7 Steps to Take if You Don’t Trust Your Data

7 Actions to Take if You Don’t Trust Your Data

Do you trust the data in your system wholeheartedly? Many professionals find their system data to be subpar, which makes it challenging to fully understand their business. Here are seven tips from Epicor’s CIO that can help you successfully craft and execute a plan designed to clean the data, and importantly, ensure its quality is maintained. The full article from Epicor is available here.

A Plan to Get Your Data Reliable, and Keep It That Way

1. Start by crafting and communicating a data strategy vision

Craft a compelling vision that stakeholders can rally around, and keep it simple.

For customer data, something like this is suggested:

  • You want to know who your prospects and customers are.
  • You want to know what they purchased from you, and how much they paid.
  • You want to know what their post-sales experience with you is like.

2. Identify the specific data elements required to support your vision

While CRMs are capable of housing huge data volumes, 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.

3. Assign owners to clean and sustain the data for each element

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.

For example prospect and customer records are often 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 good candidates for managing support data, or “what their post-sales experience with us is like.”

4. Determine what resources each data element and its owner need to be successful

Now it’s time to clean and sustain the data.

For smaller organisations the only resource needed may be the time your employees require to manually clean the 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. 

5. Learn how to leverage IT effectively on your data quality journey

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.

6. Measure progress using empirical success criteria

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.

7. Make maintaining data quality a regular part of your operations

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.

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