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Building Your Big Data Analytics Staff

Finding analytics talent is increasingly difficult, so companies are looking to build skills from within. Here are strategies you can use in your own enterprise.

An article in the October 2012 issue of the Harvard Business Review declared the data scientist "The Sexiest Job of the 21st Century". Since that article was published, the demand for the data and analytical skills required to perform the data scientist's duties have become increasingly sought after by employers.

A McKinsey Global Institute study estimates that by 2018, there will be a shortage of 140,000 to 190,000 people with the necessary analytical expertise, and a shortage of another 1.5 million managers and analysts with the skills to interpret and make decisions based on analytics. With this shortage in mind, it is extremely important for an organization with big data needs to foster a data-driven, analytics-based corporate culture and to have a strategy for developing and managing analytics talent.

According to D.J. Patil, the chief data scientist of the United States Office of Science and Technology Policy, a data-driven organization "acquires, processes, and leverages data in a timely fashion to create efficiencies, iterate on and develop new products, and navigate the competitive landscape."

Amazon is just one example of the data-driven organization. When interacting with, end users are presented with (and often sold) similar or complementary products based on their clickstream data. Behind the data are strategic algorithms and analytics. Data-driven organizations use their data to make better decisions and to create a competitive advantage.

Another characteristic of this culture is a management team that provides evidence-based management driven by data rather than by intuition. Employees are encouraged to challenge the status quo and to look at new approaches to existing problems. This culture includes technological and analytical skills development to lay the framework for creating and sustaining an analytics culture.

For companies to maintain the competitive advantages their analytics enable, they must meld analytics expertise with their existing business knowledge, write David Kiron, Pamela Kirk Prentice, and Renee Boucher Ferguson in "The Analytics Mandate." The analytics culture ensures that analytics actually provides value instead of just promising the possibility of value.

The analytics culture lays the foundation for the analytics talent management strategy. The lack of skilled data and analytics workers necessitates a strategy for obtaining analytics talent. A McKinsey study found that 15 percent of operating-profit increases from analytics projects were linked to the hiring of data and analytics talent. With this talent in place, analytics can be used to address current business challenges and to develop new applications.

It can be cost prohibitive for an organization to staff solely from established data scientists. As an alternative to recruiting data science "unicorns" with the full gamut of technical skills already necessary for the job, many companies look to groom analytics talent from within. There will always be a need for data scientists who are versed in the latest analytical techniques. That said, the existing business knowledge in an organization is extremely valuable. An employee that can add a set of analytics skills to their existing business knowledge can help bridge the gap between purely technical and purely business employees. Also, with so many companies vying for analytics talent, it makes sense to take a long look at your staff to fulfill analytical staffing needs.

Along with how and where you'll hire the necessary talent is what set of roles and skills are necessary to handle big data analytics projects. There are no absolutes in terms of job responsibilities, but there will typically be some combination of a project manager, data scientist, and data engineer. In a small organization, there may be overlap in these roles in the early days of staffing. The project manager will have experience managing quantitative projects. This manager will be capable of coding but won't be required to do so.

Additionally, the project manager will understand quantitative algorithms. As for the data scientist, this person has a quantitative background as well and will be an experienced researcher and communicator. The data scientist will usually have a mathematics or computer science background. However, anyone that's savvy with statistics or that has conducted formal research could be a good candidate. Finally, the data engineer will understand software engineering, including data structures and algorithms. (For more, see

When building a big data analytics staff, be conscious of hiring employees who have the skills that align with your company's needs. Data science and analytics curriculums in universities are becoming popular, but there's not a standard curriculum such as those seen in engineering schools. Consequently, part of the talent management strategy is to identify specific skills and experiences that are aligned with company needs.

The dearth of big data analytics talent necessitates a talent management strategy for any company that plans to have big data or analytics as a core competency. This strategy should encompass a plan for hiring external staff, training or growing internal staff and cultivating a culture conducive to analytics and data-based decision making throughout the organization.

About the Author

Chuck Currin is the principal data architect at Mather Economics. Currin is responsible for understanding emerging and evolving data technologies and translating business requirements into a solutions and data architecture that maximizes ROI. You can contact the author at

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