Table of contents for Building Business Capability 2016
- BBC 2016: Delivering the Digital Dream for Real
- BBC 2016: Application Development Innovation at Kaiser Permanente with Decision Management and Cognitive
- BBC 2016: We got the Funding for Maturing our Business Rules & Process Management Capabilities – Now What?
- BBC 2016: Enabling Operational Excellence
- BBC 2016: Business Analysis for Data Science Teams
- BBC 2016: Pioneering Decision Services with Decision Modeling in Healthcare
- BBC 2016: Beyond Textbooks: Building the Modern Business Architecture
- BBC 2016: Decision Modeling for the Business Analyst
- BBC 2016: BPMN, CMMN, DMN: The standards every BA should be aware of
An additional blog post here on a session at Building Business Capability that I missed – Business Analysis for Data Science teams. I know Susan Meyer who presented it and we talked several times about her presentation. It’s a really key topic so I wanted to present a summary. Here goes:
There is a lot of interest and excitement right now around data science (data mining, predictive analytics, machine learning). But this is not just a gold rush so much as a real indication of a change. With more and more interest in this topic and a need for companies to use data science effectively, Susan sees a key role for business analysts (as do I).
She points out correctly you don’t need to be a mathematician or statistician to contribute to data science teams. A business analyst who understands the process and has some disciplined curiosity can do a lot. And the process you need to know is CRISP-DM – the Cross Industry Standard Process for Data Mining (see these blog posts on CRISP-DM). This is an ideal process for business analysts as its iterative, begins with business knowledge and allows non-data scientists to be part of the team. She identifies 6 reasons business analysts can help:
- They know their vertical and domain
- They are used to agile, iterative projects
- They understand their company’s business model
- They can elicit requirements through data – and build a decision model to frame the analytic requirements
- They can partner on the architecture (especially for deployment and data sourcing)
- They can build and manage the feedback loop and the metrics involved
Business analysts can dramatically reduce the time spent on data and business understanding and improve the results by anchoring the data science in the real business problem.