What do you do if your data analysis team is struggling to support business growth?

Make sure your team has a solid understanding of the workflow and processes of the business processes they support. Use simple visual schematics like flowcharts or logic models to establish understanding, and then look for opportunities to use data analysis to establish and strengthen positive and negative feedback loops. Make sure you understand the data needs of the other teams in the workflow, and maintain good working relationships with the members of those teams.

If your data analysis team is struggling to support business growth, the first step is to align data objectives with business goals—ensure you're analyzing the right metrics that directly impact revenue, customer acquisition, and retention. Empower your team with the latest tools and training to improve efficiency and insights. Foster collaboration with other departments to better understand their pain points and opportunities. Streamline workflows by automating repetitive tasks, allowing analysts to focus on strategic insights. Lastly, emphasize actionable insights over complex analysis—simplify findings into clear recommendations that drive decision-making and growth. Data must fuel, not stall, growth!

Start by evaluating the current business goals and how well your team’s capabilities align with them. Identify gaps in data accessibility, analytics capacity, or interpretation that may be hindering growth.

Outdated or inadequate tools can slow down performance. Assess the software and technologies your team is using and consider upgrading to more advanced data platforms, automation tools, or cloud-based solutions to enhance efficiency and scalability.

Invest in your team’s development. Identify skill gaps, particularly in areas like advanced analytics, machine learning, or business intelligence tools, and provide relevant training to help the team stay at the forefront of industry trends.

Process bottlenecks can significantly hinder a team’s ability to contribute to business growth. I’ve addressed this by reviewing and optimizing workflows, focusing on reducing redundant tasks and improving cross-team communication. One impactful change was streamlining the way we handled data requests from other departments, which improved response times and data quality. By refining processes and automating where possible, we were able to focus more on high-value analytical work that directly supported business growth and operational efficiency.

Analyze your existing workflows to uncover inefficiencies. Streamlining processes, automating repetitive tasks, and implementing data governance frameworks can increase your team’s output and the accuracy of insights delivered to the business.

In some cases, the solution to a struggling team is simply expanding its capacity. When we hit a point where the workload was exceeding our bandwidth, I advocated for hiring additional talent with complementary skills. Bringing in new members who specialized in areas like data engineering or machine learning allowed us to handle more complex projects and meet business demands more effectively. Expanding the team gave us the flexibility to focus on both long-term innovation and immediate business needs, improving overall support for growth initiatives.

If the workload has outgrown the team, it may be time to expand. I’ve been in situations where bringing in new talent—either permanently or temporarily—helped distribute the workload and provided fresh perspectives.

If the demand on your team is growing but resources are limited, consider hiring additional talent. Look for candidates with complementary skills such as data engineering or business analysis to balance the workload and bring in fresh perspectives.

Improving collaboration between the data team and other departments has proven to be one of the most effective ways to support business growth. I’ve encouraged stronger cross-functional communication, ensuring that we’re not working in isolation but in tandem with product, marketing, and other teams. This collaborative approach has allowed us to better understand business needs and offer more relevant, actionable insights. Enhanced collaboration also facilitated smoother integration of data-driven strategies across the organization, making our work more impactful in driving growth.