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Below are seven tips that can help you successfully craft and execute a plan designed to clean the data, and importantly, ensure its quality is maintained.
As Chief Information Officer, one of the more frequent comments I hear from customers is that they don’t trust the data in their systems. After making significant investments in technologies, they find system data to be subpar, which makes it challenging to fully understand their business—or effectively engage and serve their customers. So how can you get a handle on the Information component of Information Technology—especially customer data?
Below are seven tips that can help you successfully craft and execute a plan designed to clean the data, and importantly, ensure its quality is maintained.
For almost any endeavor, I’m a big believer in crafting a compelling vision that stakeholders can rally around. I also believe in keeping it as simple as possible—with a vision stakeholders can quickly comprehend and easily understand how to support. For customer data, I suggest something like this:
Note how these three elements follow the customer lifecycle, from pre-sales to post-sales activities (e.g., support and billing). For companies that have other major milestones in their customer lifecycle, such as complex product implementations, additional elements can be added to your vision statement. Just make sure you keep any additions as simple as possible, as you want your vision to be easily recalled.
Information systems are capable of housing large volumes of data, and it’s not uncommon for Customer Relationship Management (CRM) systems in use for many years to have hundreds of data elements or fields for every customer. In reality, the actual number of data elements you need to “know who your prospects and customers are” is likely much lower: company name, key contacts, key contact information, hierarchies (i.e., the company is a parent or subsidiary of another company), revenue numbers, and territory and sales manager assignments. Define what data you want on the list, but like your data strategy vision, avoid “bloat” by keeping it simple. And then repeat the process for each data strategy vision element.
Aligning the data elements with stages of the customer lifecycle makes it relatively easy to identify good candidates for improving and sustaining data quality. Prospect and customer records are first created and used by Marketing and Sales, both of which are good functions for managing CRM data, or “who your prospects and customers are.” Business Operations and/or Finance teams typically operate a company’s ordering and billing systems, and these teams are good candidates for managing what’s often called entitlement data, or “what they purchased from us, and how much they paid.” And Support teams often have the best understanding about customer experience post-sales, and are good candidates for managing support data, or "what their post-sales experience with us is like.”
Regardless of how your company is structured, using the customer lifecycle as well as data elements used at each stage is an effective way to determine the best team to task with cleaning and sustaining the data for each element.
Now, the hard work to clean and sustain data begins. For smaller companies with simpler needs, the only resource needed may be the time your employees require to manually clean the data. It’s feasible for businesses with a relatively simple product portfolio and customers that number in the hundreds to clean data record by record.
For larger companies with tens of thousands of customers (and a complex portfolio of products and services), additional resources may be required to make timely improvements. For example, data boutique firms exist that can provide CRM data that already has updated elements, like company name, key contacts, and hierarchies. This leaves a smaller number of data elements that must be cleaned and enriched manually. And complex product portfolios can require product mastering to give you the ability to effectively track purchases and payments (i.e., entitlements)—a capability that might require outside expertise to develop.
Business functions are best suited to define and meet their data quality needs, but IT is responsible for ensuring systems are available to securely store, manipulate, and present the data. This work includes ensuring systems of record are defined for each data element, that fields exist to store the needed data, and that fields are properly permissioned to ensure only the right teams and individuals can manipulate the data. IT is also responsible for developing, implementing, and operating data integrations that reliably and securely transport data between systems.
If you invested time on tip number 2, then establishing success criteria should be relatively easy. Success means the data you defined as essential is clean. Measuring attainment can be a little more challenging, especially for companies with data volumes too large to assess manually. For these organizations, crafting system queries that sample a statistically significant volume of data can help make the task more manageable. Building and generating reports that support data quality assurance is typically something IT can assist with, though business functions are best suited to review and assess the reports.
The minute your company stops managing data quality efforts, your data begins to decay. Sales representatives may forget to update a key contact’s new phone number, or a duplicate account mistakenly gets created in your CRM or a new product is introduced without a corresponding product ID. Avoiding data decay requires ongoing quality efforts that are woven into your routine operations— an investment that will undoubtedly pay dividends.
Ultimately, getting your data to a place you can trust it to help run your business more effectively requires your willingness to put in the time. But with patience, clear vision, and delegation of clear responsibilities to the right teams, you can break it into manageable parts. It can turn a seemingly daunting endeavor into a much more achievable goal.
