With a remarkable talent for transforming vast datasets into compelling visual stories, Chloe Maraina stands at the forefront of Business Intelligence. Her expertise isn’t just in data science; it’s in her forward-thinking vision for data management and integration, helping organizations unlock the true potential hidden within their sprawling information landscapes. Today, she shares her insights on a critical component of that vision: the data catalog. We’ll explore the journey of building a successful data catalog, moving beyond technical checklists to understand the strategic thinking required. This conversation will touch on the crucial first step of securing executive buy-in, the foundational role of a business-centric data model, and the delicate collaboration between technical and business teams. We’ll also delve into the practical power of data lineage for troubleshooting and the essential design principles that make a catalog a truly valuable tool for everyone, not just the IT department.
Your first step mentions documenting metadata’s value for data governance. Beyond just stating the benefits, how can a team effectively demonstrate this value with concrete metrics or examples to secure executive buy-in for a new data catalog project?
That’s a fantastic question because this is where so many projects stumble before they even begin. You can’t just walk into a boardroom and say, “We need to manage our metadata better.” It sounds abstract and costly. Instead, you have to tell a story grounded in business impact. We build a case by documenting the direct link between properly managed metadata and improved data quality and operational effectiveness. For example, you can quantify the hours your analytics team wastes every week trying to find and validate data. If you can show that a data catalog will reduce that “data hunting” time, you’re talking about accelerating insight and making your most expensive data talent more productive. It’s about framing it not as an IT project, but as a business enabler that supports the very data-driven decisions leadership is asking for.
You highlight the importance of a subject area model as the catalog’s foundation. Could you walk us through the practical steps of creating one for a common domain like “customer” and explain how that model makes data discovery more intuitive for business users?
Absolutely. The subject area model, or SAM, is what prevents a data catalog from just becoming another technical repository that only developers can navigate. Let’s take “customer.” The first step is to forget about databases and tables and start with the business conversation. We gather people from sales, marketing, and support and ask: “What does a ‘customer’ mean to us? What information is most important?” From there, we define the core business concepts encompassed by “customer,” such as contact information, purchase history, support interactions, and marketing engagement. The SAM logically groups all the underlying data assets—which might be scattered across five different systems—under these intuitive headings. So, when a business user comes to the catalog, they don’t have to know that order details are in the FSL_ORD_TBL in the ERP system. They simply navigate to the “Customer” subject area, click on “Purchase History,” and find exactly what they need, with all the relationships clearly mapped. It transforms the experience from a frustrating technical search into a simple, guided exploration.
The article distinguishes between business glossaries and data dictionaries, noting they are domains for business and technical stewards. Can you share an anecdote about how these two roles collaborated effectively to define a complex business term and what challenges they overcame?
I remember a project where we had to define “Customer Lifetime Value.” It sounds simple, but the room was filled with tension. The business data steward from marketing had a clear definition based on their campaign models, which included predicted future purchases. The technical steward, however, pointed out that the data warehouse only contained historical transaction data. This is the classic challenge: bridging the gap between a business concept and its physical implementation. The collaboration was intense but incredibly productive. The business steward had to articulate the precise business rules—”It’s total revenue minus acquisition cost, but only for customers active in the last 24 months.” The technical steward’s team then translated that logic into code, documenting in the data dictionary exactly which tables, fields, and transformations were used to calculate that metric. They had to go back and forth for days, but in the end, they created a single, authoritative definition that was understood by the business and verifiably accurate in the technical systems.
You discuss incorporating data lineage from ETL tools into the catalog. Could you share a specific example of how this lineage information helped an analytics team trace a data error back to its source, and what the ultimate business impact of that discovery was?
There was a situation where a critical weekly sales dashboard suddenly showed a 20% drop in revenue for a key product line. The leadership team was on the verge of making some drastic, panicked decisions about marketing spend and inventory. The analytics team, however, went straight to the data catalog. Using the data lineage view, they traced the revenue metric backward, step-by-step, from the dashboard, through the data warehouse, and back to the ETL process that loaded the data. The lineage map showed them exactly where the data came from, its origin. They quickly discovered that an update to an upstream source system had changed the format of a product ID field. The ETL job hadn’t been updated to handle the new format, so it was silently failing to load sales data for that entire product line. Without that lineage, it would have taken days of manual investigation, sifting through logs and code. Instead, they identified the root cause in under an hour, fixed the ETL script, and re-ran the report. The business impact was enormous; it prevented a costly, reactive decision based on what was essentially a ghost in the machine.
The final step advises organizing the catalog for business consumers, not just IT. What are some key design choices that make a catalog user-friendly for non-technical staff, and how can you measure whether these choices are actually improving data accessibility and usage?
This is paramount. If a catalog feels like it was built for engineers, business users will never adopt it. The key is to base the entire structure on that subject area model we discussed, using plain business language. Design choices should mimic familiar consumer experiences—think a clean search bar, filters, and even ratings and comments so users can share knowledge. We should prominently feature the business glossary definitions right alongside the technical metadata. To measure success, we track user engagement metrics: How many people are logging in? Are they successfully executing searches? Are they using collaborative features like commenting? We also watch for a decrease in the number of ad-hoc data requests coming to the IT department. Ultimately, the best measure is a simple survey to the business users, asking them to rate the ease of finding and understanding the data they need. Seeing that satisfaction score climb is the clearest sign that your design choices are working.
What is your forecast for the future of data catalogs, particularly regarding the integration of AI and machine learning to automate curation and enhance the user experience?
I believe we’re on the cusp of a major evolution where the data catalog transforms from a passive, searchable inventory into an active, intelligent data discovery partner. Right now, commercial tools are already using AI and machine learning to automate the heavy lifting of profiling data and suggesting tags. But the future I see is far more proactive. Imagine a data catalog that observes a data scientist is working on a customer attrition model. It won’t just wait for a search query; it will proactively recommend, “I see you’re using customer purchase history. Other analysts building similar models also found our support ticket sentiment data and web clickstream data to be highly predictive. Would you like to explore them?” This level of intelligence will automate curation, certainly, but more importantly, it will foster serendipitous discovery, connecting users with valuable data they never even knew to ask for. The catalog will become less of a map and more of a guide, dramatically shortening the path from a business question to a trusted, data-driven answer.
