Errors in data exist. And when most marketers start looking into their data, they realize the data problems are worse than they thought. Common problems include:

  • Miskeyed dates and misspelled names.
  • Inaccurate abbreviations (AR for Arizona, for example).
  • Multiple spellings for the same thing (e.g., John, Jon, and Jonathan).
  • Fields that are missing information.
  • Variations in date structures used in different systems, such as dd/mm/yyyy or mm/dd/yyyy.

Consider these examples: A higher education organization discovered it had more than 200 different prefixes used for individuals. That’s over and above Dr., Mr., Mrs., Ms. and Miss. A federal agency had missing Social Security numbers in 133 million records, or roughly 22 percent. When marketers struggle to get reliable answers, it’s often because of the quality of the data: the gaps, inconsistencies and errors within it. Marketers then ask our technology counterparts to help us “fix our data quality problem.”

As you’d expect, data quality software is used to solve many of these problems. Data quality software helps us evaluate, identify, and fix the misspelled names, date variations and miskeyed items. It standardizes capitalization, formats (such as zip codes or telephone numbers) and abbreviations. It provides metrics on the overall quality of our data. When data needs to be fixed within an application or as data moves from an application into a marketing database or data lake, data quality software is used to fix the problems that exist within that data source. 

Know the Difference Between Data Quality and Master Data Management

As you take ownership of your data, it’s essential to understand the difference between data quality and master data management (MDM). Where data quality software addresses the quality of data within an application, MDM solves a different, but similar type of data quality issue. MDM solves the problems in data across disparate applications such as Marketo, Salesforce, NetSuite and DemandBase.

MDM creates a trusted and authoritative view of the people, places and things that matter the most to a business and can be found in data company-wide. It applies integration, quality, enrichment and governance principles to create what’s referred to as a “master record.” Automation is used to reconcile, match and merge data across the systems that hold it. From there, the “clean” data is shared with the applications, systems and analytics that need it. In merging records, MDM can also correct for inconsistencies in records, capture where the data came from, and create an audit trail of changes — providing transparency with a trust framework that gives visibility into how a master record is created or modified.

Mastering data gives marketers the relevance and context that’s needed for timely, personalized and authentic communication with individual customers. For a marketer, master data most likely starts with the basics: the customer’s name, address, demographics, preferences, products owned, and social media accounts – but it doesn’t stop there.

Related Article: How Master Data Management Improves Your Understanding of the Customer

Know How a 360-Degree Customer View Can Evolve

So let’s dig deeper. Think about how a customer shows up in your campaign system, your sales force automation application, your billing system, and all of the different ways a customer interacts with your company. Is it all consistent? Or does William Henry appear as Bill Henry, Henry William, W. J. Henry and Mrs. W. Henry in different applications? If so, then master data management software can bring those records together to get a single view of the customer as a first step.

Learning Opportunities

You can then evolve that single view into a trusted profile as you expand beyond name and address by adding to the data you’ve merged into a single record. MDM can match the consolidated records and enrich the profile with third-party data such as those provided by Dun & Bradstreet, IMS Health, Experian, Acxiom and others. Records can be matched to external data to assemble household profiles or understand customers across a company’s multiple lines of business, products, locations, channels and geographies.

With MDM, you begin to recognize the different roles of customers and gain insights into the complex relationships they hold. For example, your customer Bill Henry may also be your employee, a small business owner, and part of a four-person household. The store location or office branch he visits most may not be the nearest to his home, but instead the nearest to his former residence. He goes there because Betty, who he’s worked with for over 10 years, is the employee he trusts the most. When Betty moves to a different location, will Bill’s preferred location also change?

By adding more of the data on hand to the master record, you can start to build a 360-degree view that includes more details, such as products customers have purchased or returned, employees they work with most, marketing communications that have been opened, sales offers that have been accepted, open service items, browsing activity, and more. This helps you to take the right action for the customer.

Related Article: 4 Reasons Why Data Quality Trumps Data Quantity

Next Stop: Data We Trust

Technology is evolving. A KMPG Guardians of Trust study released earlier this year noted “trust relates not only to a company’s brand, products, services and people — but also to the data and analytics that are powering its technology.” About two-thirds of the survey’s of nearly 2200 IT and business decision makers have some reservations or active mistrust in their data and analytics. With data-centric regulations like GDPR, trust in data is not only a marketing concern, but a boardroom one as well.

Marketing is evolving. My early experience with the online travel company provided visibility to the real value of data; a topic that is talked about frequently. But I often ask myself why a gap still exists in many companies who have yet to realize the benefits of real-time access and high confidence in data. It is possible to experiment, take measured risks, innovate with and get answers from our data to questions long thought of as unanswerable. As long as we pay attention to the quality, completeness, and relationships in our data, imagine what we could do.