It’s no secret that the topic of talent is a hot one.
It underlies conversations among dozens of executives, and has been growing in importance as we understand the enormous impact that Analytics will have on our future in shared services. To have the skills and acumen needed, you'll likely need more people than your team has today. And that need extends far beyond just a modest supply of IT and analytics professionals.
Companies are starting to crack the problem through various enticing compensation strategies and creative recruiting. But, whether they know it or not, what companies really need are more “translators”—people whose talents bridge the disciplines of data, analytics, and business decision making.
These translators can drive the design and execution of the overall data-analytics strategy while linking the business-unit with the analytics team. Without such employees, the impact of any newly applied data strategies, tools, and methodologies will be nothing short of disappointing.
Usually, companies seek and settle on individuals who combine two of the three needed skills. The data strategists’ combination of IT knowledge and experience making business decisions makes them well suited to define the data requirements for high-value business analytics. Data scientists combine deep analytics expertise with IT know-how to develop sophisticated models and algorithms. Analytics consultants combine practical business knowledge with analytics experience to zero in on high-impact opportunities for analytics.
The translator (otherwise known as a “unicorn”) in data analysis is the one that can do the number crunching but also have the soft skills to stand in front of an executive and convince the business that the data is right. Someone who can build up trust, so they say “Okay, I believe the math, and I believe you.”
It’s the old battle of the left brain vs. the right brain, and finding the balance between the two.
To get a good understanding of the current landscape, and how to attract and retain these so-called unicorns, I spoke with Sam Pritchett (Senior Manager Data Sciences) and Zach Zivkovich (Senior Business Intelligence Analyst) at VMWare. Sam does the Data piece, and Zach does the Business Intelligence, so, together, they were able to give much needed insight into the topic of talent as it relates to analytics in shared services.
Sam and Zach have seen their VMWare grow from 7,000 to 27,000 in a little over 4 short years, their teams playing a critical role in helping VMware keep pace with the rapid growth—and prepare for more.
In light of their rapid change, and the major successes they’ve had in creating their teams and fostering successful relationships with IT and the business, they had a lot of solid advice for hiring and retaining this special talent pool.
What do Sam and Zach look for when first interviewing data scientists and analysts for their team? Someone who:
• Likes to solve puzzles, take on difficult challenges, and feel like they have accomplished something
• Feels their input to the business makes a difference
• Believes the work they're doing has a significant influence at the company
Sam says, “Analytics can be referred to as both an art and a science. There’s a lot of truth to that. When I look for skills in an analyst, it goes beyond the technical ability of working with numbers. Business expertise, problem solving, and interpersonal skills are actually more important than you might expect. I even have a spreadsheet that I use for scoring resumes that gives preferential weighting to categories like Leadership, Creativity, and even one called Unusual Qualifications.”
For the ideal candidate, it’s not going to be what you might think: someone fresh out of college, coming in green. While they don’t have to have a formal analytics background, be a programmer, or have done report and BI work, it could be someone who has done regular analyst-type work, slightly related, but not exactly an analytics role. The ideal candidate will have more than 5 years, less than 10, and, importantly, be someone who like to do puzzles.
A widespread observation I’ve encountered among our sharedserviceslink network is that the usual sources of talent—elite universities and MBA programs—are falling short. Few are developing the courses needed to turn out people with these combinations of skills. To compensate, and to get more individuals grounded in business and quantitative skills, some companies are luring data scientists from leading internet companies; others are looking offshore.
Zach notes, “For a hire in the Director or Manager level role, you need to obtain a skill set that skews more towards the business, as that person will do much more interactive work, say for example when the business comes to them with a problem.”
When looking for the ideal candidate at VMWare, Sam uses the candidate matrix, a simple 1-10 rating system. As resumes come in, he scores them, without thinking too hard about the resume. This is gold for anyone who needs help getting started thinking about hiring criteria and where to begin when developing a roadmap of what they imagine their team to be.
Taking a look at the matrix, things to keep in mind, according to Sam:
- Based on the role I’m hiring for, I modify the weights to the scoring. For example, if I’m looking for a manager or director, then I’ll place less emphasis on technical and more on leadership.
- As resumes come in, I give them an initial score on the sheet just from the document. I also double check the weights after a few resumes to make sure it makes sense.
- After I see enough resumes, I rank them in terms of overall score and talk to the people at the top. It’s a subjective assessment of how many to talk to.
- During interviews, I update the scores. I’ve had candidates go up or down.
- Ranking, again, determines who is best in the group. Extending an offer is easier to figure out from a ranked list. It might not exactly match the rank, but it’s a great guide.
If what the persons skills are is not readily apparent to Sam, this matrix helps to get a sense of it.
In the business acumen section, he picks up on how well he gets along with the person. If they don’t "click," they’ll score lower on the communications piece.
To whom these new types of recruits report depends on the organization.
At VMWare, it’s Finance.
Zach explains, “You have to really think in terms of output and not mechanics, because the team is really going to be doing business output, for the business. Almost like shared services, you probably want to place them in a term that's more relevant to someone who will consume their services. This would ideally be a finance part of the organization instead of an IT group. VMWare’s data analyst team has an awesome relationship with IT, and that partnership is key to success.”
What’s the biggest mistake people tend to make?
Sam says, “A lot of people try to just really hire an analytics rockstar, but they will be unhappy because they are on an island, and typically those are the types of people you won't find a lot of places, anyway. First, you need to hire the person that builds the team up and knows what it takes to be successful. This can be hard for a lot of companies, because they won’t have modules built for 6 months to a year.”
The management and retention of these special individuals requires changes in mind-set and culture. Your first priority is to provide the space and freedom to stimulate the exploration of new approaches and insights. At times, you may not know exactly what data scientists will find (or even what they are looking for), but it’s all part of the process to provide more latitude for innovation and creativity. Another priority: create a vibrant environment so top talent feels it’s at the cutting edge of technology change and emerging best practices. Stimulating engagement with the data-analytics ecosystem (including venture capitalists, analytics start-ups, and established analytics vendors) will be important.
When first building up the team at VMWare, a few core principles were pillars to their success. They knew that every strength is a weakness and every weakness is a strength, and it wasn’t going to get done without help from every group of the business.
Zach noted something that newly developing teams should truly consider: “Allow the team to spend time without any defined responsibilities, so your team can explore and say, “what is the biggest pain point at this company?” Then allow your team the creative flexibility to actually go and work on solving that pain point. You’ll be amazed at the results.”
The downside of having a team up and running, and then hiring data analysts on, is that since everyone has a business problem, they don’t have the time to focus on the biggest business priorities.
For the right candidate, the problem will be the selling point. This is what you can do to help.
Remember, if you need people to help you out, the worst thing you can do is hire someone with really good expertise or data analysis skills related to natural sciences, when the issue in the business is actually in sales. You, as the person hiring this talent, need to understand what pain point you’re looking to solve, as well.
One of the most surprising and important things there was to note, was that an organization may start making an investment into people who are data scientists, while there are still parts of the organization that struggle with Microsoft Excel and have to be taught things like pivot tables.
When companies start to think about fully exploiting their data, they need to think beyond using a small group of rockstars, and think about the skills of average employees.
During my conversation with the VMWare team, we also touched on the broader question of whether there should be a basic training at organizations. This training, preferably done within the new hire's first weeks (in any role at the organization), would give a basic level of understanding about what vocabulary to use when asking questions in the organization. It would also help with things like Microsoft Excel, to ensure everyone could fulfill simple requests such as the creation of a pivot table.
Whichever path you choose for creating a data analytics strategy, it should start with pilot efforts and clear rules for making “go/no-go” decisions for the shift from exploratory analytics to what you’d hope to be a full-scale rollout. Some believe that if new data analytics models don’t end up being predictive enough to deliver the desired impact, it’s better to shelve them before they become investment sinkholes and undermine organizational confidence in analytics. At the same time, executives need to be willing to convey to the organization that the failure of some analytics initiatives to materialize is nothing to worry about; in fact, this is the reason for pursuing a portfolio of projects. The combination of success stories and hard-nosed decisions to pull the plug where appropriate will create a climate where business units, functions, top management, and frontline employees can embrace the transformational possibilities of data analytics.
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