There are different project management methodologies teams often employ to get a specific task done or a requested application developed. One of these techniques is the Agile Methodology, which manages a project by breaking it into several parts or phases.
This methodology requires teams to constantly work together and with various stakeholders to ensure continuous improvement at every turn. Its repeating cycle of planning, execution and evaluation works with different industries, including data analytics. In this application, data engineers prepare the data that is often uploaded regularly to a dashboard accessible to all relevant stakeholders. It is then open to reviews and comments readily integrated into the project’s next step.
If you’re wondering about the advantages of agile methodology for data analytics purposes, here are a few considerations to look at and see if Agile Methodology works for your particular data analytics team.
Consider your team size
Usually, smaller data analytics teams have a more precise, more limited scope of work. This setup makes these teams better suited for adapting agile strategies. Usually, small groups set up planning and prioritization sessions with various stakeholders. Scrum is a software development framework that supports prioritization with stakeholders.
This agile approach also provides the necessary tools to give data analytics teams good handling of the project and develop strategies to meet their goals–and the same can applies to stakeholders looking to monitor their resources.
However, the fundamental nature changes when working with Big Data Analytics. Adopting the agile methodology in larger teams presents different challenges. A 2016 study lists the main concerns on Agile in BDA as the following:
- The right team composition, namely, the managers, data specialists and analysts, and developers.
- Scale differences in the project scope concerning data streams available.
- Project safety limits based on data dissemination.
Additionally, responsibility in agile remains with product owners to ensure that the value expected by stakeholders is met and delivered. By design, agile teams are quick, and compact, making them ideal for smaller software or mobile app development projects. However, larger teams experience bottlenecks in translating the critical traits of agile teams. Setting the proper organizational structure is usually the first step to preparing everyone in a cyclic, iterative data analytics working nature.
Define the tasks, timelines, and dependencies
One problem with inexperienced Agile teams that lack expert guidance and management is that they fall into a never-ending cycle of iterations, which continue to consume resources without making significant progress. Derailed projects can be caused by many factors, from the drive of individual team members the organizational setup of the entire project to the ambiguity of first-time data analytics problems.
Another challenge is when various teams overlap over deliverables and dependencies expected from them. Clearly defining tasks for each team in the organization, plus their timelines can help mitigate this problem from occurring down the line. Despite its nature, an Agile project can always use a mindset of defining roles and timelines with the end in mind–a trait often associated with the waterfall methodology.
Additionally, a well-defined timeline protects your teams from getting sidetracked down the road. This arrangement is one of the challenges for agile projects. Agile focuses on iterative development intended for quickly responding to changes encountered along the way. However, this also risks teams getting caught in time-consuming repetitiveness. Usually, the final product is not identified, unlike in projects under the waterfall methodology. Agile user stories often come from previous processes and are continuously adapted to changing parameters, needs, and additional information recently made available.
For new projects, expert analysts or data analytics leads should help outline the tasks needed and deliverables from each agile team if possible. Communication with various stakeholders is critically important. For example, the net promoter score (NPS) is a commonly-used indicator for assessing customer experience and predicting growth opportunities. Product owners and team leads can include the following aspects:
- Product reviews (pricing, ratings, reviews)
- Customer service (lead time, touchpoints with customers, customer service scores)
- Delivery
Delineating the timelines includes meeting and reporting schedules with the client, allowing agile teams to work on their iterations uninterrupted. As the method often requires communicating with stakeholders, some groups find themselves with less time to work. This practice also details when teams can hold their sprints, optimizing the work they can complete.
Can you adopt Agile for your data analytics teams?
By examining the challenges often encountered by Agile teams in data analytics, it is possible to avoid the pitfalls that come with the speed and adaptability of following this methodology. More importantly, product owners and team leads should know that there is no one-size-fits-all in managing and overseeing data analytics projects. However, the core concepts of the methodology remain relevant and, once appropriately scaled to meet your organizational needs, can ensure an efficient implementation of any project.
