Today’s data-driven businesses have high expectations about data accuracy. How do you ensure customer data quality is up to the challenge? Bring your A-game with these four steps.
Step 1. ACCEPT That You Have Bad Data
Sorry to break it to you, but you have bad data. Without data quality processes in place to prevent errors in data entry and routinely clean the database, bad data is in the mix. If you’re lucky, the bad data is minimal. But more than likely, you have issues you don’t even realize are issues yet, such as:
Duplicates in a database create sales and marketing inefficiencies. Sales reps spend valuable time nurturing the same leads. Leadership can’t rely on sales forecasts. Marketing wastes spend on duplicate mailers or undeliverable email campaigns. And the resulting database bloat adds unnecessary marketing automation costs.
Lack of Standardization
When data fields aren’t standardized, database searches can pull insufficient results. This leads to skewed data reports, poor audience segmentation, and errors in projected ROI.
Customer data changes rapidly. Consumers change physical addresses, phone numbers, and email addresses. Lead and client data changes as individuals change jobs or businesses move, merge, or close. If you don’t regularly verify the validity of all this contact data, customer engagement and customer retention decay along with it.
Step 2: ASSESS Customer Data Quality Issues
Once you’ve accepted you have issues with customer data quality, the next step is to assess them. The best way to do this is with a data quality assessment designed for the purpose. You need an assessment that doesn’t just evaluate what’s wrong with customer data, but includes a remediation plan to help you address it.
Assessing customer data quality isn’t about placing blame. It’s about identifying different areas where bad data leaks in and finding ways to plug up those holes. The right assessment shows you where the quality of your customer data stands currently, and how to get it where you need it to be.
By identifying the root cause of an error, you’re better prepared to prevent it from happening again. And an assessment that pinpoints the impact data quality has on key business functions makes it easier to get buy-in for implementing solutions.
Step 3: ASSIGN Ownership
While it’s generally everyone’s responsibility to keep customer data clean, it’s best if someone is assigned ownership of customer data quality initiatives. This person should be your organization’s top data quality ambassador.
Who is responsible for customer data quality in your company? Perhaps it’s someone in IT, or a Salesforce admin. Maybe it’s a marketer who manages audience data in Marketo. If the role isn’t defined, identify who will champion customer data quality.
As part of their role, they should:
Define Data Quality
What matters to one stakeholder may not matter to another. Likewise, what one person deems to be high quality may not match someone else’s definition. Discover what each stakeholder considers to be high quality data to ensure everyone gets what they need.
Identify Your Ideal Record
Determine what your ideal record looks like, including the information that must be captured to make the record valuable. Requiring more fields than necessary can make data entry cumbersome for users. Instead, look for required fields that could be made optional. You can also implement tools that keep data standardized and reduce data entry errors. For example, employ pick lists where possible.
Establish Data Governance
Implement processes and protocols for data quality, and hold users accountable for adhering to them. Data governance is key to ensuring data quality is achieved, respected, and maintained.
Step 4: AUTOMATE Data Quality Processes
The final answer to the question, “How do you ensure customer data quality?” is automate data quality processes. This step is a cyclical one that keeps data as accurate and actionable as possible. You can identify many of the steps you need to clean, standardize, and protect customer data by asking yourself:
- How many users touch your customer data? Do too many individuals have administrator privileges? You can minimize your risk of poor customer data quality by minimizing who can manipulate it.
- Are you stopping bad data at the source? Employ email, phone, and address verification APIs to prevent errors in data entry wherever customer data is collected. Use deduplication software to flag or stop users when they’re about to enter a duplicate record. You can also use software to decide what to do with duplicate records when they’re found, such as merge or remove records based on criteria you set.
- Do you validate data before it flows into your system from sources like integrated databases, tradeshows, or acquired lists? Use software that cleans and verifies the data before you import it.
- Do you use third-party software to support your efforts or are you relying on native functionality only? Address more of your customer data quality needs by making the case for a more robust data quality platform.
To learn more about automated data cleansing, check out the on-demand webinar, Developing a Data Quality Process.