The importance of a mature data strategy for every business becomes clearer every day, and data literacy is a serious obstacle along the way. Have you run across data-related terms that weren’t clearly defined? Do you want to understand more about the key components to building a data strategy? In this post, we’re focusing on Master Data Management (MDM). When you’re finished reading, make sure to browse our other blog posts for more information to build your data literacy.
There are many variations on the Master Data Management definition because it’s a rapidly maturing discipline. MDM strategies tend to adapt and evolve depending on the unique needs of the business. Gartner defines MDM as:
“A technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.”
It’s best to focus on what MDM can do for your business and let your understanding of it take shape from there. Trust your business audience to give it the meaning most useful to them.
Does your business strive to satisfy its customers? Is growth important? How about reducing costs and managing risk by complying with regulations? If the answer to these questions is yes, then you need a Master Data Management strategy.
Here’s why: Master Data is the most important data your business has--it’s the foundation of your internal data related to customers, employees, parts, etc. When the foundation is broken, the rest of the house crumbles. Misinformation, duplication and errors spread across the organization unchecked, negatively impacting business decisions. Without Master Data Management solutions in place, it can be very difficult to pinpoint the source of those errors and fix them.
While your official definition of MDM may change, there are a few components that successful strategies tend to include.
Data quality and data governance are key to a successful MDM strategy. Data quality refers to how complete, accurate, credible and consistent your data is. Your data quality level is partially dictated by what kind of data your business needs vs. what kind of data you currently have. Data governance, on the other hand, ensures that your data stays at a high quality throughout its lifecycle. The accuracy and timeliness of your Master Data can’t be overlooked as they are the key differentiators between business leaders who make proactive decisions based on facts and business leaders who make reactive decisions based on anecdotal evidence. Make no mistake--without a sound data quality and governance plan, your MDM strategy isn’t likely to succeed. Learn more about the costs of bad quality data vs. the benefits of good quality data.
A focus on the right kind of data also makes a difference in right-sizing the scope of your MDM strategy. Your MDM team should understand the difference between Master Data and Reference Data. Reference Data is defined by data expert Malcolm Chisholm as “any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise.” In other words, Reference Data tends to be external data while Master Data is internal. A successful MDM strategy focuses on internal Master Data.
A mature data integration plan is also important. Data integration refers to the integration of business systems resulting in a single source of consolidated data. This helps democratize data access so it can flow securely and easily throughout the organization, being reformatted and manipulated as needed by various business users. Learn more about the powerful benefits of data integration.
Leadership and cross-functional peer buy-in can be difficult to build but is worth the effort. Spend ample time educating leadership and peers around the benefits of a Master Data Management strategy, and come prepared with answers to questions they’re bound to have. Don’t forget to put yourself in their shoes to clarify how they’ll benefit from the investment and time commitment involved. Make sure their concerns are heard and addressed, otherwise you will have quite the uphill battle getting your MDM strategy properly designed and implemented.
Data literacy skills and MDM talent are another pivotal component. As mentioned earlier, data quality, data governance and data integration are key to designing a successful MDM strategy. Achieving this requires a deep understanding of data science paired with clarity around your organization’s unique needs and the industry you’re in. With data experts in such high demand it can be very difficult for businesses to fill this need. Partnering with an expert in both data consulting and IT staff augmentation like Onebridge solves this need. It’s often more cost effective than trying to build in-house capability without any prior expertise, and it allows your team to focus on what they do best while our augmentation team builds your organization’s data literacy from within. Using a data science consulting firm like ours lightens the administrative and investment load on your organization while supporting business goals like reducing cost and increasing revenue.
Want to learn more? Get in touch with us today and we’ll help you navigate the road to a mature Data Analytics strategy and a successful Master Data Management roadmap.