Talent acquisition professionals and hiring managers are facing several headwinds, especially when it comes to hiring and retaining data analytics professionals. The traditional challenges are finding the right allocation of challenging and inspiring work, providing compensation and growth opportunities, and offering work-life balance. There are few professionals whose dream position wouldn’t entail competitive, above-market compensation, inspirational work and a 40-hour-or-less work week. Unfortunately, this is infrequently the reality.
The perfect job seldom exists, and neither does the perfect candidate. Every data analytics candidate has their unique thumbprint of values that will lead them to accept an offer and to remain at an organization for several years. However, as an analytics professional grows both professionally and personally, that thumbprint can change over time. One candidate may stay in a position for regular raises and growing compensation; another may find meaning in their work; and yet another may remain in their position for the clear expectations and work/life balance.
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There is no panacea to guarantee that top talent will stick around in an organization. That said, there are some steps hiring managers can take to find the right candidates and boost retention.
Hire smart people and teach them skills
Using case studies rather than a skills checklist effectively uncovers elements of quality in the hiring process.
One of the most common pitfalls and inefficiencies that we see in the analytics talent market is an overemphasis on certain software or platform skills. Skills such as Python, Jira, Github, Snowflake or others are common within job descriptions, but there’s no real indication which are the highest priority or which are going to immediately apply.
This can both discourage highly qualified candidates from applying because they are missing expertise in just one of the listed skills, or conversely, encourage candidates to over-list skills on their resumes and CVs based on general familiarity or cursory coursework.
For hiring managers, a skills section on a CV that lists off dozens of software, languages or applications is effectively meaningless without either a cover letter that discusses how those skills were used to solve a problem or similar elaboration in the responsibilities or accomplishments section. Moreover, most candidates will be sustainably engaged and excited about a job that gives them an opportunity to learn and grow rather than doing the same work for slightly increased pay.
The most effective way to screen for the right analytics talent is to use a variety of case studies. This can include:
- As part of a technical pre-screen, recruiters could ask candidates to submit a response to a business question involving provided data. This can help weed out the volume of applicants that result in progression to a live interview by as much as 75%.
- When conducting live interviews, consider asking candidates to describe a recent dataset they’ve used, and then ask them how they would use a variety of the tools they’ve listed to solve business problems on their “home turf” with a dataset they’re familiar with. A candidate who has demonstrated capacity to solve the problem is going to pick up the syntax and functionality of the related tools very quickly. On the other hand, someone with years of experience coding using a specific tool may be great for a specific purpose but may not be successfully fungible on other types of projects.
Using case studies rather than a skills checklist effectively uncovers elements of quality in the hiring process, which ultimately makes the experience better both for the employee and the employer.
Rethink performance management and define value-add metrics
Another major issue with analytics talent retention is ineffective and misguided performance management. Analytics professionals enjoy a market of rapidly increasing wages, and it’s not uncommon to receive double-digit (10%+) raises every year for the first decade of their careers.
It’s unreasonable for organizations to accept this and just offer blanket raises based on skill or function. At the same time, a candidate who has just one or two years of familiarity with an organization’s data is much more valuable than a newcomer.
Companies should be mindful of how easily such investments can go awry and lead to businesses starting over repeatedly with new pools of talent because they aim to artificially meet certain performance ratings or wage distributions. Commonly, managers of analytics professionals are not analytics professionals themselves and are not qualified to opine on their technical development and non-behavioral aspects. However, anyone in a performance management position should be able to define, measure and articulate value, regardless of their reports’ functions.
Questions to ask include:
- Did the professional make a manual process more efficient and save the organization time?
- Did the professional develop tools to cover more ground and increase the quality of work products while reducing risks?
- Did the professional uncover actionable insights that influenced business decision-making?
A paradigm that measures these types of value-add components is far more valuable than one that simply measures productivity. If there aren’t defined value-add metrics prior to hiring an analytics candidate, organizations should consider postponing until there are.
Be flexible with hybrid and remote work
Most organizations winning the war on talent appreciate “face time” just as much as they do flexible working conditions.
The reality is that not every analytics role needs someone to be present in an office. Prior to the 2020 pandemic, managers of technical personnel, including data analytics professionals, frequently oversaw remote offshore teams in a follow-the-sun solutions delivery model, which allowed this function to effortlessly transition to a remote work paradigm. Loading and profiling data, generating reports and developing dashboards can be done virtually anywhere.
Similarly, employees need to have reasonable expectations when working far away from the people who can heavily influence their careers. A degree of self-measurement and upward management is required to ensure that recurring touchpoints with management focus on articulating visible value. Companies should reflect on their historical insistence on frequent presence and take time to develop dynamic career progression and compensation models that accommodate both remote and onsite professionals. This is where the real work is in successfully attracting and retaining quality talent.
For companies of all sizes, hiring and maintaining the right talent is a challenge. Data analytics can be particularly challenging given the nuanced skillsets it requires. Organizations seeking to come out ahead should take a good look at their interview tactics, performance management processes and flexibility in hybrid working models to help yield long-tenured, quality staff across the board.