Promising data-minded individuals are easy to spot from the distance: curious detailed-oriented thinkers, energized by uncertainty, open to opposed opinions and willing work in 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 benchmark but to just 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:
Improving brand recognition is of paramount importance since data scientist are selective about their employers. One way of doing this is 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 an 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, or even acquisitions of, small data-analytics service firms can also help boost capabilities, but need to be integrated properly in 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 from technical degrees can be easily converted into data scientists and data engineers.
Employee turn-over among data professionals can be very high, with tenures often below 1 year; this can seriously hinder the big data analytics aspirations of employers.
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 are an inexpensive way of providing exceptional quality 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 trainings.
Additionally, these online curriculums offer a certification for every course and their corporate plans allow students to take unlimited courses. However, it might be difficult to assure a high completion rate. This is why companies are increasingly hiring in-house trainers, sometimes from the academia, who complement the online offerings and make sure 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 track and clear 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 certain certifications, participating in projects of another track (e.g. a data scientist working as a consultant), demonstrating certain 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:
- Data Science track: data analyst >> data scientist, senior data scientist >> principal data scientist >> chief data scientis
- Data engineering track: data engineer / architect >> senior data engineer / architect >> principal data engineer / architect
- Consulting track: business consultant >> project leader >> consulting principal
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 the probation period of 1 or 2 years. Longer term incentives are usually less effective for young analytical millennials.
Additionally, data professionals pride themselves in 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).
Last but not least, sense of purpose is key for long term retention. Without being able to make a real impact on the organization’s success, data professionals will not find enough meaning in their work, and so they will be less engaged and less likely to stay. For many of them, having to spend 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.
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