The ability to engage in data analysis is not a destination, it is a muscle your staff possesses—and the sooner you can start working that muscle, the better. It’s time to get more from your data than routine reports. Instead, you can use it to reveal insights that will serve your organization’s goals and mission. But with a multitude of priorities and commitments sometimes it’s hard to find the right time to get started.

You might be surprised at how data analytics, strategy, and governance can be developed together, step by step. Contrary to popular belief, you don’t need to put analytics at the end of the process. Here are three common myths about association data that can hold back progress.

My Association’s Data Is Not Clean Enough for Analysis

“I’m really pleased with the state of my association’s data,” said no association executive ever. We all know data cleanliness is something to strive for, but we seldom, if ever, fully achieve it. You might be tempted to think that once your data is in tip-top shape, then you can go beyond reporting and start using data for deriving business insights.

That might sound appealing, but data analysis can help you understand how to prioritize your data cleaning efforts. Through exercising that data analysis muscle, you can unearth obvious discrepancies in your systems and prioritize which data is most critical to clean in the first place.

By engaging in data analysis, you can advance an understanding of what data is most critical to your association. Then it’s time to begin a process for addressing that data, rather than setting an unrealistic goal to fix everything. This approach reduces the risk of getting overwhelmed and stalling your efforts.

It’s possible to begin data analysis with imperfect data and still derive relevant insights as well as directional focus for data cleanliness efforts.

We Need a Data Governance Policy Before We Begin Analysis

Data governance is the complex, interconnected web of practice that goes on in a business, spanning how an organization collects, uses, and manages data and data systems. It is critical for modern businesses to have an effective data governance policy.

However, data governance is just a part of the wider journey an organization will take to increase its overall data maturity. A less-evolved data governance model will not necessarily prevent you from using analytics in a constructive way. As with data cleanliness, you can take a step-by-step approach to data governance, creating an initial framework to get started, then evolving it as you increase your understanding of the place data holds in your organization.

Again, it’s better to have organizational experience with data analysis and develop an understanding of your most relevant data-related processes before undertaking a significant project to fully articulate a data governance policy across all areas of your business.

It’s possible to begin data analysis with imperfect data and still derive relevant insights as well as directional focus for data cleanliness efforts.

We Need a Data Strategy Before We Begin Analysis

It is difficult to pursue a data strategy until your team has experience with conducting data analysis. It is through the practice of reporting and analysis that an ideal strategy will begin to reveal itself.

While it is valuable for organizations to compile analysis questions from staff, members, volunteers, and board leadership, that alone is not an effective starting point for building a data strategy. If you begin the practice of analysis within your organization, you will go much further in drafting a data strategy.

When you start to engage in data analysis, you can tie analytical questions to insights that will change your organization for the better and begin to determine the scope of an ideal data strategy. Scope plays an important role for organizations early in their data maturity. How can you begin to articulate scope if you are drafting a strategy in the absence of any actual analysis?

These myths unfortunately add to the misconception that data analysis is an overwhelming challenge. Don’t let your organization be held back by them. Among your leadership and teams, socialize a view of data analysis that does not create unnecessary obstacles to achieving your goals.

Data analysis technology is increasingly accessible and enhancing staff skills and contracting people for this kind of work has never been easier.

Beginning analysis work with your data in its current state will help you build a plan that clarifies your goals and will create natural curiosity, buy-in, and progress more quickly than if you wait for the “right time.”