How to get more bang for your MDM buck

Mike Burger Slalom Consulting

Mike Burger

One of the biggest problems with a lot of master data management (MDM) engagements is that there isn’t any solid proof that the quality of the data is actually improving over time. Without any proof, it is hard for data governance groups within organizations to prove the ROI for MDM implementations. We’ve helped organizations solve this issue with data quality analytics, using Slalom’s MDM framework and the Microsoft BI stack.

Building data quality analytics

The process for creating data quality analytics is straight forward, but involves both the business and IT. First, identify a data source with data quality issues and procure a data sample. The source data used for data quality analysis can come from any platform(s). Once the sample source data is acquired, the data quality business rules need to be defined. For example, Customer Name must not include any special characters (#, %, ^, _). Using the Microsoft BI stack, the data is cleansed and the quality statistics are created. Statistics are created by loading a separate database with information related to the quantity and types or errors that exist within the source data. Since the solution is built to continuously load and reevaluate data over time, trends can be analyzed to ensure that the quality of the data is improving.

Building Data Quality Analytics with the Microsoft BI stack

Lastly, this solution uses visually appealing dashboards to expose the statistics surrounding the quality of the selected source data.

Data quality analytics dashboard

The results

The end result is a robust, easy-to-use platform that empowers all areas of an organization, from the C-Suite to data stewards, to monitor the quality of their data before, during, and after a data governance/MDM implementation. These dashboards can also be used to monitor data quality and ensure that it does not degrade over time.

A data quality analytics solution allows these organizations to gain insights into just how good (or bad) their data quality is. By understanding the exact quality of the data based on the defined business rules, clients can easily understand the impact and ROI of data-cleansing activities for a given MDM initiative.  The solution was designed with reusability in mind and developed around a common set of dimension, attributes (e.g., Customer, Customer Name, Customer Address, etc.), and business rules that could potentially be used within any organization. The solution is easy to implement and can be extended  across other data domains (e.g., Product, Item, etc.).

About mikeburger
I am a Solution Principal at Slalom Consulting. I have extensive experience on large end to end data warehouse implementations. Over the last 6 years I have had a strong concentration with the Microsoft SQL Server stack.

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