Breaking the Bottleneck: Empowering Your Analytics Team

When it comes to incorporating data and insights into your business, many companies have a simple formula in mind:

A centralised data analytics or insights team serves as the go-to source for information across the organisation. They help to flesh out ideas by looking into the current data and fill-in gaps through sourcing of third party data/market research. They help you make the right choice.This team is seen as a linchpin for making informed decisions. While this model might sound effective, in practice, it can lead to significant challenges.

The Bottleneck Problem

Analytics teams often operate with a small headcount and face demands from multiple stakeholders, each with competing priorities. These teams quickly become overwhelmed, juggling numerous requests. This workload not only compromises the accuracy of their work but also limits their ability to deliver meaningful insights, observations, and actionable stories.

Rather than enabling agility, the team inadvertently slows down processes, becoming a bottleneck. This is especially true when their work relies on specialised skills, such as programming or niche software, making it difficult for others to access or interpret the data independently.

Over time, the bottleneck problem compounds. With every new data initiative, the team creates more dashboards, models, and infrastructure that must be maintained. As maintenance demands grow, the team has even less time to generate value through fresh insights. This dynamic leads to inefficiencies and prevents the business from fully leveraging its data.

“Some people actually like being the gatekeepers,” says Oliver Gwynne. “If everything has to go through them, they feel indispensable to decision-making. However, this approach may not be in the best interest of the company.”

Fact: Harvard Business Review highlights that 73% of executives believe their company’s analytics initiatives deliver less value than expected due to workflow inefficiencies and over-centralization. (Source: HBR, "Why Data-Driven Transformations Fail")

How to Address This Issue

Tackling these challenges requires a dual approach:

Data Roadmap

The first step is to establish a formalised process for generating, evaluating, and implementing ideas on how data will be utilised. By equipping stakeholders with a foundational understanding of data, they can better appreciate the realistic timelines, capabilities, and workflows of the analytics team. this can then feed into a Data Roadmap should prioritise planned projects and strategic initiatives, ensuring that ad-hoc requests are minimised and treated as exceptions rather than the norm.

An essential part of this process is identifying patterns in common requests and recurring questions. These insights can help pinpoint areas where automation such as self-serve dashboards or prebuilt reports can deliver significant value. Automating frequently requested insights not only streamlines workflows but also reduces the burden of repetitive tasks on the analytics team, freeing them to focus on more strategic initiatives.

A well-structured roadmap fosters transparency, helping teams understand what to expect from the data function and reducing inefficiencies caused by last-minute or unstructured requests.

Empowering Decision-Makers with Data

Enable decision-makers to access data directly through tools they already use. Self-serve dashboards are a strong starting point, but businesses should think beyond reporting. Consider automated lists, alerts, flags, or push notifications that simplify decision-making without requiring constant analyst input. Training end-users to read, interpret, and act upon data is critical for success.

Above all, avoid overloading stakeholders with excessive information. More data doesn’t always lead to better decisions. Instead, focus on helping stakeholders identify the key metrics or insights they need to act decisively. Tying data into key decisions can greatly decrease the amount of data that needs to be extracted and modelled.

Team Structure Matters

It is common for organisations to begin with a small, centralised analytics team. This team typically focuses on building a solid foundation by creating core reports, dashboards, and insights for the company. However, as the organisation scales and data-driven decision-making becomes integral to various functions, this centralised model often struggles to meet the diverse and evolving needs of the business. To address this, many companies transition to a more distributed model, which offers greater flexibility and fosters collaboration.

One effective way to implement this distributed approach is through a ‘hub-and-spoke’ model. In this setup, the centralised analytics team (the hub) remains responsible for overarching strategy, governance, and maintaining the company’s data infrastructure, while analysts (the spokes) spend part of their time embedded within specific business units or departments. By doing so, these analysts become more aligned with the needs and goals of their respective units, enabling them to deliver tailored insights and solutions.

In addition to restructuring the analytics team, appointing Data Champions can further accelerate the adoption of data best practices. These individuals, serve as advocates for data-driven decision-making within their teams. Importantly, Data Champions don’t need to be technical experts themselves; instead, their role focuses on fostering a data-savvy culture. They can help colleagues frame better and more actionable analytics requests, ensuring that inquiries are specific and aligned with business priorities. Moreover, they act as a first line of support for addressing basic analytics queries or troubleshooting issues. This dual function of advocacy and support not only reduces the burden on the centralised analytics team but also ensures that data is being used effectively and consistently across the organisation.

Plug In External Help

There are times when plugging in an external agency to support your analytics efforts can be a strategic move. Agencies are particularly useful in situations where the internal team lacks the bandwidth or specific expertise to handle certain tasks. For instance, they can step in to help with complex projects such as developing advanced predictive models, implementing new data tools, or conducting deep-dive analyses.

Agencies are also valuable during periods of rapid growth, when the internal analytics team may struggle to keep pace with increasing demands, or when a fresh, unbiased perspective is needed to solve a persistent problem.

Conclusion

Any department with a small headcount and multiple stakeholders risks becoming a bottleneck, and analytics teams are no exception. Without a proactive strategy, they may inadvertently slow down decision-making and add technical debt as they manage growing infrastructure demands.

The solution lies in prioritising automated reporting and self-serve dashboards, building data literacy, and implementing structures like Data Champions and hub-and-spoke teams.

A formalised data roadmap aligned with business strategy helps reduce ad-hoc requests and ensures analytics delivers maximum value.

If your organisation needs guidance in understanding business priorities, creating a data roadmap, or supporting your analytics team, contact 173tech today!