Clarkston Consulting
Skip to content

Considerations for Applying Agile Principles to Analytics

Contributors: Brooke Comune

Below, we explore use cases of Agile analytics and dive into considerations for applying Agile principles to analytics. 

What is Agile?

“Agile” is an iterative and explorative method of working and discovering data. In the world of project management, the term “Agile” has become ubiquitous, representing a shift away from traditional, inflexible approaches toward a more adaptable and collaborative way of working. Since its inception, Agile methodologies have been applied beyond software, into various verticals such as the data and analytics space. What “Agile analytics” looks like can depend specifically on an organization’s size, speed, and function of analytics within the broader business, but the spirit of Agile can be a very effective approach to managing a team. Originating in software development, Agile is rooted in four key values:  

  • Individuals & interactions over processes and tools 
  • Working software over comprehensive documentation 
  • Customer collaboration over contract negotiation 
  • Responding to change over following a plan  

Benefits of Agile in Analytics and Data 

Rapid delivery of findings 

The most important benefit of bringing Agile methods into data projects is the consistent delivery of new findings for analysts and features or datasets for engineers. Agile practices encourage analytics teams to deliver smaller, actionable insights more frequently, enabling stakeholders to access valuable information sooner. This accelerated pace of delivery results in quicker decision-making and checks progress in meeting expectations.  

Frequent communication 

Another significant benefit stems from an emphasis on frequent communication. Agile teams engage stakeholders throughout the development process, ensuring deliverables align with organizational goals and user expectations. This iterative feedback loop enhances collaboration and reduces the likelihood of misunderstandings or deviations from original objectives, which can cost a great amount of time and money.  

Within a data team, the feedback loop can help analysts and engineers confirm the accuracy, impact, and relevance of their solutions. Analysts are empowered to learn more about the area they are servicing, helping generate more relevant insights and improve quality of work. Ultimately, this collaborative approach improves the quality of work produced, as analysts learn more about the business itself, therefore creating more valuable insights and cultivating a culture of learning and continuous personal improvement within the team. 


Agile methodologies require prioritization to ensure the team is working on the most urgent tasks. This prioritization is optimized when the team understands the sum of the requests they have accepted. In Agile terms, prioritization is a well-organized backlog of requests that the team regularly updates, reviews, and reorders as a group in sprint planning. Often, sprint planning can look like ordering the backlog of requests according to a single criterion (e.g., value, importance, cost, complexity, risk, etc.), thus force ranking a list of requests against each other to identify high-impact tasks, enhancing overall project efficiency. 


Both Scrum Framework and Kanban methodologies can be applied to data and analytics teams, and variations can be used to accommodate unique team and organizational requirements. 

Some teams may benefit from Scrum framework, which revolves around a set framework: sprint planning, daily stand-ups, sprint review, and sprint retrospective. The bulk of the effort should be in planning sprints, which allow for fast-paced, result-driven progress. Allocating “story points” to tasks helps teams balance resources and workload effectively. Sprints help in breaking down the project into manageable, achievable chunks while also making changing requirements and feedback easier to handle.  

Others may benefit from Kanban methods. Kanban forces teams to create ranked priority and avoids resource constraints by limiting work in progress. There are no set sprints, which can make this valuable for longer exploration tasks or model testing. A Kanban board is often useful for more exploratory analytical requests that don’t fit as well into a sprint-based method of management. However, Kanban does create risks and challenges with teams putting too much work in progress and slowing delivery.  

What to Watch Out For 

While Agile can push an analytics team to deliver at a high level, considerations must be weighed before diving into this methodology.  

A common Agile mistake is overcommitment, often based on pressures from business or executive demand. This can strain resources and compromise the quality of deliverables, causing a regression in the benefits of Agile. Additionally, scope creep, where requirements expand beyond their original scope mid-sprint, can hinder progress.  

Striking a balance between both comprehensive and ad-hoc business demands along with team bandwidth is essential, as shifting priorities too frequently can disrupt ongoing tasks and diminish team productivity. As mentioned previously, sprint lengths can be adjusted, or Kanban boards can be added. However, too many modifications to the process can deteriorate the core principles of Agile and can even cause execution risk, so there always needs to be a sense of accountability and direction.  

Another important Agile consideration revolves around the time commitment required for agile ceremonies, such as sprint planning or team retrospectives. These ceremonies, though crucial for effective communication and alignment, can potentially monopolize a significant portion of team members’ time. Effective facilitation is essential to ensure that these meetings remain productive and focused. With this, it’s crucial to fold in change management elements when rolling out Agile to ensure that there is alignment on the value of the process and possibly foster the expansion of Agile within other areas of the organization, where appropriate.  

Final Thoughts 

The core values of Agile: individuals and interactions, working products, collaboration, and responding to change can guide analytics teams in delivering value to stakeholders more effectively. While variations in Agile practices exist to suit diverse organizational needs, adherence to the foundational principles is crucial for success. The essence of agility lies in collaboration, flexibility, and a relentless focus on delivering meaningful outcomes.  

If you believe your organization would benefit from discussing how to applying Agile principles to your analytics and data management strategy, our experts at Clarkston can help.

Reach out to learn more about our Project Management or Data and Analytics Consulting Services.

Subscribe to Clarkston's Insights

  • I'm interested in...
  • Clarkston Consulting requests your information to share our research and content with you.

    You may unsubscribe from these communications at any time.

  • This field is for validation purposes and should be left unchanged.


Tags: Advanced Analytics, Project & Program Management, Data Management