Traits of Successful Data Practitioners

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You’ve already filtered for the best candidates with the appropriate skill sets that you need to execute on your data strategy. Now how do you filter for the right people for your specific organization in a sea of technical talent and experience? How do you find the je ne sais quoi of stellar data practitioners?

There are certain traits and characteristics that make some folks stand out above the rest. In data, this is especially true. If you’re lucky, you may have worked with great data practitioners in the past. What was it about them that made them so great? 

We have compiled the traits most commonly associated — and least talked about — with stellar data practitioners so that you, too, can maximize your chances of hiring candidates who will have an outsized impact on your organization.

What they do now is less important than what they can do in the future

Hiring managers can sometimes be rigid about the way in which they evaluate candidates. Do they have specific experience with the industry? Do they know specific coding languages? Though sometimes having a specific skill or experience with a specific tool is necessary, a candidate’s ability to learn the necessary skills and tools to do the job well is what you want to evaluate. This is especially true in data given how dynamic the data world is today and how quickly things are changing.

Candidates will inevitably need to pick up skills on the job, and having a foundational set of skills and a great attitude are the best indicators of being able to do so. Candidates with some kind of STEM background are preferable given the technical nature of data roles, whether this background is in their studies or prior work experience. Also, candidates who have demonstrated learning on the job in the past are likely willing and able to learn on the job again.

Make sure you’re looking for someone who will ebb and flow with all of the changes we’ll inevitably see in the data space in the future.

Soft skills are more important than hard skills

All roles require a combination of hard and soft skills, and this is especially true in data:

Unlike hard skills, which can take education, experience, and years to master, many soft skills can be hard-wired and a part of who we are. For this reason, soft skills can be significantly more difficult to teach or change, so it’s oftentimes more important to ensure your data hires have the appropriate soft skills versus hard skills.

As with most things, there is a spectrum, and soft skills come in a variety of flavors. Though skills such as collaboration and communication fall into this bucket, there are others that are potentially even more important, such as curiosity, creativity, and humility. Someone who is constantly trying to figure out why — and doing so in creative ways — will go further than someone who doesn’t feel compelled to dig deeper. Someone who tries to reap as much as they can from data, especially when it is imperfect, will always find more insights than one who stops when they’ve exhausted traditional methods they’ve learned.

A soft skill that is invaluable in data candidates specifically is business savvy. Because data is so central to an organization and applies to every part of it, someone who can understand the business context and prioritize their work within that context will be in the best position to move the needle. Hiring a technical superstar will not guarantee that you solve real-life business problems. 

Further, having business context will enable your data team to proactively identify problems and questions and surface those back to the business, making the data conversation more of a two-way street. Being in a data role means you’re closest to the data and deeply understand it. When your data team is empowered as a business partner, it increases the whole organization’s understanding of the data.

Humility to know when and how to get help

As we’ve mentioned, data is a dynamic field that changes quickly. There’s always something new — new data, new tools, new information — and it’s impossible to keep up. Further, data is a vast field, and it’s unlikely one person can master all of it. Therefore, data practitioners will inevitably have questions and not know how to do some things, a fact that has forced data to be a rather collaborative field. 

As you’re evaluating data candidates, you’ll want to understand whether they can identify when they need help in the first place, and how they go about getting the help they need.

“Help” can take a lot of forms in data, including:

  • Asking for clarification of a question or ask
  • Collaborating with someone who knows more about a topic rather than getting stuck in the problem
  • Sharing work early and often to ensure they’re going down the right path
  • Crowdsourcing work for feedback and suggestions
  • Researching the latest methods or best practices to ensure their solution is the most effective

Of course, some struggle is good. Asking for help too early not only hinders one’s learning, but can also result in unnecessary reviews and revisions from team members and leaders. 

Striking the right balance is an art, and phenomenal data practitioners know this all too well.

Track record of execution is key

Another skill that many may not ask or expect of data candidates is the ability to execute, or taking a business problem and getting to a solution that has a meaningful impact. This can be tricky to screen for as it manifests in different ways for different roles. For example:

  • Data Engineer archetype: A track record of strong, underlying transforms and effective data quality monitoring
  • Data Analyst archetype: Curating a narrative that effectively combines the most relevant data with business context, enabling the team to make decisions

A common misstep of data practitioners — even those who have been successful executors in the past — is slipping into a data rabbit hole. This is especially dangerous when someone has a strong depth of knowledge or the data is interesting. Those who know how to focus on driving business value and identify and avoid unimportant rabbit holes will inevitably outperform those who get lost in the work.

Requesting examples of how the candidate has improved the business in the past is a great place to start.

Get to an answer and keep digging until you fully understand it

It can be tempting as a data practitioner to get to an answer, present it, and  move on. However, data is rarely straightforward:

  • Insights are rarely about a single answer and are more so about what the answer comprises
  • Transforms are not just a way to aggregate data, but also simplify it
  • Packaged models can be relatively easy to use, but understanding what the model is doing and why is far more powerful

The data practitioner who digs a few levels deeper will not only learn more, but is also likely to identify more opportunities along the way. One way to detect a data practitioner’s level of investment is to ask probing questions. Are there edge cases within the transform that have not been solved? Are there important segments of the population that skew the data? Are there outliers that are impacting the output of a model? 

Finding someone who proactively answers these types of questions is what you’re looking for.

Focus on the big picture while living in the details

Focusing on the big picture is necessary to deliver business value, but the devil is oftentimes in the details in data. From data issues to business insights, details can expose great findings. However, you need to actively look for them.

Digging into the details goes further than researching outliers. It can mean working closely with Design and Engineering teams to understand flows and measure how well the generated data matches the expected data. It can also mean spending multiple weeks on a single important transform to make sure all of the edge cases are considered and built correctly.

A great data practitioner knows when to focus on the big picture, when to dive into the details, and how to seamlessly move across this spectrum.

Data talent can exist anywhere

Many folks who end up working in data did not spend a lot of time earlier in their careers in a “typical” data role for a variety of reasons, including:

  • Data roles have evolved more rapidly than most in the past decade thanks to tools and resources that have made data more accessible to organizations
  • Data role nomenclature is poorly defined and inconsistent across industries and organizations (see archetypes of data roles)
  • Data work is oftentimes included in the responsibilities of non-data roles

When considering applicants for data roles, it’s important to understand a candidate’s journey rather than his or her specific roles and titles. Perhaps you find an amazing operator who has great business skills and loves working with data, or a business-savvy engineer who wants to better enable organizations to leverage data. 

If you’re willing to search outside the box, you will generally be rewarded. An added bonus is that bringing in folks with experience in other roles can make your data team stronger, as they:

  • Bring a diversity of skill sets, experiences, and perspectives that can complement and uplevel other team members
  • Bridge data gaps across your organization through their ability to connect, collaborate, and build strong relationships with those to whom they relate outside of data

Alignment

Perhaps the final thing you should consider once you find the right people for your data team is whether your organization is a place they want to be. Regardless of their experience, backgrounds, and skills, the most important consideration is whether they’re going to thrive in the role. If they’re thriving, your team is far more likely to thrive as well.

This may sound obvious to you, and you might feel like the responsibility for determining this lands with the candidate. However, there are a few things you can do to ensure that once the candidate signs, you will have a strong and lasting professional relationship.

First, be clear about the work that will be expected of the candidate and what they’ll be doing. If you’ve sold them on one role and they end up in a completely different role, they are likely to be unhappy. If you’re not completely sure what you’ll need them to do, be honest, tell them what you do know, and give them the opportunity to talk to others who may be able to give them more information. Hiring the right people to do the wrong role will only waste your time and limited resources.

Finally, ask them what their growth expectations are and ensure the expectations are realistic within your organization. If they’re looking for mentorship from more senior leaders, make sure your organization can provide that mentorship. If they’re interested in learning and applying new skills, make sure the roadmap can support this growth. Providing and enabling growth opportunities not only makes your organization better and stronger, but also keeps your team members energized and motivated.

How to evaluate these traits

Interested in learning more about how to evaluate some of these traits? Contact us to learn about some best practices we’ve developed over the years!