Accelerating Data Initiatives for the Banking & Finance Industry

With the rapid growth of FinTech1 organizations, the banking and finance sector are chomping at the bit to figure out a way to accelerate their data initiatives.

A vast majority of consumers, especially younger ones, are increasingly seeking out ways to accomplish their financial needs it a more expediated manner via digital platforms. Because of this, it is imperative that banking & financial institutions accelerate their data initiatives so that they can evolve from traditional brick and mortar institutions into future ready organizations.

But how do these organizations streamline such a massive undertaking? In our experience, the best approach is to focus initially on a specific department that has the greatest need or would experience the greatest impact with the least amount of effort. This department could be used as a Proof of Concept (POC) to identify a data strategy that supports business goals and aligns with organizational requirements. A successful POC should incorporate a data governance framework and select the appropriate technology to support the initiative while working with key stakeholders to create a plan of action that will allow for a smooth implementation. Let’s take a closer look at each of these areas so that you get a better understanding.

Modest Approach

Often times when data initiative fail it’s because inexperienced data implementers believe this undertaking can be applied to an entire organization all at once. While this ambitious thinking is commendable, it isn’t really logical or feasible, especially with larger organizations. Our experience has taught us that, initially, a more modest approach is best. By building a leaner project focused on a specific department with greater data needs you can construct a more focused game plan that is repeatable, reusable, and sustainable so that when the initial project is complete it can quickly be deployed to the other departments within the organization. For this article, let’s say that a bank has identified their accounts payable department looking to automate day-to-day tasks like receivables and payments so they can devote more resources to high value activities.

Now that a specific business unit has been selected, the project team can begin determining business goals as well as guidelines on how they will support the achievement of those goals. These will serve as the foundation for which the data strategy will be built upon. Sticking with the accounts payable example, we have already determined that the department would like to improve operational efficiency and a sure-fire way to support that is by working to reduce or eliminate manual processes through automation. Another goal might be to minimize compliance risk and spending by improving data security and accuracy of forecasting. By creating goals and the support capabilities at the onset, the data strategy is focused and keeps the project team on task, all of which help speed up the overall initiative.

Setting the Strategy

As goals are defined, a strategy can now be determined and put in place. An effective strategy identifies issues, solve for roadblocks, and gives explicit direction on how to execute on the plan. An important aspect of the strategy is to make sure that it aligns people, process, data, and technology. Often, data initiatives are seen as solely an IT responsibility, when in fact they should promote collaboration between business groups and IT. Only when these groups begin to work together can the data initiative see positive results faster.

Governance Framework & Technology

An often-overlooked aspect of a data initiative is having a proper data governance framework in place. Data Governance allows the business unit to have a clear understanding of data ownership, data definitions, and permissions and data privileges. By having effective governance in place, organizations can avoid data silos and data fragmentation giving them the ability to have a single source of truth. At the same time, technology being considered should support the data initiative. Continuing with our accounts payable example, the technology should have the ability to work with a smaller scale at first and then be able to scale up as it is rolled out to the other departments of the organization. It should also consider the required competency of the users, the amount of capital expenditure required and the length of time to implement the technology.

Smooth Implementation

To make sure the implementation is done in a timely and effective manner you need to pay attention to several key areas. At Data Ideology, our philosophy is to start with smaller use cases that can be expanded to bigger ones, provide leadership with POCs of the success of the smaller use cases for buy-in, build strong data governance in order to ensure data integrity and quality, get business intelligence into the hands of those who need it on a real-time basis, and be deliberate when expanding sources of data.

As you can see, introducing data initiatives into banking and financial institutions can be a labor-intensive process, however, it is evidently clear that it is a necessary means to keep pace with competitors like FinTech entities. We have helped organizations within the finance sector with accelerated data initiatives by focusing data strategies to start at a smaller scale with the built-in ability to expand. By working on a specific business unit, initially, the project teams make themselves more agile and can address issues and make decisions on a micro level that can also be applied on a macro level when the time calls for it.

Written by Mike Sargo


Co-Founder & Chief Data and Analytics Officer at Data Ideology


Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.

Healthcare

Pitfalls to Avoid when implementing Health Information Exchange

In recent years, Health Information Exchange (HIE) capabilities have helped mobilize patient data allowing clinicians and healthcare managers to provide near real time diagnosis closing the gap on member care.
Strategy

Scrum Master Best Practices to Accelerate Data Projects

Scrum is a Delivery Framework, under the Agile umbrella, that can accelerate initiatives, breakdown data silos, and bring organizations together as a team to progress any project. Individuals certified in Scrum are known as Scrum Masters.
Healthcare

Surprise! Payers’ Data Struggles with the No Surprises Act

In late December of 2020, bi-partisan legislation known as the No Surprises Act was passed by the U.S. government. The main purpose of the bill is to eliminate surprise medical costs for out-of-network services.