Building a high-performing data team is key to leveraging data for better decision-making. By balancing technical skills with soft skills, fostering continuous learning, and aligning work with business goals, companies can create teams that generate impactful insights and drive meaningful business outcomes.
The value of data and the importance of data analytics are permeating every department of modern organisations – from sales to marketing to product. As platforms get easier to use and handy tech like low-code/no-code grows in prevalence, we’re moving towards a future where anyone, regardless of technical know-how, can get valuable insights from data. At the heart of this, data teams are crucial in ensuring organisations use data correctly to make better decisions.
Data teams are like code breakers. Uncovering the insights hidden in an organisation’s data can drive better decision-making and business outcomes. 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 data distrust from causing paralysis. But how exactly do organisations build and develop their data team to maximise their value? Here are the best practices leaders need to follow.
Maintain a balance of both hard and soft skills
When hiring data analysts, it’s tempting to focus solely on technical skills, but being a great data analyst also requires intuition and people skills. They must know how to read between the lines to pull out what someone is really asking for, and then craft the right approach to meet that need. This kind of critical thinking requires empathy and executive communication skills, and these are far more difficult to teach compared to technical knowledge.
Of course, technical skills are a prerequisite for the role. So, this begs the question: what should hiring managers look for when acquiring analysts? It requires a balance. From a technical perspective, they should be able to understand and manipulate raw data to generate new insights and be highly familiar with technologies like Python and SQL. From a soft skills perspective, consider the candidate’s curiosity levels, strong communication skills, and ability to self-motivate.
The best analysts will have a natural desire to identify and explore problems and convey insights clearly and effectively to stakeholders. They will know when to uplevel their findings to avoid technical jargon. And perhaps most importantly, they should always be driven to go above and beyond just answering the immediate question at hand to delve into the core of what is being asked and uncover deeper insights.
Provide data teams with ongoing learning opportunities
Hiring top quality talent is just one piece of the puzzle. The right people will be naturally curious and eager to learn. It’s important to foster that culture and facilitate opportunities to learn and think critically.
When a data team is experiencing busy periods or crunch times, it’s easy to prioritise clearing tasks over learning and development. But putting employees’ growth at the bottom of the priority list could risk burnout, disengagement, and a slowdown in skill development. Investing time in the team’s development will help them stay engaged and feel empowered and capable of tackling new challenges.
Keep a constant line of communication open. Ask team members what they’re interested in learning about, and how the business can support their growth. Make it clear that their development is as much of a priority as their contributions to the team’s success.
Businesses should also carve out 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 employees’ roles and aspirations. In practice, this could be through weekly training sessions, workshops, or dedicated learning hours. Finally, a solid development plan should include tailored resources, such as online courses, books, and seminars.
Connect the data team’s work to business outcomes
Employees today do not want to be cogs in a machine. They want to add real value and have an impact on the organisation and its customers. To make it clear how the data team’s work impacts the business, establish specific goals and KPIs that map back to the broader business goals. You can take these a level deeper by outlining specific metrics for individual employees to track how they are contributing to their team goals.
Once goals have been set, set monthly reviews to ensure analysts are making progress. In the reviews, discuss what was achieved since the last check-in, what priorities might have been delayed for other projects, and what the team will be working toward next month. These touch points serve as opportunities for team leads to provide meaningful feedback and proactively check in on how the team is feeling about their work.
With a small investment of time, this approach is simple and effective. It not only empowers employees to take pride in their work, but also holds them accountable through the goals they’ve set. By creating an environment where team members believe they are helping to drive results, they will be more self-motivated, proactive, and autonomous.
Trust your data team to work autonomously
Over time, if analysts are repeatedly addressing the same questions and generating the same repetitive monthly or quarterly reports, the work becomes robotic. They lose the drive to think critically or approach problems in new ways. This leads to a scenario where the team becomes focused solely 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 this could open the door to boredom and burnout.
Analysts are like partners, or consultants, for other teams. They should be able to prioritise their tasks based on potential impact, rather than just responding to all incoming requests in order. Moreover, they should feel empowered to open up discussions about how to solve a problem when meeting a request. It could be that an alternative method leads to more useful insights and outcomes overall. Or, maybe they discover that what a team really needs is something entirely different than what they’re asking for. Giving analysts more freedom with how they approach requests means that they remain curious and engaged with both the data, and its impact.
What does this mean for businesses? Simply put, don’t define success as the number of analyses completed. Instead, focus on the impact and value of analysts’ work. Set the expectation that they don’t have to mindlessly follow stakeholder requests and give them autonomy to determine the best approach for their work. Encourage them to ask questions and understand why the request is being made. What’s the real question being asked? What’s the best way to find that answer? Are there other relevant analyses that could provide additional insights? The analysts are the experts, so treat them as such.
Finally, always find time to measure and celebrate wins. If an analyst’s alternative approach has been successful in driving strategic decisions, improving processes, or achieving business goals, then ensure they get recognised for it!
By prioritising the above best practices, businesses can successfully acquire and develop a top-tier data talent. With a strong data team at the heart of your organisation, you can ensure every team within your company has the support it needs to take data-driven approaches to their work, driving internal efficiencies and delivering the best, fastest results for your customers.
