Enhance revenue using data as an asset
For about a decade, revenues from transactional businesses have been on the decline. Financial institutions are compelled to offer freebies to their clients to remain in competition with fintech and disruptors. Similarly, online retailers must operate at very low margins to entice their customers in a competitive landscape. Nearly every market sector has been impacted by such industry changes and has been looking for new avenues and innovative ways to expand its businesses.
By the mid-2000s, data emerged as the new gold, and ever since, enterprises have been thinking of ways to harness data and analytics to derive meaningful insights into their businesses. As legacy technologies were found incapable of dealing with the explosion of data, new technologies emerged as a part of the "BigData" revolution. "Big" is the norm now – hence the term "BigData," or the associated hype, has fallen out of grace. As cloud-based data warehouses, data lakes, and AI/ML technologies matured in the last few years, enterprises are now considering exposing and selling their data in a marketplace.
Many enterprises now view their transactional OLTP (online transactional processing) systems as an area of "keeping lights on," while some focus on building a better digital experience. However, nearly all enterprises have started their journey on cloud and analytical data, combining transactional data, market data, social feeds, demographic and environmental data from unstructured sources, and domain-specific data in the marketplace.
Technology is not viewed as a challenge anymore, as mentioned. The biggest impediment for Financial Institutions (FI) has been getting around legal and compliance restrictions for data sharing. FIs had to seek explicit permissions from each of their counterparts or parties involved, whose data may be exposed in some form, even if aggregated and anonymized. But over the last few years, this requirement has been significantly eased.
There have been several other challenges, including the ability to answer questions such as; 'Are we ready to share the data we have?' 'Do we have the right data quality?' 'Will the data be useful to my clients or other stakeholders?' Many FIs are still struggling to have the right data in their data warehouses or lakes and have yet to attain the maturity data consumption calls for.
Lastly, they are also faced with the concern that it might lead to exposing their IP on the subject, and competitors may gain some vital insights and learnings from what they see.
A likely candidate is any data of interest to stakeholders in the supply chain, including the suppliers and consumers of the company's products or services.
For example, a wealth management company may expose data about the usage of specific funds or securities in various portfolios in custody, with analytics around the institutions or individuals who own the portfolios. An online retailer may provide analytics around the popularity of products supplied by manufacturers, such that they can view their competitors' data. A mortgage and securities lender could share market offerings data sliced across consumer classes, demographics, or analytics based on credit scores. Trade finance lends to immense opportunities across the supply chain, while rating providers may expose data around global counterparty credit risk.
The use cases are too varied to list here, but the business divisions at an enterprise usually maintain a clear view of what can be exposed and to whom. Also, there can be multiple offerings; competitors may be given access to only a specific data category.
After the data has been made available in a marketplace (typically in a modern data warehouse), it is shared "in-place" from where it is stored and exposed through interfaces such as Direct Database Queries or APIs in multiple flavors, such as RESTful or GraphQL. The concept of "making replicas" of data for distribution is now obsolete, driving lower costs and lesser woes of keeping the replicas in sync. Cloud scalability, enhanced concurrency of access, and modern security features are further aided by sharing. It is also simple to share links from a cloud reporting tool (such as PowerBI or Tableau) – for end-user analytics. Data masking, encryption, managing entitlements and access, authentication, and attribute/rule-based control – are essential features of the underlying platform that need to be worked out.
Once the technologies and interfaces are worked out, there can be a free or freemium service to render certain parts of data, reports, and dashboards. The offering can be monetized using several models, such as fixed monthly charges, per API usage charge or frequency of usage, number of reports used, etc.
Virtusa offers expertise in the financial domain to help decide what parts of data owned by an enterprise may be shared with its clients and other stakeholders. The data practice at Virtusa can set up a data marketplace on a data warehouse or data lake of choice and expose interfaces that allow consumers to retrieve data at scale while addressing security, authentication, entitlements, and other concerns. Virtusa can also set up reports and dashboards using a visualization tool of choice, with the links shared with the consumers.
Surajit Mitra, Virtusa's VP of technology, leads the data practice for banking and financial services clients. Focusing on data and cloud adoption, he has spearheaded modernization and digital transformation programs at several global corporations. He has extensive experience with data warehouses, lakes, and a variety of services on AWS, Azure, and GCP. Surajit is an avid night and weekend coder and a part-time payments and fintech blogger.
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