by Angela Guess
Miles Johnson and Sam Hochgraf of IBB Consulting Group recently wrote in InsideBigData, “Look no further than the sports world for proof that just having all-star talent doesn’t always guarantee success. They must work together cohesively, have a solid strategy and be organized in a way that plays to each member’s strengths. Data science teams are no different. Companies are tirelessly hiring the best and brightest to support big data efforts. This is crucial. But to get the best ROI out of your team and achieve maximum effectiveness for your organizations, there are other key considerations. Every company’s needs will vary. But many share common goals that can be more effectively achieved with the right structure and roles.”
They continue, “In IBB Consulting’s experience, a business-facing data scientist supported by a data science product manager and a lean team of domain experts maximizes return on data science investment. Consider the following roles, responsibilities and processes for your data science team… Your data analyst leverages deep vertical experience and an intimate understanding of the data that leads to the right questions. This role is responsible for the “ah-ha” insights that drive enterprise-wide decision making.”
They go on, “The role of Data Management and QA is to evaluate the integrity and usability of new data sources and account for privacy policy and boundaries on data usage. This role manages enterprise risk and establishes protocols and definitions for knowledge share and reuse that enable the data science team to iterate quickly without questioning how data may be used. In many organizations the scope of the QA team is too broad to provide targeted analytics testing. Because a single data quality issue in a base dataset can render results of derived models meaningless, it’s important to have specialized QA that understand the data and collaborate with data scientists and engineering throughout the modeling process.”
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