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Master Data Management: How to Get the High-Quality Data Your Business Needs

Tom Hoblitzell | | May 2, 2019

Being a data-driven company should be the goal of every organization. Data helps you retain more customers, run more effective marketing campaigns, and make smarter business decisions in general.

 
There’s a catch, of course—this information has to be high-quality, accurate, up to date, secure, and available across the enterprise. Master data management (MDM) is the collective term for the organizational policies and practices that aim to create a single point of reference for your business data.

Every year, the Business Application Research Center (BARC) publishes a survey about companies’ attitudes towards trends in business intelligence, including MDM. According to the BI Trend Monitor 2019 survey, MDM and data quality is the most important issue among 20 BI trends for the second year in a row.

In this post, we’ll go over BARC’s recommendations for improving MDM at your organization, broken down by the three classic factors of people, processes, and technology.

The Right People

Organizations that excel at MDM foster a data-driven culture by delegating certain roles and competencies to their employees. This ensures that everyone is always on the same page and aware of their data management responsibilities.

Some common data management roles are:

  • Data owners who are ultimately accountable for the quality of data assets. These people make executive decisions about topics such as who has access to a given asset.
  • Data stewards who perform regular checks to ensure that the data is high-quality.
  • Data managers who are responsible for the technical implementation of the data owner’s decisions.
  • Data users who access the data.

The Right Processes

MDM practitioners often follow a process known as the “data quality cycle.” The choice of the term “cycle” is far from an accident. This acknowledges that once achieved, high-quality data is not frozen in time; it must be constantly maintained, refreshed, and updated.

The data quality cycle consists of five broad stages:

  • Definition: The business defines the rules, goals, and metrics for its data quality strategy. The specifics of this stage will be different for each organization. For example, how often should a given database be updated with new information? Is it really necessary to keep complete records of a certain type of data, or can some fields and records be dropped?
  • Analysis: The business performs an in-depth analysis of the types of data it wants to maintain—for example, what kinds of values a given field can assume.
  • Cleaning: The business cleans the data to remove inaccurate, corrupted, unused, and out-of-date information.
  • Enrichment: The business enhances its existing data with additional information as necessary in order to achieve its objectives. For example, if a retailer installs free Wi-Fi networks in its stores, customer records may be enriched with geolocation data about store visits.
  • Monitoring: The business continuously monitors and checks the quality of its data to ensure that it meets standards. This is often done with automated software tools.

The Right Technology

There are now more software applications than ever before to help with your MDM goals and requirements, each with different features and functionality. MDM software has a variety of purposes, including:

  • Data cleaning and standardization
  • Data enrichment
  • Data migration
  • Data profiling (collecting statistics about a data source to understand its structure and content)
  • Integrations between different databases, applications, and systems
  • Reporting and dashboards

Conclusion

MDM requires a good deal of effort to perform correctly, but it’s an essential business practice as your organization grows and scales. The sooner you get started, the less work you’ll have to do and the sooner you can start enjoying the benefits of good MDM.

Looking for help? Our Analytics team can get you on top of your Master Data Management so you have the data you need when you need it. Learn more here.

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