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:
- Trusted data is the motivation behind any MDM or DG initiative. The end goal is to have managed data that users trust. • Single customer (or anything) view is really the end goal. While product information also factors into this, “party” or customer contact information tends to be the focus of many organizations seeking 360-degree views, enabled by MDM and DG.
- Self-service is more important now, than ever. With digital transformation and the push for data-driven strategy across organizations, empowering business users to control their data is so important. Gone are the days where IT managed every aspect of data: the rise of self-service is the new name of the game. Providing users with easy-to-use tools, friendly interfaces, and dashboards is now par for the course.
- People, culture, and attitude are three of the most important and essential pieces to success for any MDM or DG program. Even the best tools are there to enable the people, so creating a positive culture and attitude surrounding data initiatives is huge.
- One step at a time. Master Data management and Data Governance are both massive undertakings. While it’s important to take a holistic approach to these efforts, you cannot try to boil the ocean. Identify your priorities and first steps, set metrics, and use your successes to build momentum.
- Data quality is the foundation of every MDM and DG strategy, but still needs a reminder now and then. Starting with high-quality data is crucial. From there, you can build on analytics and more advanced programs and processes. Building a sustainable and successful MDM or DG program is no small undertaking.
With a foundation of quality data, your organization will be better set up for success.
Here’s why
