Hiring and Retaining Scarce Analytics Talent

Promising data-minded individuals are easy to spot from a distance. But simply paying a premium to staff an in-house analytics team is typically not enough.

Promising data-minded individuals are easy to spot from a distance: curious detailed-oriented thinkers, energized by uncertainty, open to opposed opinions, and willing to work in a team iteratively to produce insightful recommendations, but still with feet on the ground.

The analytics job market is nascent, and employers compete fiercely for the best people. Sadly, salary benchmarks are unavailable or do not reflect the market since the common practice is not to pay a standard but to give a rise. Simply paying a premium to staff an in-house analytics team is typically not sustainable. What creates sustainability is a combination of things:

Brand recognition is paramount since data scientists are selective about their employers. One way of doing this is by organizing recruiting hackathon (design sprint-like events where participants of diverse backgrounds collaborate intensively over a weekend on a data-science project and winners get a job offer). Another way is releasing anonymized data to create online data science competitions (such as Kaggle.com.)

A few strategic hires, generally more senior people to help lead an analytics group, can also attract more talent. In some cases, strategic partnerships with small data-analytics service firms or even acquisitions can also help boost capabilities but need to be appropriately integrated into the organization.

But probably the most effective technique, although lengthy, is to develop talent internally. Profiles with quantitative backgrounds (e.g., BI, IT developers, and engineers) and young graduates with technical degrees can be easily converted into data scientists and engineers.

Employee turnover among data professionals can be very high, with tenures often below one year; this can seriously hinder employers' big data analytics aspirations.

Capability development is required to retain curious, analytical talent. Creating a training program for data science and engineering is the first step. Online curriculums such as Coursera, Udemy, or Udacity are becoming popular because they inexpensively provide exceptional training for beginner or intermediate students. They cover most aspects of data science (from programming and advanced statistics to visualization) and data engineering (from structured and unstructured databases to distributed computing systems). However, advanced professionals might require face-to-face and vendor-specific training.

Additionally, these online curriculums offer certification for every course, and their corporate plans allow students to take unlimited classes. However, it might be difficult to assure a high completion rate. Companies are increasingly hiring in-house trainers, sometimes from academia, who complement the online offerings and ensure students complete the courses.

It is also paramount to institutionalize a career path for data professionals. Meaningful work and career opportunities are critical for engaging and retaining all types of employees, and analysts are no exception. A proper career path should have several tracks (e.g., data science, data engineer, analytics consultant) with 3–5 steps in each way and precise transitions between the tracks as well as to other professional tracks such as marketing, operations, or general management.

Promotion to a higher rank and transition criteria might include obtaining specific certifications, participating in projects of another track (e.g., a data scientist working as a consultant), demonstrating specific skills, participating in an international assignment, becoming an expert in a particular area or having a minimum tenure in the role. For example, a typical career path could look like this:

Innovative remuneration approaches can also help to retain data scientists. For example, skillset allowances upon achieving a particular certification and progressive bonuses upon reaching key milestones, such as succeeding in the probation period of 1 or 2 years. Longer-term incentives are usually less effective for young analytical millennials.

Additionally, data professionals pride themselves on their uniqueness and look for a sense of fit with their employers. They want to work for companies that value analytics and with colleagues that appreciate and respect their unique talents. Therefore data-driven companies define company values and cultures which ignite passion, such as Unrivalled Environment for Exceptional People (McKinsey) or Every Day is Day One (Amazon).

Lastly, a sense of purpose is critical for long-term retention. Without being able to impact the organization’s success, data professionals will not find enough meaning in their work, so they will be less engaged and less likely to stay. For many, spending too much time on simple analyses and report generation quickly shrinks their motivation. How companies organize analytical talent affects whether they have access to the most meaningful opportunities.

Pedro URIA RECIO is a thought leader in artificial intelligence, data analytics, and digital marketing. His career has encompassed building, leading, and mentoring diverse high-performing teams, developing marketing and analytics strategy, commercial leadership with P&L ownership, the leadership of transformational programs, and management consulting.

Disclaimer: Opinions in the article do not represent the ones endorsed by the author’s employer.

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