At a client meeting recently I was informed that the company had over 150 business analysts. Even the client’s business executives acknowledged this to be true. But that many business analysts suggests that these people are spending their time on efforts outside the realm of business analysis.
Most IT organizations model the business analyst (BA) role around transactional or operational systems. Whether organizations buy packages or build code from scratch, business analysis skills are usually focused on system and process analysis. The majority of data issues within an operational system are data entry-related. The challenge in business analysis is to establish standard business processes to automate.
Few operational systems are built with the goal of data share-ability. So issues such as standard business values and definitions don’t come up. When a new ERP system is being implemented, little attention is paid to the customer id number or the customer’s name. It’s fairly common for operational systems to be built in an isolated way to support a well-known business process with no attention to data standards.
But it’s different in the BI and analytics environments. To be successful in BI, it’s critical to have integrated data from individual systems to support often-complex analytics. The BA doesn’t need to focus on the business processes that created the data—rather he or she should focus on the business scenarios that mandate accurate and meaningful data. The expertise needed is around data content analysis and understanding data from different source systems and what it represents.
To be an effective BA for business intelligence or master data management it’s critical to understand how different systems see data, even data that’s ostensibly the same. In a telecommunications firm, System A was account-based and System B was customer-based. Customer details existed in both systems. A good BA understands which data is critical from each system and what the rules are for matching records across them.
The BA needs to:
- Be able to identify different business scenarios for how data will be used
- Interested and willing to go through often-tedious analysis of content, formatting, and definitional differences in data within and across systems
- Be comfortable with the tools necessary to dig into the data and profile it
- Excel at communicating data requirements and anomalies in business language
At the end of the day a good business analyst should understand that data should be independent of applications and reflective of good business terminology. Until then, we’ll have hundreds of them and they’re likely to be forklifting data from one system to another in an on-demand, non-repeatable, non-scalable, and inefficient way.
By Evan Levy
Sometimes we find clients who overestimate their need for analytics. Often, IT is focused on using BI to analyze a problem exhaustively, when sometimes exhaustive analysis just isn’t necessary. Sometimes our analytics requirements just aren’t that sophisticated.
Twenty years ago, WalMart knew when it needed to pull a product from the shelf. This didn’t require advanced analytics to drill down on the category, affinities, the seasonality, or the purchaser. It was simple: if the product didn’t sell after six days, free up the shelf space and move on. After all, there were other products to sell.
Why does this matter? Because we get so wrapped up in new, more sophisticated technologies that we forget about our requirements. Sometimes we just need to know what the problem and resulting action is. We don’t necessarily need to know the "why" every time. Often, all business users want is the information that’s good enough to support the decision they need to make.