Human Capital
20
Big Data ...
In this era of “Big Data”, public sector organizations will experience a growing need to recruit, retain and engage data scientists. An effective human capital system for data scientists can be developed. To be successful, a data scientist human capital system has to be designed, built and implemented with purpose and intent; a piecemeal approach based on minor adjustments to current practices is likely to fail.
The following five challenges or barriers are examples of common problems within public sector institutions. Organizations must overcome these limitations in order to allow data scientists to contribute to their mission: 1) preference for qualitative analyses, 2) stove-piped data and restrictions on data access, 3) tight control of a centrally managed capability, 4) the lack of a data strategy, and 5) delays in developing a Big Data capability compared to other organizations and the private sector.
To design a human capital system for success, the interdependent components of the talent management life-cycle have to be integrated. The eight components of a complete talent management life cycle are represented by an abbreviation, PRIDALRM: Plan, Recruit, Inspire, Develop, Assign, Lead, Retain and Measure.
In support of establishing a data scientist talent process in public sector organizations, we have developed the following list of the required actions to complete before new talent is acquired or put to work:
1. Determine the competencies / certifications required to perform the work well, and associated team members.
2. Create and pilot a unique talent acquisition strategy for data scientists.
3. Design and develop an onboarding program which efficiently propels data scientists to full contribution levels within the current culture and with the existing work conditions.
4. Budget for required software, storage and communications capabilities, and
5. Determine the appropriate data access to best meet work requirements.
Data Scientists are a Big Deal. There is a consensus that the organizations that figure out how to analyze petabytes (i.e., quadrillion bytes or a million gigabytes) of data will make leaps in creative analysis and productivity. Organizations “that leverage workforce analytics effectively will win the war for talent," says Michael Capone, chief information officer at software giant Automatic Data Processing Inc. The bottom line is that while the potential for analyzing large amounts of data is tremendous, the tools required to do so effectively are still under development and the storage requirements are expensive and cumbersome.