The 4 People Every High-Performing Data Team Should Have (and How They Make You Successful)

The last few years have seen an acceleration in technological advancement as AI has been thrust into the limelight, and that’s caused organizations and employees equal parts excitement and concern. It’s far from the first time that profound disruption has happened, but it is a prime example of a moment when an optimal data and analytics team can help companies make the best decisions for their future without jumping on the bandwagon or upsetting the apple cart.

‍

In this article, I look at how an optimized and balanced data team is critical to departmental success, and I’ll give pointers on how to build one in line with a philosophy of balancing four “types” of personas – Disruptor, Optimizer, Keeper, and Organizer.

‍

Three of these four personas are drawn directly from the work of Moxy Analytics CEO and co-founder Laura Madsen, and I'd like to credit and thank her for the years of work she put into developing her framework.

‍

Let’s dig in.

‍

What you’ll learn in this article  

• Discover the strengths of each data team “persona” – Gain insights into the unique abilities of Disruptors, Optimizers, Keepers, and Organizers, and how their contributions drive team success.
• Learn how to build a balanced team – Understand how to strategically assemble a high-performing data and analytics team by leveraging diverse personas.
• Enhance innovation and collaboration – Find out why integrating diverse personas fosters better teamwork within your organization.
• Implement practical strategies – Explore actionable steps and best practices to create a cohesive, results-driven data and analytics team tailored to your goals
  ‍

How to build a high-performance data and analytics team

Data and analytics is a team sport.  

‍
One of the biggest challenges you’ll embrace as you build your data and analytics team is assessing how well each individual integrates, or will integrate, into your team. Speaking as a department director with a long track record in the data and analytics industry, structuring teams to work well together is a daunting task that often feels impossible. Finding talent isn’t the hard part; choosing the right talent is. I have a good feel for how people work together that has served me well in building my teams. But in the past, I couldn’t have easily articulated “how I did it” when someone asked me about my secret team-assembling sauce.
‍
About a year ago, I read an amazing book called AI & the Data Revolution by the CEO and co-founder of Moxy Analytics, Laura Madsen. In it, Madsen identifies three types of people, which she terms “personas”, that make up a successful team: Disrupters, Optimizers, and Keepers. These three distinct personas interconnect to form a high-performing data team because of their complementary strengths.

After reading her book, it all started to come together for me – my team members have these characteristics! – and I even added a persona to her list (Organizer).  Thinking in these terms helped me to see that the key ingredient to my “secret sauce” was having the proper mix of Disruptors, Optimizers, Keepers, and Organizers. Balancing out strengths/weaknesses is key. If you’re too heavy on one type, you risk a team that’s hyper focused on smashing through the status quo, but neglects the day-to-day governance required to keep the house in order. Too many of another, and your team won’t deploy anything.
I’ll take you through the “why” and “how” in this article, but first, let’s look at the “who”.  

‍
So who are these four data team “personas” and why do they matter?

‍

Team persona #1: Dancing with the Disrupters

Why are disruptors critical for your data team? Simply put, they think outside the box to challenge the status quo. They embrace – even love – change, rather than the “grind”. On their resume, you’ll likely see a lot of jobs in a short span of time. Often, these patterns emerged because they were positioned on a team where they couldn’t do what they do best: Pioneer. Your task is to give them the space to be an innovator, as Madsen describes in her book.

‍

Best roles: From a team composition perspective, this person could thrive in many different roles, such as an Analyst, Developer, Team Leader (Manager/Director), or Architect. My preference is to position the Disruptor in a Lead Developer role, and I like my Disruptors to be personable and somewhat outgoing, which is usually the case anyway.

‍
Team persona #2: Optimizers architect improvement

Optimizers take an innovation or a prototype and make it fit-for-use by a larger audience. They’re natural problem-solvers who love refinement: They can clean up interfaces, streamline processes, and roll up their sleeves to dig into persistent problems. They understand what it means to scale solutions, building upon existing work.

‍
Best roles: In my experience, having Optimizers in both development and analyst roles is ideal. This enables optimizing on both fronts, and it’s highly likely that Optimizers in different roles on the same or adjacent teams will collaborate, accelerating the innovation process. Caveat: If your team is too heavy on Optimizers, tasks like validation and administration will be left undone.

‍
Team persona #3: Keepers keep the lights on

Keepers are people who, to put it plainly, keep the lights on. These individuals flourish in roles with predictable daily responsibilities and take a lot of pride in keeping the wheels turning. They thrive on tasks like weekly/monthly data validations, regular stress testing, and administration. They are rule-followers and overseers who are gifted at keeping data clean and user friendly.
‍

Best roles:  In my experience, a Keeper in an Analyst role stays on top of proper data validation before any new data goes to production; other persona types might overlook this critical part of the process.

‍
The risk you run with Keepers? Too few, and the one(s) you have will feel overwhelmed. Too many and the team doesn’t work at peak efficiency. There’s nothing quite like getting a call from your CIO asking why a key metric is way off – only to learn that you don’t have a field mapped quite right because of rushed validation. You didn’t have a Keeper (or anyone with Keeper traits) when you needed one.

‍
Team persona #4: Organizers know the org inside and out

This is my addition to Madsen's list. I’ve learned over the years that I need Organizers, who keep us all playing to the same tune from the same song book. They don’t necessarily need to lead the team or project, but they are the ones who step into meetings and call out issues that delay projects; they can tell you how each issue influences everyone in the room. They’re likely skilled in creating relationships within the business and have no problem scheduling a quick call with, for example, the developer and subject matter expert (SME) to root out a key issue.  
‍
Best roles: They may or may not be in a project manager-type role.  I have found that outside of Project Manager/Product Owner, these types thrive in Lead Analyst, Business Analyst, and Architect roles.
What does this mean for data team leadership?  

‍
Disruptors, Optimizers, Keepers, and Organizers are not roles.

Remember, they’re personas based on a blend of traits who are suited to particular roles on your teams. People aren’t just one thing, in life as in work. So, usually – and there are exceptions – people fall along a spectrum. For example, I’m a blend of Disruptor and Optimizer (with a small side of Organizer) and I lean towards the profile of a Disruptor.

‍
Knowing where you are as a leader on the spectrum from Disruptor through Organizer – which traits you most embody – can help you bring on teammates who are compatible with both you and one another (if you already have a team, you can try adapting their roles to play more to their strengths and chemistry). If you balance out the traits on your team, taking great care to identify each’s strengths and align them to their role, not only will the team be well-positioned to see innovation and disruption through from ideation to deployment to maintenance, but they will also feel comfortable with the role and its responsibilities, which leads to higher performance.

‍
Note: Building a data team with complementary strengths is particularly important if your team is on the smaller side (e.g., 5 or fewer). And yes, you can build a great data team even if you’re small-but-mighty.  

‍
Assembling a data team: What to consider as you structure your team

Rule #1: Start by hiring where the work is.

To fill out your team, look at where the work is and hire accordingly. Need determines how you’ll proceed initially. Ask yourself questions like:

• Is there heavy data validation and testing? You need a Keeper.
• Do you need to bring together several disparate ERP systems in a pinch? Hire an Optimizer next.
• Is the business is asking questions of the data that fall outside of development and enhancements? Keepers make great report writers and analysts.
• Is the business giving you feedback faster than you can tackle it? Bring on an Organizer.
• Do you wake up in the morning and think that things could just be better? Disrupters thrive in this environment.
• Business leadership wants more clarity about strategic direction? Disruptors and Optimizers, especially well-spoken individuals, can have a big impact here.
• Are you looking to take the next step up in the data and analytics maturity curve? Disruptors and Optimizers will get you there.
  ‍

You will make tough decisions about team composition

When building a team, I try to follow a strict recipe according to my preferences and personality/persona. But even with a recipe, you still have to make tough calls, which is particularly hard when you have two well-qualified candidates or colleagues who you know will butt heads based on the traits you’ve observed (I’ve been there many times). On small teams, you have to be particularly decisive.

‍
How to hire your data team (procedure)

Your data team is an integral part of your data strategy.

My number one rule: Take care to match your outcomes with peoples’ experiences, knowledge, and expertise.  It might be difficult for a Disruptor with deep experiences in Data Vizualization to innovate and advance an organization in Machine Learning and AI.

‍
My method: I look for about 20% disruption, 40% optimization, and 40% keeping – I add Organizers if I can and rebalance the ratio. That’s because I am looking at the ratio of overall traits across the entire team. If one person on a five-person team is 100% Disruptor, I wouldn’t add another “primary” Disruptor – or even a 50% Disruptor – into the equation. Doing so might create too much negative tension and be counter to productivity.

‍
Building a solid foundation through a phased approach: my secret sauce

To get things started, I do not hire everyone at the same time. Procedurally, when I’m assembling a brand-new team, I hire a Disruptor before anyone else and I ask them to participate in interviews; this is partially to ensure fit, though it’s never 100% that you’ll get a perfect match.

Now that I have the Disruptor (my Lead Developer) on board, I look for the Optimizer, usually in the form of an Analyst (logically, that Analyst role is needed to build the development lifecycle). This initial Optimizer should also have some Organizer traits. Typically, the Lead Developer (Disruptor) and Analyst (Optimizer, Keeper, Organizer) are going to work together to really build the foundation of the data team, both technically and logistically.

‍

But often overlooked is the need to start building relationships within the business. This needs to start right away, and an Optimizer or Organizer can do just that.

‍

Once you have the first two hires made, start filling in the rest.  

‍
Tips and tricks for applying this persona framework to your team

• A developer that is a solid Keeper will thrive with an analyst who gathers all the requirements so they can focus on execution (writing the scripts).
• A developer that is a Disruptor won’t last long in that role; they need opportunities to break new ground.  
• Overloading on the Disruptor trait is like having two superstar players share a single position. They will compete and conflict, and one will leave.
• If you do have the luxury of bringing on a project manager, do so quickly. Project managers have different styles and personalities, so it’s crucial that your team has some influence on that role since they’ll interface frequently.
• Assigning a Disruptor to a Keeper role is just as brutal as assigning a Keeper to a Disruptor role. The Keeper will not meet expectations and will struggle. The Disruptor will quickly get bored and move on.
• Moving from one persona to another is not a promotion. A good Keeper does not evolve into a great Optimizer, and so on.
• As the team starts to mature and disruption starts wane, be sure to keep tabs on your Disruptor. They run the highest probability of attrition if you don’t keep them challenged.
• Take into consideration organization size as you apply these tips; of necessity, a larger team will have more hands to fill each role. Just be mindful of proportionality and personality. Aim for the golden mean.

‍
Drawing a blueprint for data team success

Understanding how people think and work is critical, and one way you can do that is to categorize their personas into Disruptors, Optimizers, Keepers and Organizers. Adding the right mix of personas to your team and gaining an understanding of where those personas have the best chance of success is important. It may seem like common sense, but it’s easier said than done. Once built, keep the team challenged by giving them tasks that will allow them to grow and achieve as who they are.

‍
Make team structure part of your data strategy

DI Squared is here to advise you as you make choices about team structure and goals through our Delivery Modernization and Data Strategy programs. Drop us a line to discover about how our experts can help.

‍

To learn more about Laura Madsen and her well-researched book that inspired this article, please visit her website.

‍