Back when I was applying to college, I’d read over college catalogs. Inevitably, each university would mention the number of books it had in its library. When I finally went to college, I realized that this metric was fairly meaningless. A dozen volumes on Grecian pottery did me no good when I was in search of a book on polymers for my mechanical engineering class.
Clients will often ask us to scope a “data inventory” project, inevitably focused on identifying and describing all the data elements contained across their different application systems. Recently a new CIO asked us to head up a “tiger team” to inventory his company’s data. He was surprised at the quantity of information needs that had been sent his way. As expected, he inquired about systems of record and data dictionaries. As you can imagine, he received multiple and conflicting answers which only exacerbated his confusion.
As a point of reference, well-known ERP systems can have in excess of 50,000 discrete data elements in their databases (never mind that some aren’t in English). As I’ve written in the past, many of these data elements have no use outside of the application itself.
Having terabyte upon terabyte of information is equally irrelevant if that data is unrelated to current business issues. The problem with a data inventory activity is that identifying and counting data elements in different systems and applications won’t necessarily solve any problems. Why? Because data across applications and packages is inconsistent: there are different names, definitions, and values, and there is no practical means of determining which data they actually have in common. This is like going to the hardware store and looking for a specific screw, but all the different screws are in one big barrel—you end up having to pick through each screw, one at time. When you find the screw, you just throw all the other screws back into the barrel.
The point of a data inventory isn’t to pick through data because it exists, but to inventory the data people actually need. If you’re going to undertake a data inventory, your output should be structured so that the next person doesn’t have to repeat your work. Identify the data that is moving across various systems, as this indicates key information that’s being shared. Categorize this data by subject area. You’ll inevitably find that there are inconsistent versions of the data, enabling you to identify data disparities. You can then begin to develop a catalog of key corporate data that will form the basis of your data dictionary.
Inventorying the data that moves between systems accomplishes two things: it identifies the most valuable data elements in use, and it will also help identify data that’s not high-value, as it’s not being shared or used. This approach also provides a way to tackle initial data quality efforts by identifying the most “active” data used by the business. It ultimately helps the data management team understand where to focus its efforts, and prioritize accordingly.
So next time someone suggests a data inventory without context or objectives, consider sending them to college to study Grecian urns.
By Evan Levy
One of the most misunderstood roles on a BI team is the Project Manager. All too often the role is defined as an administrative set of activities focused on writing and maintaining the project plan, tracking the budget, and monitoring task completion. Unfortunately IT management rarely understands the importance of domain knowledge—having BI experience—and leadership skills.
To assign a BI project manager who has no prior BI experience is an accident waiting to happen. Think about a homeowner who decides to build a new house. He retains a construction company and the foreman has never built a house before. You’d want fundamental knowledge of demolition, framing, plumbing, wiring, and so on. The foreman would need to understand that the work was being done in the right way.
Unfortunately IT managers think they can position certified project managers on BI teams without any knowledge of BI-specific development processes, business decision-making, data content, or technology. We often find ourselves coaching these project managers on the differences in BI development, or introducing concepts like staging areas or federated queries. This is time that could be better spent transferring knowledge and formalizing development processes with a more seasoned project lead.
In order for a project team to be successful, the project manager should have strong leadership skills. The ability to communicate a common goal and ensure focus is both art and science. But BI project managers often behave more like bureaucrats, requesting task completion percentages and reviewing labor hours. They are rarely invested in whether the project is adhering to development standards, if permanent staff is preparing to take ownership of the code, or whether the developers are collaborating.
An effective BI project manager should be a project leader. He or she should understand that the definition of success is not a completed project plan or budget spreadsheet, but rather that the project delivers usable data and fulfills requirements. The BI project manager should instill the belief that success doesn’t mean task completion, but delivery against business goals.