There is a major change happening in the IT industry—the use of big data and analytics to guide how businesses are run. Many companies are embracing analytics as part of their core strategies.
Unfortunately, some of those companies think that they can just purchase an analytics solution, implement it, load and collect data—and poof! all their problems will be solved. There are many software solutions available for business analytics, but there is also still a need for requirements analysis to ensure the right solution is selected and implemented correctly.
Business analytics implementations need software requirements, just like any other commercial, off-the-shelf solution. There will be business requirements, functional requirements, non-functional requirements, and business rules. Requirements models help us understand how people will use the solution and help guide the configuration of the system according to the business rules.
The process to elicit and develop requirements for business analytics projects can be broken down into four steps, as shown in the following diagram. I’m going to focus on the fourth step: Define the data transformation analysis requirements. Find more information on the other three steps in the white paper “Forward Thinking for Tomorrow’s Projects: Requirements for Business Analytics.”
In an analytics project, once you have identified the data and understand how users want to consume and use the results of analytics solutions, then the main focus is on determining what analyses must convert the data into in order to get those results.
Sometimes, users just explore the raw data objects and attributes because they don’t really know what they want. However, it’s important with big data to at least zero in a bit on what kinds of problems users want to solve and what type of analysis they might want on the data. Otherwise, the analytics solution might do too much and be too overwhelming to be useful.
Good business analysts (BAs) can help stakeholders figure out what they want from the solution by brainstorming what possibilities exist with analytics solutions.
Business analytics solutions can enable future-state strategic analysis, such as exploring “what-if” scenarios. For example, “What if we could offer a new service and predict what our future sales would be for that service—before we ever deploy it? How would that be helpful?”
A good analytics solution with the right data can run models and algorithms to enable these types of data predictions. That said, prediction models and usage scenarios still must be specified in the requirements so that the analytics system can be configured correctly. Furthermore, the analyses that transform the data might require computations, statistics, or that other business rules be applied to the data prior to its being delivered in the solution.
Business analytics projects need software requirements defined, just like any other project. The approach is slightly different: Prioritize using decisions, define how information will be used, specify the data needs, and define the analyses that transform the data. When defining those analyses, we can’t assume the analytics solution will just come configured to do what we need our solution to do.
By Joy Beatty