Data is the lifeblood for intelligent companies today. Organizations are leveraging data to make smarter, faster decisions, and drive greater business outcomes. The benefits from data are obvious: competitive advantage, reduced costs, new services, new revenue streams, and enhanced customer experience, to name just a few.
Companies are rushing to take advantage of data and maturing new technologies that move, mine, and consume increasingly diverse data from an ever-larger array of sources. AI, machine learning, and advanced algorithms are enabling organizations to unlock tremendous value from data and drive outcomes sooner, with greater impact than anyone ever imagined possible.
Traditionally, business analysis has focused on requirements management— eliciting information from stakeholders, analyzing requirements, and designing a solution. Today, Business Analyst professionals need to work together with the business and the data science team to extract value from data. To be able to do so, they need to first move from a data-apathetic approach to a data-driven one.
Then, they need to familiarize themselves with data science tools and technologies, including machine learning, data virtualization, and predictive analytics. They should know the possibilities offered by technology; be able to evaluate its utility, applicability, and benefits in specific business situations; and elicit and communicate requirements in a very creative manner to implement a solution for a business problem.
OSEMN: Solving problems like a data scientist – Business Analyst professionals can benefit from a proven methodology called OSEMN,5 which data scientists use to solve data problems. This “awesome” technique—OSEMN rhymes with awesome—requires a Business Analyst professional to go through the following steps when working on a data problem
Below is the summary of the various activities a Business Analyst professional would be involved in while following the OSEMN framework. Understanding the organization’s goals and translating it to a data science problem through various analysis techniques and ensuring that the right problem is being addressed is crucial. Also, providing the necessary business context to discover the right data for insights needed by the decision makers, and communicating the trends and patterns uncovered through the algorithms in the form of a visual story that is easily understood by a broad set of stakeholders are critical elements.