Managing today's gargantuan volumes of data has its challenges. These include silos, regulations, data, quality, Extract, Transform, Load (ETL), pipelines, storage, and so on. While organizations recognize the importance of investing in data engineering solutions and embark on ambitious data warehousing or data laking platforms, many organizations flounder in the data deluge. The problem is, engineering doesn't have all the answers. Very few organizations can claim to be in command of their data journey. In this podcast, we try to understand why. Raghuram Bhatt gives us an inside view of the challenges and what it really takes to solve them in the banking and financial services industry.

Raghuram S Bhat, Singapore

Raghuram leads Cognizant’s Banking & Financial Services Consulting practice for ASEAN countries and is based in Singapore. He has worked extensively with regional and global financial institutions across multiple markets in APAC, Europe & USA. He has advised clients on topics related to large scale business and technology transformation programs, organization design, IT & digital strategy, business process reengineering and data & analytics. He has more than 18 years of experience and a proven record in C-level client management, consulting sales & delivery, people management, thought leadership and P&L management.

The opinions expressed within this podcast are solely the author's and do not reflect the opinions and beliefs of Cognizant.

[00:02:53] Traditional banks have made significant investments, but very few have succeeded in diffusing and scaling artificial intelligence and analytics technologies throughout the organization.

[00:05:46] Failures are a gold mine of information, but there are no incentives doing a deep dive on the failures in most banking and financial services institutions.

[00:08:33] Several of the clients that I've worked, success has taken longer than anticipated. It happens when data and analytics is not native to your Genesis.

[00:10:38] I follow radical gradualism, which is have a vision, but take small steps towards that direction.

[00:12:38] Failure's not being dramatic. Several platforms that firms have invested tens of millions of dollars, nobody frankly uses.

[00:18:33] The data and analytics strategy is often not owned by one person.

[00:22:45] Data from the new platform does not reconcile with the old one. So using the new numbers from this platform means you having to restate some of your earlier performance numbers and financial reports.

[00:34:40] Be it the art or framing the problem, there's generally a lack of understanding about what and how data can help in running businesses.

[00:37:04] The ambit of engineering is much broader. This is a fundamental capability required to industrialize analytics.

[00:39:24] Have a data lab, but also take the output from the lab and industrialize it.

[00:40:27] The future of finance is underpinned by deep technology and data and analytics is at the core of it.