The secrets to developing a high-performing data team

The value of data and the importance of data analysis permeates every department of modern organizations – from sales to marketing to product. As platforms become easier to use and the use of nimble technologies such as low code/no code become more prevalent, we are moving towards a future where anyone, regardless of technical know-how, can gain valuable insights from data. At the heart of it is that data teams play a key role in ensuring that organizations use data in the right way to make better decisions.

Data teams are like code breakers. By uncovering insights hidden in data, organizations can drive better decision-making and business results. A strong data team should be able to quickly deliver the right information to help leaders make confident, well-informed decisions and prevent uncertainty and mistrust of data from causing paralysis. But how exactly do organizations go about building and developing their data team to maximize their value? Here are the best practices for leaders to follow.

Maintain a balance between hard and soft skills

It’s tempting to focus solely on technical skills when hiring data analysts, but being a great data analyst also requires intuition and people skills. They need to know how to read between the lines to pull out what someone really wants and then create the right approach to meet that need. This kind of critical thinking requires empathy and managerial communication skills, which are much more difficult to teach compared to technical knowledge.

Of course, technical skills are a prerequisite for the role. So the question arises: what should hiring managers look for when recruiting analysts? It requires balance. From a technical perspective, they should be able to understand and manipulate raw data to generate new insights and should be well versed in technologies such as Python and SQL. In terms of soft skills, consider the candidate’s level of curiosity, strong communication skills and ability to self-motivate. The best analysts will have a natural desire to identify and investigate problems and communicate their findings clearly and effectively to stakeholders. They will know when to refine their findings to avoid technical jargon. And perhaps most importantly, they should always be guided to go beyond simply answering the immediate question to delve into the core of what they are asking and uncover deeper insights.

Provide continuous learning opportunities for data teams

Hiring top talent is just one piece of the puzzle. The right people will be naturally curious and eager to learn. It is important to foster this culture and facilitate opportunities to learn and think critically.

When a data team is dealing with busy periods or a crisis, it’s easy to prioritize settlement tasks over learning and development. But putting employee growth at the bottom of the priority list could risk burnout, disengagement, and slowing skill development. Investing time in team development will help them stay engaged and feel empowered and able to tackle new challenges.

Keep constant communication open. Ask team members what they want to learn about and how the company can support their growth. Make it clear that their development is as much a priority as their contribution to the team’s success.

Businesses should also set aside time with each team member to create a personal development plan based on what they are interested in focusing on. From there, they can collaborate on achievable learning goals that align with the roles and aspirations of employees. In practice, this could be through weekly training sessions, workshops or dedicated teaching hours. Finally, a solid development plan should include customized resources such as online courses, books, and seminars.

Connect the work of the data team to business results

Employees today do not want to be cogs in the machine. They want to add real value and impact to the organization and its customers. To be clear about how the data team’s work impacts the business, set specific goals and KPIs that map back to broader business goals. You can take them a level deeper by outlining specific metrics for individual employees to track how they are contributing to their team goals.

Once goals are set, set up monthly reviews to ensure analysts are making progress. In reviews, discuss what has been accomplished since the last review, what priorities may have been delayed on other projects, and what the team will be working on next month. These touchpoints serve as opportunities for team leadership to provide meaningful feedback and proactively gauge how the team feels about their work.

With a small investment of time, this approach is simple and effective. Not only does it allow employees to take pride in their work, but it also holds them accountable for the goals they set. By creating an environment where team members believe they are helping to achieve results, they will be more motivated, proactive and autonomous.

Trust your data team to work autonomously

Over time, if analysts repeatedly solve the same questions and generate the same recurring monthly or quarterly reports, the work becomes robotic. They lose the desire to think critically or approach problems in new ways. This leads to a scenario where the team is only focused on clearing tickets rather than taking a step back to consider the value and impact of their work. Suddenly, analysts feel more like a ticket queue—and that could open the door to boredom and burnout.

Analysts are like partners or consultants for other teams. They should be able to prioritize their tasks based on potential impact, rather than just responding to all incoming requests in sequence. In addition, they should feel empowered to open up a discussion about how to resolve the issue in fulfilling the request. It is possible that an alternative method leads to more useful insights and results overall. Or they may discover that the team really needs something completely different than what they are asking for. Giving analysts more freedom in how they approach requirements means they stay curious and concerned with both the data and its impact.

What does this mean for businesses? Simply put, don’t define success as the number of analyzes completed. Instead, focus on the impact and value of the analysts’ work. Set expectations that they won’t have to mindlessly follow stakeholder requests, and give them the autonomy to decide the best approach for their work. Encourage them to ask questions and understand why the request was made. What is the real question? What is the best way to find the answer? Are there other relevant analyzes that could provide additional insights? Analysts are experts, so treat them as such.

Finally, always find time to measure and celebrate your wins. If the analyst’s alternative approach has been successful in driving strategic decisions, improving processes, or achieving business goals, make sure they get credit for it!

By prioritizing the above best practices, businesses can successfully recruit and develop top data talent. With a strong data team at the heart of your organization, you can ensure that every team in your company has the support they need to use data-driven approaches in their work, increase internal efficiency and deliver the best and fastest results for your customers.

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Labels: data, skills, team