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Top Pitfalls to Consider When Establishing a Data and Analytics Team 

Data is at the heart of the success of any life sciences, retail, or consumer products business. It enables a deepened relationship with the consumer, increased speed and efficiency, and allows new insights to be shared across functional and account teams. Yet, many companies struggle to gain insights effectively due to operational challenges. Issues with prioritizing data analysis needs, allocating resources efficiently, and addressing organizational gaps ultimately hinder their ability to make data-driven decisions that drive business outcomes. When revamping an existing team or establishing a data and analytics team internally for the first time, leaders should watch out for these common pitfalls that can often be a barrier to unlocking the value of your data.

Common Pitfalls to Establishing a Data & Analytics Team 

#1: Long-term strategy and executive oversight 

The most common issue that data teams struggle with is losing sight of the overall strategy. Leaders and analysts can lose sight of the forest through the trees as requests and questions pour in. The daily questions and ad-hoc analysis obscure the larger picture. This lack of strategic direction can mean missed opportunities, confusion on priority, and a failure to deliver on the data team’s core goals.  

The antidote is a long-term strategy driven by the executive team that will align with the goals of the broader organization, anticipate emerging trends and technologies, and balance short- and long-term priorities. An executive-backed and well-maintained long-term strategy will ensure that the team is equipped with the tools, talent, and infrastructure to succeed. The best strategies incorporate change management and a focus on continual improvement of organizational data literacy. These foundational efforts can improve later analytical work and help increase the use of data in decision-making. 

Lastly – a strategy doesn’t have to mean inflexibility. Successful analytics and data organizations often adopt agile frameworks and methodologies to ensure they can continuously deliver value while providing flexibility to adapt to changing business needs. 

#2: Speed of delivery vs. sustainable architecture 

Since a large portion of the requests that come into a data team are often time-sensitive (or at least have that impression due to pressure from executives), a dilemma is created between the speed of delivery and building the best long-term solution. This predicament is pronounced for new analytics and data teams as they may not yet have a mature data platform. 

It may be faster in the short term to run a quick query or build a quick dashboard to answer an ad-hoc question, but in the long term, it would ultimately save time to properly build a foundational data model to serve current and future reporting capabilities. This short-term decision-making creates technical debt, which can be worsened when compounded with data debt and other quality issues.  

Balancing quick wins that may incur debt and long-term foundational work is crucial. Teams who set aside some bandwidth for ad-hoc analysis while building for the long term are often the most successful. Any technical or data debt created should include a plan to fix or update it later to avoid “compounding” debt.  

#3: An undefined role of the team 

Analytics and data can mean various things based on who you ask within an organization, making it critical to set the expectations of the team’s role appropriately. For example, in some organizations, the analytics team is responsible for dashboarding only, leaving the data to IT. In others, the team may only maintain the data platform for the business to create insights, or the team could be responsible for all the above and more. 

This confusion can result in missed opportunities to drive business outcomes and frustration with performance. Without clear goals and responsibilities, it can be hard to measure the success of the data team, making it challenging to demonstrate the value of the team to senior leadership, secure additional resources, or expand the team’s capabilities. 

To prevent this, answer a few core questions about the team and ensure all stakeholders understand these: 

  • What tangible deliverables is this team responsible for building? 
  • What are the key responsibilities of the data team within the organization? 
  • How does the data team contribute to decision-making processes within the organization? 
  • How does the data team communicate their insights to stakeholders? 

These answers may change over time, but ensuring alignment throughout the organization can make a difference in generating insights and helping drive overall business metrics.  

#4: Prioritizing a high level of demand 

The successful introduction of a data and analytics team can often cause a spike in demand for insights and information. If unprepared, a team can struggle to meet varying deadlines and criticality. Prioritizing across various stakeholder definitions of “high priority” can prove impossible. 

Leaders should strive to set standards for new work, where each stakeholder must tie the business value and use for each request. Use cases can then be prioritized with leadership or based on a set framework, executing what is truly top priority.  

Transparency is crucial here as well. All stakeholders should clearly see what the analytics team is working on and understand why their project may not have been prioritized. This clarity can set organizational standards for priority and reduce noise that analytics or data leaders may hear when stakeholders raise concerns about unmet requests. 

#5: Hiring and organization of resources 

A final area that can be a stumbling block for a data team is its hiring and organization of resources. Hiring can be challenging due to the high demand for skilled data professionals, often resulting in higher salaries and competition for top talent.  

Additionally, building an effective data team requires a mix of technical and soft skills, making it challenging to find candidates with the right combination of expertise. Even once a data team is assembled, organizing and allocating resources can be difficult, especially in cases where there are limited resources or competing priorities.  

Leaders faced with bandwidth constraints while growing the team can consider leveraging a third-party managed analytics team. The right partner can assist a data team in completing their tasks while meshing well within the current team culture.  

Establishing an Effective Data & Analytics Team 

Organizations and their executives that can be cognizant of the pitfalls when establishing or redefining a data team are best equipped to deliver reporting and insights that are most effective. If you believe your organization would benefit from talking through your data strategy or team, our experts at Clarkston can help. We have experience helping executives and leaders navigate the pitfalls mentioned in this piece as well as the many other challenges that data teams may face.  

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Tags: Data & Analytics, Data Strategy, Project & Program Management, Data Management, Data Operations