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The role of AI in Master Data Management

Master Data Management (MDM) is the process of maintaining a central repository  - or single source of truth - of an organization's critical data, which includes customer data, product data, and other key data entities. The data is often scattered across multiple systems and applications, and MDM helps organizations to consolidate and manage this data effectively.

As organizations have embraced digital transformation in order to better serve their customers and enhance operations, this reliable supply of high-quality, accurate, and accessible data has become even more desirable. The problem, however, is that in many cases MDM has never quite lived up to the promise of delivering it.

Do we need AI to fix MDM?

Augmented data management techniques such as zero modeling and eventual connectivity have already gone a long way toward solving some of the well-established problems with MDM. For example, it is now possible for business users to wrangle with the data directly, without continually needing the support of IT teams. Upfront data modeling, profiling, and analysis are no longer a necessity as Graph-based platforms like CluedIn can do this work for you in a completely automated fashion once the data has been ingested. Systems like CluedIn are also capable of automating the integration of data in any format from a limitless number of sources. All of this has accelerated time to data value significantly and allowed businesses to fast-track insights, intelligence, and data science initiatives.

However, traditional MDM systems have struggled to keep up with these advances, and in some cases have turned to AI as a means of bridging the gap between what they should have delivered and the reality. For example, there are MDM players today using their own AI engines to help with data lineage – i.e. cataloging the sources of master data and their domain types, and mapping how master data moves between sources and applications. Advanced MDM systems like CluedIn can already do this – without relying on AI. Another example would be using AI to help automate schema matching. Again, not a job that requires AI if you’re using a Graph-based, augmented MDM platform.

What is the role of AI in MDM?

That said, there are areas in which the use of AI can dramatically improve the speed, cost, and ease of preparing data for ubiquitous use across an organization. As advanced as an MDM platform may be, there is no doubt that AI is a force accelerator when it comes to mastering data. Here are just a few examples of the potential application of AI in MDM:

  • Data Quality: Data quality is a major concern in Master Data Management, as data is often incomplete, inconsistent, or inaccurate. Advanced systems like CluedIn have already automated much of the data cleaning and enrichment process, but AI brings a whole new level of speed and simplicity to this exercise by using machine learning algorithms to automatically identify and resolve data errors, such as duplicate records or inconsistent data formats.
  • Data Governance: Creating and enforcing effective data governance is a challenge for every organization. It not only involves creating policies and procedures to ensure that data is properly managed and secured, but also the application of them which is where many data governance efforts fall down. With AI, however, the policy or rule can be automatically enforced immediately following its input into the platform.
  • Data Democratization: One of the main problems with traditional MDM is its heavy reliance on IT teams both in terms of deployment and ongoing use. Again, platforms like CluedIn have taken a low/no-code approach in order to make the system as accessible as possible, but the potential is for AI to take this to a whole new level as natural language processing makes even the least technical amongst us data scientists.
  • Data validation: A huge benefit in the application of AI with MDM is that it can effectively act as your “MDM co-pilot”. This not means that it can explain any of the decisions it took at your on-demand, but it will also intuitively corroborate (or challenge) your decisions too.
  • Data Maintenance: Ensuring that your data is up-to-date and ready to deliver at any time is an ongoing, resource-intensive task. AI can help to automate data maintenance by using machine learning algorithms to identify changes in data records and update them automatically. The benefit of doing this is that the model can essentially train itself based on the data in the MDM system – becoming more reliable and accurate over time.

Should MDM vendors build their own AI engines?

As previously mentioned, some MDM vendors have already built their own AI engines as part of their MDM offerings. The issue with this is that their models will never be as powerful or comprehensive as dedicated AI platforms like OpenAI, Google AI, and IBM’s Watson. Developing a high-quality AI engine requires significant expertise in machine learning, data processing, and software engineering. It is also a time-consuming and expensive exercise. Although MDM vendors may have some of the required specialisms and investment capacity at their disposal, AI is not their core focus, which is why in most cases they are better off partnering with AI vendors or utilizing existing AI platforms to provide their customers with the best experience.

What’s next for AI and MDM?

It’s an exciting time for the technology industry as AI gains momentum and starts to show exactly what it is capable of. At the moment, we have only witnessed a fraction of what AI can bring to the data management industry as whole, and MDM in particular. Without a doubt, AI will bring about a major transformation in how we prepare data to deliver insight in the future, and once its potential is realized the way we master data will be changed forever.


By Natasha Scott
Head of Demand Generation at CluedIn