Last week, myself and members of the Experian team attended the MDM and Data Governance Summit in Chicago. The main topics of this conference were MDM (Master Data Management) and DG (Data Governance), although at many times, it was difficult to tell the difference. MDM and DG are starting to meld together as one topic, with MDM being the data repository for all (or as much as possible) corporate data, and DG being the documentation and “GPS” for navigating the data (in this case, GPS means “Gain Perspective Simply”.
The focus of the conference for most sessions was around the business user, and the need for the business user to have useful and productive access to their data. I hesitate to say control, as ultimate control around architecture, data types, security, etc. still seems to reside in IT. But the business user wants to be able to access data on the fly, manipulate that data in ways that meet their needs, and operationalize their rules and processes on a schedule that ensures data is fit for purpose within their timelines.
As technologies try to ingratiate themselves to business users, there is also a common theme around reporting and dashboarding. A few organizations that attended the event performed short demos for the audience and each one showcased a product through the eyes of a business user, using dashboards to drill into other content. This is a trend that will only increase over time, as products become easier to use and dashboarding technologies become more available for product integrations.
Terminology (for good or for bad) was also extremely consistent across presentations and products. “Trusted data” was spoken about often, along with “time to value”, “digital transformation” and “single customer view”. I interpret this to mean that we’re all still trying to solve the same problems, and that we want to provide data practitioners with usage terms with which they are familiar.
AI (Artificial Intelligence) and ML (Machine Learning) continue to be the hot topics du jour, with an emphasis on interrogating and analyzing big data in a more automated and thorough fashion, using algorithms to check for trends and anomalies that humans can make sense of without a massive amount of effort. Every vendor was talking about AI and ML, but there is still skepticism (even from themselves) around how much it is really being leveraged. The prevalence of graphing software also came up in my conversations, which means organizations are investing in the use of visual representations of data relationships to provide additional insights.
Data quality continues to be an area of concern in the project planning for MDM and/or DG, and was an area of focus throughout the event. The importance of data quality is slowly being recognized as a critical success factor for any data related project (as it should be). Users of data will never gain the “trust” they seek without a purposeful concentration on data quality.
At the end of the day, here are the key insights gained from the conference:
With a foundation of quality data, your organization will be better set up for success.