Big data are a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.photo: JD Hancock
So say the opening sentences of the “Big data” article in Wikipedia. The people primarily responsible for conquering those challenges are data scientists. Being a data scientist these days is rather like being a Renaissance person; one must possess knowledge of and competency in a wide variety of subjects related directly and indirectly to the fields of mathematics and science.
Fortunately, data science has a number of sub-specialties to share the load. Understanding–defining–who does what (capturing, curating, storing, searching, sharing, transferring, analyzing, visualizing, processing) and why they do it means companies building data science teams can intelligently choose the areas of specialization that will best serve their goals.
Five Roles You Need on Your Big Data Team
Of course there’s the data scientist, the coveted knight in shining armor who visualizes models and creates (and continuously optimizes) sophisticated algorithms to transform data into something useful. But she could not do her part to fulfill corporate expectations without the support of equally coveted
- Data hygienists, who deal with the “dirty data” problems inherent in collecting data so the data is clean now and stays clean in future.
- Data explorers, who burrow into all the data a company collects to determine what, if anything, can be done with it, including how data originally collected for a different reason might be repurposed.
- Business solution architects, who structure and organize data so it’s properly updated and where it needs to be within the necessary timeframe of every query–a critical feature of today’s data science when queries are ‘answered’ in real-time.
- Campaign experts, who, with an in-depth understanding of both the technology and marketing, can turn the knowledge derived from data into insight and then into advice.
Assembling a powerful data science team, whether that team is internal or third-party, is necessary to applying big data tools. However, success rests as heavily in the hands of the right corporate culture as it does in the right specialized people. The best solution? Welcome reevaluation, innovation, experimentation, and keep the focus on the end game.
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