Two signs your organization may have outgrown its data strategy.
In any organization, there are some things that are relatively fixed. Mission and values, for example. Other things naturally evolve over time such as scale, services, and operating model. One thing that is sometimes treated as fixed but should not be is data strategy. By “data strategy,” I am referring to organizational frameworks, capacities, and decisions that inform what data are collected and why, how and by whom these data are managed and used, and how data ultimately informs organizational learning, accountability, and decision-making.
One problem with a static data strategy is that it can limit what an organization is positioned to know about itself. Specifically, when measures, metrics, and methods remain unchanged for long periods, they may constrain the range of questions an organization is equipped to answer. Think of it as a narrowing horizon. As an organization evolves, a static data strategy narrows its field of vision, leaving leaders trying to answer newer questions with systems that were designed for an earlier stage of the organization. In this sense, data may still provide some visibility while not fully illuminating present realities in ways that are useful for decision-making.
A second problem associated with a static data strategy is that with time it may no longer represent the most effective or efficient way of using data. In addition to methods, measures and metrics, data strategies are comprised of organizational capabilities such as staff time and expertise, software and technology, platforms, and workflows. When operational demands change such as increased clients, expanded services, or additional reporting requirements, organizations may find that the data capabilities that worked well at the previous scale are, to one degree or another, ineffective at the current scale. Resulting errors, duplications, and delays can ultimately reduce the usefulness of data.
Two Signs that a Data Strategy Might Need to Evolve
Here are two signs that your organization’s data strategy might be in need of a strategic update.
Increased Data Gaps and Bottlenecks
If you are a founder, there is a good chance you once owned the data strategy. You collected, cleaned, and reported the data your organization had. As the organization grew, however, it likely became necessary to delegate some or all of that work. That transition is not always clean or smooth. Data work may stop happening with the pace, consistency, or quality you are used to or that the organization now needs. Data gaps emerge as instruments are not administered consistently, accurately, or at all. They also occur when data are stored in multiple locations or in various formats, decreasing accessibility and usability. Reporting bottlenecks and periods of frenetic data activity follow when staff wade through the waters of data chaos as deadlines loom.
Persistent data gaps or recurring bottlenecks may signal that a data strategy no longer fits the organization’s current operations.
In some cases, this happens when responsibilities are handed off as the organization grows, but the underlying workflow is not redesigned to reflect the new scale of operations. Specifically, the new workflow may not account for the volume of data that now needs to be processed, who is responsible for which tasks and when those tasks need to occur, or the disparate reporting requirements that come with having multiple funders. Thus, what worked when one person informally managed a straightforward process may not work when responsibility is distributed across staff and when data needs have increased in scope and complexity. An updated data strategy is now needed to account for this evolution in data needs. It might include a clearer workflow, defined ownership, and a system that better supports follow-through.
Limited Organizational Clarity and Direction
In the early days of an organization, data work primarily fulfills reporting requirements and proof of impact. As organizations scale, some use data to help ensure continued growth, innovation, and sustainability. Data help answer questions such as: What is working well or not? What does it cost to serve a client? How do we improve? These are questions whose answers can bring some amount of clarity and direction to those making the tough decisions.
In 2014, I had the opportunity to help lead the evaluation of a publicly funded community mental health center in the Northeast. Annual surveys revealed that a majority of service users were satisfied with their care. The Executive Director was particularly curious about the experiences of the minority of users who were not satisfied and commissioned a focus group-based evaluation to learn more. The addition of this evaluation represented a key evolution in the organization's data strategy, which prior to then consisted of annual satisfaction metrics.
What the Executive Director learned through the evaluation became the North Star for planned improvements, including renovating the lobby and adjusting medication schedules in the inpatient unit. Evaluation findings in subsequent years informed the integration of primary care into the center and the creation of a gym for service users. In other words, the evaluation's data helped clarify where the organization could improve and what improvements would be most helpful.
For some organizations, a data strategy that no longer generates actionable insight can contribute to a sense of stagnation or uncertainty about how the organization should evolve.
Of course, not every organizational challenge can be reduced to a data problem. Stronger data systems alone cannot resolve issues such as limited funding, staffing shortages, or leadership conflict. Even so, organizations may still struggle when their data strategy no longer produces the kinds of insight needed to support learning, adaptation, and decision-making. In such cases, an organization’s data strategy may need to evolve to better support those functions.
Conclusion
To be clear, not all measures, metrics, and methods in a data strategy need to change. Indeed, continuity in some core data work is vital for program coherence and to tell a consistent story over time. However, when data gaps and bottlenecks, as well as limited clarity and direction appear, these may be symptoms of a data strategy that requires some updating to meet an organization's current needs and realities.
In some cases, organizations can internally identify these issues and make the needed adjustments. They can redesign workflows, clarify responsibilities, and improve data processes. In other cases, organizations may opt for an external expert to design a data use framework and administer a data capacity assessment. These processes can help organizations identify operational challenges, determine where systems no longer align with current needs, and recommend concrete changes to strengthen the organization’s data strategy.
Having a data strategy that your organization has outgrown is not uncommon among organizations navigating the complexities of evolution and scaling. I would even venture to say that the struggle itself can be a positive sign of growth or readiness for growth. But over time, the struggle can mushroom into a real problem that introduces frustration, inefficiency, and stagnation. If this sounds like where your organization's data strategy is, perhaps it is time for a refresh.
Editorial Note: AI was used to improve the clarity of this post in reference to grammar and precision. The ideas, arguments, and interpretations are entirely my own and reflect my own thinking and professional judgment.
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