Data Management for Financial Services

Raghavendra (Raghu) Chinhalli, VP, Digital Transformation & Solutions Delivery Services, Ocwen Financial Solutions Pvt. Ltd.

Raghavendra (Raghu) Chinhalli, VP, Digital Transformation & Solutions Delivery Services, Ocwen Financial Solutions Pvt. Ltd.

Data is the new oil. This statement in itself elevates the Data (Management) office to one of strategic significance. Financial Services has certain peculiarities similar to if not more than comparable industries like a few key highlights listed below:

a) is a highly regulated industry which has multiple layers of compliance, legal & regulatory requirements to risk manage

Comply at all costs the Federal (US) / National and State regulations: Build a conducive organizational structure with three lines of defense i.e. operational business risk management, risk & compliance & Internal Audit. These departments are empowered with easy to use cross-functional data that is appropriately labelled for self-service & automated queries to validate data across data silos.

b) has data privacy protection as a non-negotiable requirement

Formal top down centralized Privacy program: At the heart of the Privacy program would be the Data Classification which would further support the data tenets of a sound Privacy program such as collection, storage, processing, retention, & disclosure of data.

c) ensures data security throughout the data journey within & outside the organization

Sound encryption, user access, backup & restore policies: Data variety i.e. structured, unstructured needs to be dealt with from an information security lens to ensure high levels of data security to prevent theft, unauthorized access, and corruption throughout the lifecycle of the data.

d) has customer journeys that are getting simplified with omni-channel platforms; these real time interactions mean low tolerance to data integrity issues

Tight coupling of Data Management to Customer Experience programs: Data leaders need to be key stakeholders in Enterprise Architecture designs thus ensuring robust solutioning to critical client interactions via portal, mobile, text, chat & voice channels. Semantic data layers with common API / middleware is a key enabler to a more simplified, standardized & consistent customer experience.

e) requires Analytics as a prime differentiator in the revenue model thus demanding cross-functional Big Data availability in a data lake

Manage Data explosion to make it a competitive advantage: Organizational data deluge could be due to various reasons e.g. newer / more automated Point Of Sale integration, or integrating broader market data for analytics. Remember tons of your own and competitor data is somewhere available for insights and analytics. It’s a competitive advantage to tap into this data & benefit.

f) has as table stakes mature real time Business Intelligence (BI) reporting, Data Governance & Data Quality programs

Traditional Data Management practices are now table stakes: Mature the practice in areas of BI & self-serve reporting. Today's BI tools provide not only more advanced reporting but also provide embedded Analytics capabilities. The challenge here is more of the skill of competency to view the data & insights to create a competitive advantage to the Enterprise. Data Governance is yet another vital pillar in a DM organization.

Having well-defined data owners, stewards & custodians ensures successful cross-organization partnerships in managing the Data Assets. Finally, Data Quality based Key Metrics must be ubiquitous across all strategic business reviews including the Board reporting.

g) need to scale rapidly to facilitate some happening & futuristic tectonic shifts such as AI/ML

Stay hungry for disruptors & ride the wave at the earliest opportunity: AI/ML places huge demands on the data sets both internal & external. Having a data foundation framework that will scale to newer disruptions will ensure market differentiator advantage, thus propelling the Enterprise to market leadership positions.

h) need to monetize data to create value

Transform Data to Intelligence and make it a strategic asset: When Enterprise derives Intelligence out of its data it has applied a force multiplier to its strategic asset. This intelligence could be used to better position your products in the market, better understand your customer behaviors, better manage vendor costs & better price your offerings. Of course, the same intelligence derived out of data can streamline operations & optimize costs for the Enterprise.

i) need to acquire & nurture talent and while at it set diversity & inclusion targets in hiring

People, people & people: A Data Management practice needs to be dynamic, fungible & cutting edge. This is only possible if the Enterprise invests in the acquisition & retention of the skilled workforce. Data Management principles are now taught in colleges & a plethora of organizations as well as certifications have come up to support the discipline. Tap into them & ensure you create an adequate runway for hiring diverse talent which definitely provides outcome multiplier effects.

In conclusion, Data Management for the Financial Services is a challenging & happening area where many career progression can be created. Data is only exploding everywhere & this trend will only continue to grow exponentially. This further places huge expectations & opportunities in the use of this data. My hope is that passionate Data professionals will continue to inspire more workers into embracing this evergreen Enterprise function.