{"id":5151,"date":"2020-08-19T15:24:25","date_gmt":"2020-08-19T20:24:25","guid":{"rendered":"https:\/\/www.imt.ca\/?p=5151"},"modified":"2021-12-23T13:58:15","modified_gmt":"2021-12-23T19:58:15","slug":"the-five-key-mdm-metrics-you-need-to-track","status":"publish","type":"post","link":"https:\/\/www.imt.ca\/the-five-key-mdm-metrics-you-need-to-track\/","title":{"rendered":"The Five Key MDM Metrics You Need to Track"},"content":{"rendered":"

August 19, 2020<\/span>
\nDeanna Nole, Director – Solution Offerings\/Marketing<\/span><\/p>\n

Your master data is a strategic business asset \u2014 so make sure you\u2019re treating it that way. That means you should be demanding (or providing!) concise enterprise data metrics to measure MDM program effectiveness.<\/p>\n

However, simply relying on data warehouses to extract meaningful master data insights can obscure the important dimensions that measure your data management program\u2019s true return on investment. The best source of your important metrics is right in your MDM hub.<\/p>\n

Here are the five key MDM metrics you should be tracking:<\/p>\n

#1:\u00a0 How good is your source data? Is your data quality improving? If not, what are the contributing sources of bad quality?<\/strong><\/h4>\n

Quality snapshots are easy to get. But to understand if data quality is improving and sustaining, you need a history of the original source records. From there, you can accurately identify where data quality issues are originating \u2014 and take steps to fix them.<\/p>\n

#2:\u00a0 How current is your data? Is your data timely enough to support essential operations and analytics?<\/strong><\/h4>\n

Your MDM data may include records that have not been updated for more than ten years or are for deceased individuals. Maintaining irrelevant data can bog down operational performance, skew quality metrics, and generate unnecessary manual review work for data stewards. It is important to focus efforts on high value, current data. You should be working with your team to determine what is OK to purge.<\/p>\n

#3:\u00a0 How useful is your data? Can you more efficiently automate your data decisions without impacting quality?<\/strong><\/h4>\n

Over time, as MDM collects history on how data stewards make decisions on \u201csame person, different person,\u201d intelligent analytics applied against those past decisions can help you automate future data decisions with confidence and reduce manual cost and efforts. Do you have a place where you can access historical task decisions?<\/p>\n

#4:\u00a0 How complete is your data? Would data enrichment really benefit your data?<\/strong><\/h4>\n

Data enrichment services are popping up everywhere these days, and the lure of enriching or augmenting incomplete data sets is enticing. To be sure that an investment in data enrichment has any impact on master data sets,\u00a0 you first need to profile your master data composition and quality. Otherwise, you risk signing up for expensive data subscription services that yield little impact.<\/p>\n

#5:\u00a0 How accurate is your data? Is your master data as accurate as it could be?<\/strong><\/h4>\n

Accuracy matters and incorrect data breeds mistrust and hesitation to use the data for mission-critical initiatives. To reinforce confidence in master data across all stakeholders and potential consumers, you need metrics that demonstrate improvements over time or acknowledge suspected challenges to consuming stakeholders. Then document the steps you are taking to overcome these challenges.<\/p>\n

These metrics have been hard to measure!<\/strong><\/h5>\n

To be honest, until now these metrics have been difficult to produce from operational MDM solutions, for several reasons:<\/p>\n