Eight Key Big Data Roles (And Who Can Fill Them)

More data confirming the shortage of big data experts: a survey of Australian employers suggests 78 per cent are having trouble filling those positions. Here are the eight key roles that are needed and the background required to fill them effectively.

IT worker picture from Shutterstock

Research by talent solutions company Hudson amongst its Australian clients suggests the shortage is ongoing. "People who can blend deep technical expertise, business and analytical skills, an understanding of the market and the customer represent nirvana in terms of big data talent," Hudson Asia Pacific Mark Steyn CEO said in a statement.

"Unfortunately these individuals are in short supply. This presages a skills crisis of vast proportions and is forcing organisations to look outside the usual supply network for talent." (We noted a similar trend recently when we suggested studying chemistry was a potentially useful background for a big data career.)

As part of its research, Hudson also commissioned a report examining how to more effectively tackle big data projects. From an IT pro perspective, one of the most interesting elements in that report is a breakdown of the roles which are needed in a big data project and where those staff might come from. Here's the full table:

1. Analytics Team Lead Lead the team and define strategic direction Analytics management Ensure value creation and interface with management
2. Advanced Analytics Modeller Develop and maintain predictive models Statistics, business analytics Deliver insight through predictive models
3. Ad Hoc Analytics Analyst Respond to ad hoc queries from the business Reporting Assist the business with ongoing insight
4. Domain Expert Provide business-level experience and acumen Business Ensure relevancy of insight
5. Data Steward Standardise analytical data and encourage automation Data modelling, data warehousing Minimise data management overhead
6. Analytics Process Designer Create and enforce reusable and common processes Management consulting Increase repeatability and reduce execution time
7. Monitoring/ Validation Analyst Establish and enforce common measures within the measurement platform Performance management or finance business analytics Measure value and optimise focus
8. Deployment Specialist Ensure fast and robust model deployment Data warehousing Reduce time to market and interface with IT


    Most or those descriptions mean nothing to me. Am I alone?

      Not alone but perhaps falling behind ;)

      Each of these roles (not necessarily an FTE, depending on how big the team is and how many projects the business is throwing at them at any time) is necessary to facilitate the "Analytics Enabled Organisation" (some say data driven organisation, but I prefer the former). If you work in this space, you want to get your head around what these roles are, what they do, and how they work together with each other and with LOB heads.

    In fact if you're just starting out you would merge these roles into three FTEs as long as you pick the right skillsets, and then grow the team from there once you've got some runs on the board. The Analytics Team Lead would also cover the "Analytics Process Designer" (not even sure that this role is mandatory, it appears the most flaky of the eight); you would start with one Analytics Modeller who would also handle the Ad Hoc stuff initially and role 7; Domain Expertise would be provided by each LOB initially, and then you just need a Data Engineer who (initially at least) covers roles 5 and 8. Hence three FTEs initially as long as the Team Leader is strong and can engage the business for the needed expertise, and can recruit the broader skillsets that I've mentioned above. There would likely be a strong mentoring role for the team lead as he/she would probably be bringing the team into the Big Data and Analytics world out of a DW/BI background.

Join the discussion!

Trending Stories Right Now