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6 Simple Steps for Improving Data Quality Without Spending Big: Part II of II

In our last installment, we looked at three easy, but surprisingly effective, ways to get good data or make good data even better. Now, we’ll dig deeper into the process with three additional steps to expand your data improvement strategy.

4. Define clear objectives

Have you ever heard the phrase, “Done is better than perfect”? Well, it doesn’t apply perfectly to data management, because truth be told, you’re never really “done.” But the core message of the adage is spot on. Good data is better than perfect data because perfect data doesn’t exist. You’re better off continually improving the quality of your data than straining to make it perfect.

After all, in today’s world, the amount of data generated will always outpace an organization’s ability to manage it. As you define your objectives, select percentages of improvement over a time period, such as 10% score improvement over the next 3 to 6 months. This one-time goal will allow you to measure your progress and then refine your improvement goals “scale” on an on-going basis.

Next, designate an individual — yes, a single individual — to manage these goals. This singular level of accountability is critical to making actual progress. Accountability should not be diffused by pointing fingers at each other, and this should be a high priority objective for the person you defined as the accountable party. These goals embed nicely into performance objectives and, more importantly, into compensation incentive plans, as they have measures against which you can define clear and clean compensation targets.

5. Manage Expectations Across the Organization

Once you and the individual assigned are clear on the objectives, it’s time to make sure the organization as a whole understands the expected outcomes. This is not a side deal you strike with a person on your team; accurate and timely data is an organizational priority, and everyone is required to participate. Taking the lead management example, the organization must understand that while the demand generation manager may own the goal, partnering with the internal data team is key.

Likewise, take care to clearly determine data ownership. Who owns the customer data? Who authorizes access? Who can make modifications to this data and on what grounds? What are the downstream implications of updating this data, even if it would be for the better? Think through these questions and related scenarios and make sure you understand the overall impact as you embark on this mission.

Finally, explain your vision and plan to get there and reinforce the value of operating on better information — frequently referred to as becoming “data-driven” or “analytically-driven.” This crucial concept often gets swept aside as people get more into the technical aspects of data management, so keep your message clear and in business impact terms, since few care about the machinery that’s required to get there.

6. Let the Magic Happen

You’ve identified the key data, assessed data quality in your organization, made your business case, set goals, assigned an accountable party, and communicated expectations. What’s next? It’s time to work the process and let your team succeed. Your role in this step will be to remove roadblocks and measure on-going progress and velocity.

As you monitor progress, compare it against your original business case. This will give you a solid barometer to test your progress and efficacy against, and make any necessary improvements.

In conclusion, data quality directly relates to your firm’s bottom line. Companies with good data earn more, spend less, and waste fewer resources than those with poor quality data. They enjoy higher sales, more efficient operations, improved customer experiences, and reduced risk and exposure. What company doesn’t want that?

Still, data quality is only one dimension of the overall data management process. There is more to the data management process that we’ll be sharing with you in upcoming blog posts. Stay tuned!

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