A Data-Driven Approach To Banking

Today’s banking landscape is driven by data. Insights derived from data can help businesses focus resources such as people and money on key areas.

We have captured data, extracted it, transformed it, consolidated it, and stored it with extensive sets of data from various outside sources. However, how many different software do financial institutions use to complete this process from start to finish? “

The question of extracting, transforming, consolidating, and storing data varies across the size of the institutions.

Larger Institutions:

For large banks and financial institutions, their data journey has started 20 years ago with Mainframe, then Oracle, and eventually transitioned to the traditional data warehousing concept. The introduction of the data lake and several other new technologies has come in the last 10 years. Today, firms have the option to utilize cloud service providers, enabling access to various data services without manual intervention.

Commonly Observable Trends in Large Financial Institutions:

1. Large financial institutions have introduced Chief Data Officer as a role whose sole responsibility is to ensure the business’ data is used utilized for maximum benefit in addition to oversight of compliance, governance, etc.
2. As a data-centric organization, we have seen various challenges from people’s standpoint on the data site, where the roles have been changing every four or five years. They need to have a strategic view of the data based on customers use cases across the industry.
3. At times, they do not have control over technology, and sometimes the strategy, business, focus, technology and the role of data or ideas are not in line which is not enough.

• The Implementations / Possibilities:

• Several large financial institutions are doing the job of enabling new technology.
• There is a lot of local software available that can be used to accelerate the data journeys.
• There is a lot to do from people’s standpoint, especially the chief data officer role. The whole game of what responsibility is to be given to chief officers, as well as, from a process standpoint, automating standardizing and overall making it better.

• For Smaller Banks The Issue Is Different:

 Small banks have started surfacing data. They are using data tremendously for a customer-centric approach and building relationships with customers by having foresight on business strategy.

There are a lot of things that small banks are implementing:

1. They recognize that implementing a data-driven approach is necessary.
2. Smaller institutions are also thinking traditionally. They’re starting to build a data warehouse but not with a business focus, and we should be talking more on this aspect as to how to start this journey. What should be the focus? And so on.

Some institutions are doing enough, but there’s a lot more to do.

• How Can Organizations Get Started With a Data-Driven Approach?

The main reasons why most organizations start are because their competitors are doing it, or because someone else is doing it which is the wrong way to start. Our focus should be: what are my challenges? What are my business goals? Can data be an answer to achieving those goals and solving these problems? Once you start that, then you could have a strategic view.

A lot of data strategies or data plans are failing because organizations are just building massive data warehouses.

1. To start with, have a business case. Have a broad strategy. Along with that, have challenges around all these, then have a strategy and your goals around how you want to build.
2. An advice to all the institutions is to look out for the new stack. If you go the traditional way, you are starting actually with old technologies. It is as if laying cable lines while mobile phones exist. There might be a requirement in some exceptional cases, but otherwise, you want to adopt or put most of your data in the new data and start with consumption in mind.

Make your data strategy broader and take advantage of the latest and greatest data technologies that are considerably automated, and less reliant on developers.

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