Similar to how engineering and science work together, analytical engineering is the practice of structuring and applying data science approaches in solving real world problems. Since most business problems are multi-faceted, it is important to have a structure of decomposing these problems into pieces that can be solved using data science techniques. By starting with business and data understanding, and a determination of the problem that is to be solved, the work of solving the different pieces of the problem can then be followed up by combining the individual solutions into a cohesive solution that is focused on the business problem.
This analytical engineering approach can be helpful in working with data scientists because often they do not have the business expertise to understand what knowledge should be extracted (Provost and Fawcett 2013, 279). A leader can understand the business need and break up a defined problem into pieces that a data scientist can then help solve. It also allows a leader to understand the business constraints on the project to make an evaluation on the costs and benefits. This ensures the solutions will be focused upon solving the business case specifically. As an analyst, this technique removes a lot of the guess work in how to solve a complex problem. This method also allows analysts to focus on understanding each sub-project element and communicating the constraints and benefits to leadership, allowing for a higher chance of success for a project. Having a structured analytical engineering approach for solving business problems can ensure roles are clear and solutions are optimized.
Author: Logan Callen
References
Provost, Foster and Tom Fawcett. 2013. Data Science for Business. 2nd Edition. California: O’Reilly Media, Inc.
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