Creating Your Data Cleansing Strategy

If your company databases were a garden, dirty data would be all of the weeds that seem to pop up overnight. On second thought, it’d also be the wildlife gradually eating away at your hard work, but trust us — it’s way less cute than fuzzy bunnies or wide-eyed deer.

Let’s put on those gardening gloves and get to work growing a comprehensive data cleansing plan for your budding marketing campaign.

Data: It can be the seeds for a one-of-a-kind digital strategy or the inhospitable soil that makes it impossible to grow.

Dirty data comes in many forms, but generally, it’s any record in your organization’s systems that’s incomplete, outdated or inaccurate. On the flip side, clean data would be the records that are valid and follow a standardized form, making them useful when it comes time to run analytics and other data-based initiatives.

Creating a data cleansing strategy to eliminate dirty data will lead to several benefits for both your team and your customers:

• Optimized resource allocation: Many customer data initiatives include the use of automation to reduce the manual lift for individual teams. Less data entry equals happier employees and customers.

• Enhanced collaboration: Half the battle when it comes to data cleansing is ensuring that every department is working from the same source of truth. When that happens, your organization gets a 360-degree view of the customer journey.

• Improved customer experience: Dirty data takes away from the accuracy of any analytics or predictive intelligence programs you’re running to better understand your customers. When you remove inaccuracies and outliers, you’ll have a clearer understanding of your target audience.

The moral of the story is, well … bad data is bad. However, rest assured that a data cleansing strategy will help your team to transform meaningless numbers into actionable insights.
 

Every team’s data cleansing strategy will look different depending on which types of data you’re managing and what systems it’s all being entered in. However, there are some common best practices to guide you along your journey to data optimization:

• Regularly audit your systems: As soon as data is entered, there’s a chance for something to go awry. Your team will need to commit to checking your systems on a monthly basis to ensure nothing is lurking in the weeds.

• Create a standardized format: If you have to manually edit your data every time you run your reports, you’ll end up spending more time in spreadsheets than you do actually learning from your data.

• Make data accessible: Even if data is accurate, when information gets siloed across departments it becomes less meaningful. It also creates the opportunity for the same record to exist across multiple systems. While duplicate data isn’t inherently bad, it’s often the culprit when teams aren’t on the same page.

It’s important to remember that the best data cleansing strategy doesn’t start with the software that information is being entered in. To achieve the highest quality of data possible, you’ll need to go to the source: your end users.

Keeping up with your data will require your entire company’s participation. 

Although data naturally becomes outdated over time, human error is often the primary cause of most of the existing issues in your systems. Whether it’s a complete accident — we’ve all forgotten to complete a form here and there — or an unclear formatting policy, it results in the same thing: dirty data.

As you work to educate your team on the importance of data quality, be sure to keep the following considerations in mind:

• What are your goals?: Your data governance initiative should always tie back to your organization’s mission when it comes to the customer experience.

• How can your current policies be improved?: If you already have a cleansing process in place but are still seeing a serious data quality issue, it might be time to reevaluate your processes and work with your end users to find a better solution.

• Do you need to appoint a data steward?: While everyone on your team should be able to view data, the fewer users you have editing records, the better quality data you can achieve. 

By helping everyone in your organization see the purpose of your cleansing strategy and creating clear policies, you’ll be well on your way to a squeaky clean database.

The hardest part about data management is that it’s an ongoing process that will require continuous support throughout your organization. 

That’s where data cleansing software can help.

Defined as any tool you use to optimize the process of collecting, monitoring, and managing data, this category of solutions comes with several benefits:

• Monitoring capabilities: These digital tools can be set to constantly monitor for data quality issues or missing values, alerting your team of any entry points that may be introducing errors to your systems.

• Data mapping: Achieve a new degree of visibility by getting a clear idea of where data is generated from, and how it’s being used across your organization.

• Automated data cleaning process: Once you’ve set your protocols for standard formatting, you can leverage an automated solution to address data quality issues as they arise. 

While creating your strategy will help to guide you through the process of cleansing data, the right software will reduce the manual lift on your team’s end and give you an extra layer of control.