In the evolving world of Artificial Intelligence (AI), we are witnessing a revolution. AI is no longer a mere idea, it’s becoming a thinker and, soon enough, a decision-maker. Imagine this: after 75 years, Alan Turing’s dream of a thinking machine is slowly becoming a reality. Moore’s Law is still holding true, and hardware technology is advancing at breakneck speed. But here’s the twist: AI systems may be getting smarter, but they can only be as good as the data they’re fed. Clean and well-governed data has become the bedrock of any successful AI initiative.

At this very moment, enterprises must realize that data governance is no longer just a luxury or a side project. It’s a necessity. AI, especially Generative AI (GenAI), is not just a research project about Neural Networks, Transformers, backpropagation or complex algorithms. It thrives on high-quality data, compliance with legal regulations, and robust Enterprise Data Governance (DG) frameworks. Without these, even the most sophisticated AI systems will falter.

Welcome to a series of articles with Use cases on Enterprise Data Governances. Through these articles, we will explore various use cases related to MDM and Data Governance. In our next series, we will cover Use Cases with solutions on AI Governances. These are challenges I have personally witnessed, as many enterprises struggle with data management, often spending more than they should simply because they haven’t been able to prioritize this area due to their exponential growth. So it’s not that they don’t want to fix it; rather, it’s often about not having the time or resources to do so. However, addressing these challenges now can save companies from making costly mistakes later. Even a company with growth challenges can leverage these ideas, and it will show them a clearer path forward.

Have you ever felt the overwhelming chaos of managing a rapidly growing product catalog? If not, you might be in the minority. This issue is something I see far too often when working with AI and machine learning (ML) projects in medium to large organizations. What starts as a manageable list quickly spirals out of control, leaving stakeholders frustrated, confused, and wondering how it all went wrong. With duplicates with various names in the same product list may not help an ML product recommendation engine or an AI Chatbot Customer Support service. 

In one case, I worked with a company where the product catalog ballooned from just five products to over 121 products, many of which were ambiguous. There were no new product launches beyond the originals 5, still the list is exhaustive. Reports were meaningless. Executives were stunned when they saw product names they had never even heard of—yet they were listed as being part of their business. The situation was dire, and the longer the company waited to fix it, the more complex the mess became.

However, the solution to this problem was surprisingly simple once we identified the root cause. The key was acting early. If you catch data inconsistencies before they snowball, you can avoid much more expensive and time-consuming fixes later on. Ignoring the issue, however, would lead to compounded challenges, including poor decision-making, revenue loss, and a general lack of trust in the data. In this case, cleaning up the product catalog became the most important step toward gaining back control and clarity.

Think about it in terms of personal finances. Imagine your credit score being completely out of whack, with no clear idea of how much debt you have or how much credit you’re using. That’s the equivalent of an unorganized product catalog in an enterprise. Without proper Master Data Management, you’re essentially navigating a business with no idea of where you’re headed. The result? Rampant overspending, inefficiencies, and missed opportunities. Enterprises that fail to govern their data properly are essentially shooting themselves in the foot.

Let’s take a closer look at how a seemingly small issue can escalate into something far bigger. For example, let’s say we have Company X, which offers Music and Acting Courses across three categories:

• Singing Lessons – Western Classical
• Guitar Lessons – Banjo
• Piano Lessons – Western Classical

Seems manageable, right? But here’s where things start to get tricky. In the “Singing Lessons – Western Classical” category, Company X offers three core courses:

1. Singing Lessons – Western Classical – Beginners
2. Singing Lessons – Western Classical – Intermediate
3. Singing Lessons – Western Classical – Performer

Now, imagine these courses are delivered worldwide by seven different instructors, each tailoring them to their local audiences. Over time, these instructors start renaming the courses to suit their preferences. What was once three courses now becomes 24, then 96, and so on. The catalog grows exponentially, with each new instructor creating more variations. The result? A tangled mess of data, multiple disconnected databases, and severe difficulty in tracking course performance, revenue, and other key metrics.

This is the nightmare scenario. How do you track the performance of a course when its name is constantly changing? Indexing or tagging can help to some extent, but without a robust MDM system in place, the catalog will quickly spiral out of control.

The impact of poor data governance and a lack of MDM becomes clear very quickly:

• Course performance tracking becomes unreliable.
• Revenue tracking for each course becomes nearly impossible.
• Systems become bloated and costly.
• Employees begin to compete for credits they’re not sure they earned.
• Scaling the business becomes a Herculean task, akin to trying to catch multiple jumping fish with two hands.
• Security and compliance efforts double, leading to more manual work and confusion.

These are just a few of the challenges that arise when proper MDM isn’t in place. In fast-paced industries, the rush to generate revenue often leads to neglecting data governance. However, as we know, businesses that don’t prioritize governance risk falling into chaos. Industries such as banking and healthcare cannot afford to bypass MDM due to strict regulatory requirements, but other sectors often take shortcuts, assuming they can avoid the problem. This is like leaving a messy garage untouched because no one ever sees it.

While social media companies may not face the same pressures around MDM, they’re outliers. Most businesses cannot afford to operate without sound data management practices. And that’s where Data Governance and MDM come in.

Now, let’s talk about how to address this issue with a structured approach to MDM. As the enterprise leader, it’s your job to understand the pain points, take ownership of the problem, and implement a solution. Trust me, it won’t be easy at first. Establishing MDM in a mature organization is a journey filled with challenges, but the long-term rewards are worth it.

Here’s how we can tackle the problem:

1. Create a Product Hierarchy: Develop a structured hierarchy for the product catalog. This may require some manual work, but clustering algorithms can help speed up the process. Data stewards play a crucial role in aligning the products with core product lines.
2. Rebuild Dimensions for Reporting: Use the new product hierarchy to create dimensions that help with reporting. This will allow teams to track performance across a consistent catalog and ensure better decision-making.
3. Establish Change Management: Implement a change management system to ensure that the product hierarchy remains up-to-date over time, without disrupting business operations.
4. Assign Data Stewards: Appoint both business and technical stewards to manage the product catalog and ensure consistency across teams.
5. Map Course Taxonomy: Align the product taxonomy with business goals, ensuring that each course fits neatly within the hierarchy and provides a clear connection between the data and the company’s broader objectives.
6. Monitor Data Quality: Regularly monitor the quality of the product catalog. Data quality systems help ensure that your data is continuously reviewed and remains reliable.
7. Allocate Resources: Dedicate financial and human resources to maintaining the product catalog. Ensure your team is well-equipped with the right tools to manage data governance and MDM effectively.
8. Automate the MDM Process: Use automation to handle updates and ensure that data quality monitoring is a priority.
9. Evangelize Data Management: Foster a culture of data stewardship throughout the organization to ensure that everyone understands the importance of maintaining clean data.
10. Implement the 11-Step MDM Framework: Follow a structured approach to MDM, including assessing current data, designing MDM architecture, integrating with enterprise systems, ensuring data quality, and continuous improvement.

By adopting these practices, you can avoid the costly pitfalls of poor data governance and build a solid foundation for future growth. A clean, well-managed product catalog leads to better decision-making, increased efficiency, and a stronger competitive advantage. With MDM in place, your business can scale with confidence, knowing that your data is accurate, trustworthy, and ready to support AI-driven innovations.

In summary, while the journey of implementing MDM and Data Governance can be challenging, it’s absolutely necessary for the long-term success of any organization. By tackling issues like an exponentially expanding product catalog early on, you can save your business from unnecessary complexity and costs. A clean, well-governed data system is not just about avoiding mistakes, it’s about enabling growth, empowering decision-makers, and preparing for the future.

Data governance is the unsung hero behind AI success. If you address data issues today, your business will be ready to thrive tomorrow. Don’t let your data get out of control. Take charge now, and reap the rewards later.