projects can transform a business, but they need to be resourced appropriately to achieve long-term value

The acceptance of Analytics
The key inhibiting factor in the acceptance of Analytics is a good culture and mindset (An Objective and open mindedness). A Business Analytics Project requires long term plan, disciplined approach and more importantly tremendous patience, tenacity and consistency. Technology is never an issue. The culture of many companies severely limits the value from advanced analytics for strategic decisions. Decision makers have to be open to supplementing their existing decision making processes with new kinds of analysis. Decision makers must be willing to engage in new collaborative relationships.

Challenges faced in Big data Analytics
Performance is a major challenge. The root cause for this issue could be inadequate data validations and governance in the enterprise. Once data is compromised in terms of inadequately filtered / validated data, the resultant output would become unreliable and the user community diffident of the data results. Another major challenge is the cost an enterprise would need to treat continuous investments in Analytics as long term commitment and pay back potential. Systems need to continuously available necessitating major investments in disaster recovery planning and Business Continuity Management. Thus, an Analytics Strategy is not a silo based activity and calls for a cohesive strategy that is collaborative with multitude of functions and disciplines. As integrity and availability of data is of paramount importance, one needs to ensure that data is confidential and secure. In a large scale enterprise there are bound to be disparate systems, processes and silo based functions. This would cause data diversity and manageability issues. However, a well thought data analytics strategy could manage these challenges well. Costs benefit analysis therefore would need to be long term oriented and measured.

"The views expressed by the expert is purely personal and are not the views of his employer or the organisations that he represents."