Data Analytics

How Financial Firms Can Build Better Data Strategies

Discover the three components of a smart data strategy, and how to get them.

Rajiv Jain is a Field CIO/CTO for the Financial Services Vertical at CDW.

Rex Washburn is a data solution architect and the head of Modern Data Platforms at CDW.

Financial services organizations are built around their data. They must be able to quickly access, analyze and use the data they have while ensuring compliance with cybersecurity best practices and regulations. Data is at the heart of any organization’s digital transformation efforts.

But the industry suffers from a widespread data strategy problem. According to research from Mosaic Smart Data, for example, 66% of investment banks struggle with data quality and integrity, and 83% do not have access to real-time data or data analytics.

Sadly, this is unsurprising, and it’s a challenge that affects the entire sector, not just investment banks. So, let’s break down the basics of data strategies in financial services, explore key components for success and offer actionable tips to help organizations make objective improvements to their current operations.

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What Is a Data Strategy in Financial Services?

An organization’s data strategy defines the tools, processes and rules that govern the storage, analysis and use of business data.

For financial organizations, data strategies are often driven by CISOs and tend to focus on data protection and security. This enables regulatory and operational compliance by ensuring the right people can access the right data at the right time while still aligning to the corporate security and risk stance.

But this now comes within the context of the emergence of artificial intelligence and the growing sense that firms need to leverage it to gain a competitive edge. For example, loan data can be made more valuable with AI-driven analytics. Or banks can use AI tools to identify patterns related to fraud or compliance challenges to help them avoid potential regulatory pitfalls.

However, firms must have their data organized and governed in the right way to take advantage of such tools — and most don’t.

Three Things All Effective Data Strategies Include

A successful data strategy includes three components.

A modern data platform: Companies need cloudlike functionality with scalability to handle different kinds of data, and no single vendor can do everything. Instead, firms need to build out modern data platforms using multiple trusted vendors that enable anonymized data collection so that information is available for collaboration and analysis.
Effective data governance: Banks must be able to trust the data they have to provide accurate answers. This requires effective data governance, which refers to establishing clear rules about the access and use of data, as well as the enforcement of those rules. One of the biggest advantages of having a modern data platform in place, in fact, is that such platforms support the establishment of good governance.
Internal data culture: In a world where people often are not trained in data and rely on the first page of Google results, staff are similarly inclined to believe the output of large language models and generative AI tools without a critical eye to the quality of the output. Organizations with a good data culture prioritize education and collaboration to help staff use tools effectively and identify potential data issues before they become big problems.

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How to Build a Better Data Strategy

Data strategies aren’t static. They’re sets of dynamic digital processes that evolve as business requirements change. Here are three tips to help leaders build better strategies.

Keep things simple. As cloud- and AI-enabled solutions expand in number and variety, many firms embark on strategies that rely on buying and implementing “cool” new tools. While these tools can help enable effective strategies, they can’t do all the heavy lifting. Better strategies start small and keep things simple. Businesses benefit from testing strategies in focused use cases and scaling up over time.
Find the why. New tools often seem like the answer to every data challenge, but the right tools build on what’s already in place. To ensure that new solutions deliver a strategic advantage, leaders must ask simple questions: What’s the business need? Why am I pursuing this approach or tool? This requires an honest assessment of current data conditions, business needs and coworker skill sets. While many businesses are fully compliant and meet regulations, their data may not be highly active or in great shape. Identifying issues lets leaders define business needs.
Get outside help. Building a data strategy in isolation is challenging. With financial IT teams focused on securing data, improving the end-user experience and navigating the evolving technology landscape, developing a data strategy is often pushed to the back burner. But by working with a trusted technology expert, firms get full lifecycle support to design, build and manage data strategies that deliver immediate results and enable long-term value.

For financial services firms, data strategies do more than just protect data. In tandem with AI, these strategies can help streamline data collection, reduce the risk of inaccurate outputs and provide actionable insights to help organizations stay competitive.

This article is part of BizTech's EquITy blog series.