Three Mistakes People Make When Hiring a Data Team (And How to Avoid Them)

Renny McPherson
Aug 9, 2017 · 4 min read

By Renny McPherson

By now, most enterprises understand the value of having a data strategy. But finding, vetting, and hiring data professionals to execute that strategy can be difficult.

For the past half decade I’ve recruited data talent, both full-time and project-based, against better resourced companies. Five years ago, I co-founded a venture-backed data analytics product company. Now I run a data analytics firm that works with leading asset managers. From these experiences, I’ve learned plenty of lessons, and quite a few the hard way. Here are some common mistakes based on my own experience and that of colleagues, clients and friends, and how to do better.

Mistake #1 — Creating job descriptions in a vacuum

It is amazing how often someone writes a job description or puts a staffing plan together without consulting other areas of the organization. When a business person writes a job description for a data hire, it is often either a copy/paste of someone else’s past job description, or woefully off. When a data practitioner writes a job description, too often it lacks a tie back to business value. When determining and codifying the skills and experience you need on the team, having buy-in from the technical and business sides is crucial, and often it just doesn’t happen.

What to do instead: Get on the same page

Business people and data people need to work together to get the most out of any hiring plan.

● If you are a business person, work with a data person to define the types of people you need to hire.

● If you are from the data side, ask a business person for help crafting the job description.

● If you are a business person without a data person on staff, and you’re trying to build a data-centric capability for your small business, startup, or large enterprise, you would do well to talk to a data advisor who can provide impartial advice on the hire, or work with a search firm if you can afford it.

Mistake #2 — Making assumptions about data professionals’ incentives

In many verticals, and particularly in financial services, firms are competing hard for top-tier data talent. Many have already hired and then lost or fired multiple cohorts of data pros.

Many financial services firms still have trouble articulating to data professionals why their company’s vision is compelling and how the problems they solve are worth switching jobs for. The attitude is more, “you would be lucky to join us.” While many finance professionals prize working at particular asset management firms, data scientists do not always have the same incentives or values. Simply offering them a twenty-five or even fifty percent pay increase won’t work.

Take the Washington, D.C. area, which is rich with data engineers, data scientists, data visualization specialists, and software developers working in the U.S. Intelligence Community. These folks already have stable, challenging, well-paying jobs. In my experience, many of them are mission-driven, want to solve important, challenging problems, and do not want to take on unnecessary job risk. So, figuring out what you can offer them that nobody else can is paramount.

What to do instead: Focus on culture

Recruiting top talent means articulating why people should care about your mission, and why joining your team means doing something of tangible value. Be clear about why you provide compelling, challenging work.

At the data analytics product firm I co-founded, we were honored that our first few data science hires chose our small security analytics company in Baltimore, MD, against offers from much better resourced Silicon Valley titans. After they accepted, we asked them why. It turns out that while larger companies have brand recognition, catered lunches and high salaries, our small team’s connection to and passion for the work stood out. Find your authentic voice and let people know the kind of work they’ll encounter at your firm. Tell them why that matters to you and ask them what matters to them. It will pay off.

Mistake #3 — Doing it on the cheap

Many companies invest too little in hiring the right data team, and then are surprised when the value created doesn’t match their expectations. These firms typically fall further behind their industry peers in data analytics capability. Investing in well-qualified talent is a must if you want to invoke a better data strategy: there aren’t enough data professionals to meet overall demand.

What to do instead: Invest in long-term relationships

Establishing and sustaining authentic relationships with communities of data talent is the most important step you can take to build a pipeline of data professionals. Here are four practical ways to build for the long-term:

● Get to know the technical departments at local universities — they can be tremendous sources of talent. As we grew our data analytics firm RedOwl Analytics in Baltimore, we established a strong relationship with technical departments at Johns Hopkins, which not only resulted in staying current on trends in data science but also yielded several strong data science hires for our company.

● You don’t have to live in Silicon Valley to find robust, active communities of data analysts and engineers. Sponsoring a data meet-up locally is a low-cost way to connect with data practitioners in your corner of the world.

● Don’t forget about the ever-growing remote workforce — project-based professionals who can easily plug into your data analysis capability thanks to modern communication and workflow products, that are particularly good for technical roles.

● Reach data professionals in a low-cost but personal way — for example, sponsor a data-focused podcast and ask listeners to check out your landing page. On that page, explain why your company will offer unique challenges and great opportunity to data talent.

TDB — The Data Business

TDB — The Data Business is a collection of ideas about the intersection of data and business. The data team, business team, and technologists ofThe Twenty write TDB. The Twenty powers businesses’ use of data analytics to drive growth and profit.

Thanks to Joe Flood

Renny McPherson

Written by

TDB — The Data Business

TDB — The Data Business is a collection of ideas about the intersection of data and business. The data team, business team, and technologists ofThe Twenty write TDB. The Twenty powers businesses’ use of data analytics to drive growth and profit.

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