Three reasons banks shouldn’t go it alone on data lake deployments

Published: September 19, 2019

On the slopes, a "ten-eighty" means three 360 spins. For banks, a 1080 is arguably something even harder to pull off: an innovative view of the customer that delivers deeper and more accurate insights into unique needs and preferences than ever before.

Future-facing banks know that delivering on growing customer expectations around service quality will be crucial to foster loyalty in the wave of FinTech disruption ahead. By combining three 360-degree views of the customer from the front-office, back-office and external systems such as social media, banks will be equipped for responsive, tailored services.

Gaining this 1080-view requires banks to ethically and efficiently leverage massive volumes of data from multiple locations. As a result, many banks are now preparing for significant investments in enterprise data lake deployments.

Some banks will be tempted to go it alone and deploy a data lake themselves. But there are three key reasons why that is a very risky proposition.

  • Updating legacy, mission-critical infrastructures to support a cutting-edge analytics platform demands in-depth planning and careful support. This will consume a significant amount of time for top talent, who may be needed in other critical business innovation areas.
  • Enterprise data lake deployments require massive amounts of resources for what is, effectively, a one-off effort. Gathering the necessary skills and expertise is in itself a major effort, and spinning down the team at the end of such a long project will inevitably have a negative impact on motivation and morale.
  • Internal teams are unlikely to have much experience with such large, specific, and complex IT deployments. As a result, it is easy for technical leaders to fixate on narrow goals at the expense of the bigger picture: preventing the bank from benefiting from industry standardization and best practices.

A new Virtusa white paper, "The Total Customer View: Old History or New Idea?", lays out a route to the 1080-view that helps banks minimize risk, contain costs and cut time-to-value.

To achieve all these objectives, the paper argues that it is imperative for banks to engage a digital engineering partner with deep industry experience. By offering the ability to ramp up teams quickly, maintain currency in the latest platforms and technologies, and deliver the assurance of best practices, the partnership approach offers banks a model that has propelled some of today's largest technology and FinTech companies to market-leading positions.

The paper's author, Arvind Purushothaman, Head of Analytics, Insights and Data practice, Virtusa, is no stranger to the world of FinTechs. As a techno-business executive for business units with multi-million-pound revenue targets, Arvind Purushothaman has helped establish and scale up programs that deliver proven, recurring returns.

If your organization is preparing to embark on an enterprise data lake deployment, you'll know that careful planning is crucial. Opting to go it alone on a large-scale infrastructure modernization project is a strategy that could increase your exposure to risk, drive your costs and lengthen your time-to-market. To learn more, click here to read the Virtusa white paper now.

Data Quality Checks (DQC) Framework

Unlock cost-friendly and unrestricted data quality checking

Related content