S3 | E15: Defining A Data Strategy: What a Data Strategy is - and Isn’t

In this episode of the Risk Intel Podcast, Ed Vincent welcomes back Watchtower’s Chief Product Officer, Jeff Fink, to kick off a multipart series on creating a data strategy. A hot topic for many community banks and credit unions wanting to modernize their internal processes on how they handle data, collect it and how do they leverage data to achieve their collective goals. Listen to the full episode or read the summary outlined below.

“How do we leverage data to achieve our strategic goals? To me, that’s one way to think about the definition of a data strategy.” - Jeff Fink

What is a Data Strategy?

The conversation opens by challenging a common misconception: that a data strategy equates to reporting. Jeff quickly sets the record straight, emphasizing that a true data strategy is a comprehensive plan that outlines how an organization will collect, manage, analyze, and leverage data to achieve its strategic goals. It includes governance, architecture, tools, people, processes, and a roadmap for growth and maturity in data use. It's not simply about producing a set number of reports, it’s about intentionally managing data to achieve broader strategic goals for the organization. According to Jeff, a solid data strategy should encompass five key parts:

#1 Governance

At the heart of any effective data strategy is data governance or the rules and policies that define how data is handled across the organization. This includes:

• Ownership: Who is responsible for which data assets?
• Quality: How is data validated to ensure accuracy, completeness, and consistency?
• Compliance: What measures are in place to align with regulatory standards (e.g., GLBA, GDPR, etc.)?
• Security and Privacy: How is sensitive information protected against breaches and misuse?

Jeff stresses that governance is essential, not just for risk management, but also for building trust across the organization. Without clarity on who owns and stewards the data, efforts to leverage it can quickly become chaotic or ineffective.

#2 Architecture

Data architecture refers to the technical infrastructure, both cloud-based and on-premises, which supports how data is stored, integrated, and flows across systems. A scalable, secure architecture is the backbone of any data strategy. It should enable:

• Seamless integration of disparate data sources
• Centralized or federated access models depending on business needs
• Flexibility to scale with growing data volumes
• Real-time or near-real-time data availability when needed

This architecture must also be designed with maintenance and sustainability in mind, enabling future innovation rather than being rigid or quickly outdated.

#3 Tools and Technology

Modern data strategies require modern tools. From ETL platforms to data warehouses, from business intelligence tools to machine learning frameworks, the technology stack plays a critical role in execution. Jeff notes that the selection of technology should support the entire data lifecycle. But beyond having the right tools, institutions must ensure alignment between the tools and the organization's strategic goals, adopting technology with purpose, not just for its own sake.

#4 People and Roles

Your data strategy is only as strong as the people who carry it out. Jeff highlights the importance of clear role definitions and cross-functional collaboration. Key roles often include:

• Data stewards who ensure data quality and compliance
• Engineers and architects who build the pipelines and systems
• Analysts and scientists who derive insights from the data
• Business stakeholders who use data to drive decisions

Everyone from IT to compliance to frontline users should understand their part in the data ecosystem. A shared sense of responsibility is critical for execution.

#5 Processes

Finally, the strategy must be supported by agile processes that allow for continuous iteration and improvement. Jeff advises against large, monolithic “Big Bang” implementations in favor of:

• Incremental rollouts that show value early and often
• Phased roadmaps that align with business priorities
• Feedback loops that adapt the strategy based on real-world use

“Best practices aren’t about a Big Bang approach. It’s about setting a roadmap, prioritizing it, and implementing incrementally — that’s how strategy evolves.” - Jeff Fink

This approach fosters momentum and keeps the data strategy tightly coupled with the evolving needs of the organization. The emphasis here is on agility, iteration, and continuous improvement. A successful data strategy should grow alongside the organization, and serves as a living framework, not a one-off static document filed away and forgotten.

‍Why Reporting Isn’t Enough

While reporting is a visible output of a data strategy, it shouldn’t be mistaken for the strategy itself. Jeff explains that reports are tactical artifacts — snapshots that serve specific moments or decisions. However, they don’t address larger strategic concerns such as:

• Data quality
• Governance
• Long-term planning
• Evolving user needs

"Reports are important, but they’re tactical — the output, not the foundation.” - Jeff Fink

Reports are reactive tools, helping users respond to immediate questions. A data strategy, in contrast, is proactive, setting the stage for deeper insight, better decisions, and competitive differentiation.

As the episode closed, Ed reiterates the importance of distinguishing between strategy and execution. A good data strategy supports an institution’s evolution, it isn’t static, but dynamic and forward-looking. While reports are important, they’re not the foundation of a data strategy and the real value lies in a strategic, holistic, and agile approach to data management.

Stay tuned for the next installment, where Jeff and Ed will dig deeper into the nuances of reporting — what it gets right, where it falls short, and how to make it more strategic.

Listen to part one "How to Start a Data Strategy From Nothing" - here