Obinna Dike works as a software engineer for Citi in the Greater New York area. He has over seven years of experience with creating complex software systems, and working with big data and business analytics.
Business analysts work in a challenging environment. On the one hand, they are efficient because they have well-defined, purposeful processes and models.
On the other hand, there is no innovation without experimentation and trying to do different things in different ways.
Innovation and efficiency directly contradict each other. This presents organizations with a challenge: focusing on just one of these, and ignoring the other, inevitably leads to a low value.
One way to approach this challenge is to make efficiency the primary goal. When such a goal is created, analysts can develop very well-defined and results-oriented processes. However, this comes at a cost: such processes are usually very resistant to change. Every time things change, they usually fail to keep up. Adapting the processes to change requires a significant investment of time and money.
Business intelligence analytics is a prime example of this challenge. To manage risk and have defined prerequisites, most organizations exhaustively scope metric requirements before developing reports. The advantage of this approach is that is allows for repeatability, reduces uncertainty, and formalizes processes and roles.
The problem is that such an approach is very inflexible. A business may not know what it needs to focus on until it analyzes a vast array of data. It is also challenging, and at times impossible, to build models and reports based on unknown or nonstandard requirements.
In most situations the answer is about achieving a balance between flexibility and certainty. Software engineers like Obinna Dike Citi can build any model as long as the client knows what he is looking for.
