Applying Agile IT Methodology to Data Science Projects


If you keep up with the latest trends in the business world, then Data Science is a term that appears frequently nowadays. It is a steadily growing field and newer developments keep occurring as well. Data Science is responsible for multiple benefits for varying business industries. Small and large businesses alike are catching up; discovering high potential for growth using data analytics.

Data Science Projects

Businesses that seek to improve their solutions for customers by improving their operations, services, and products invest in Data Science projects. It is imperative to establish an expert Data Science team to streamline these projects and earn the best results.

What is Agile Methodology?

Also known as Agile development or Agile software development, this methodology is a comprehensive term for certain development methodologies. The popularly include Scrum, Extreme Programming (XP), Crystal, Lean Development, Dynamic Systems Development Method (DSDM), and Feature-driven Development (FDD).

These methodologies each have unique approach towards problem solving. However, they share a common feature due to which they are collectively considered under Agile development. These methodologies all use the concept of iteration and constant feedback in order to refine a system under development.

Why Does Data Science Need Agile Methodologies?

Data Science is beautiful in its application and the benefits of the process are plenty. However, problem solving with Data Science is not as easy as it may seem despite experts handling the projects. The reason is that most Data Science projects don’t have an element of certainty.

It is easy to assume that you know the problem and that other people have worked on similar projects to find good results. However, things usually don’t move along as you may have planned. Moreover, you also don’t know how to schedule the project because it is impossible to determine a specific timeline.

This is why Agile methodologies have been introduced for applying on Data Science projects. While Agile has been used for software development in the past, it has been realized that it could be quite effective for refining Data Science projects as well. It is true that applying these methodologies to a data problem is different than applying to a software problem. However, creativity is needed in both situations. On the other hand, the benefit remains the same. Agile methodologies make your work easier and organized as you can use cycles. With each cycle, you can learn something new, get more refined results, and share them with other invested entities.

Agile methodologies are expected to become more common for Data Science projects in the near future. Many data scientists have reported that it makes them more productive. It obviously does not increase or decrease the skill of data scientist. However, it can help them optimize their projects. Instead of spending time on models that are unlikely to reveal any productive results, it is better to spend that time for other result-driven purposes.

How Agile Data Science Works?

When using Agile methodologies for Data Science, the focus is not on what to do but how to think. Experts believe that a Data Scientist should have an active and dynamic approach when applying Agile methodologies.

While the Agile methodologies being used for Data Science are the same as those used for software development, the approach is unique. Here is how Agile Data Science works:

  • When working on Data Science projects, it is impossible to get any kind of insight immediately. It needs multiple iterations before you reach any kind of discovery. Moreover, the data needs to be structured before it can be analyzed. Reaching the stage where you can develop a model for predictions needs a lot of iterations. Therefore, iterating continuously is a major part of Agile Data Science
  • Apart from iteration, sharing outputs throughout the Data Science project is important as well. You will have multiple outputs before you reach any conclusion on the project. These are known as intermediate outputs. It is necessary to share them because waiting until the end of a specific sprint to share them will most likely end with you sharing nothing. And that is against the Agile concept. If you are not sharing the outputs, you are most likely perfecting the processes before you can accomplish the sharing. This is ineffective because these outputs could be important for refining the process while perfecting may lead you to a dead end. Hence, you would have wasted your time. Part of the Agile Data Science is to make the projects self-documenting. Therefore, sharing incomplete outputs while continuing to wild will result in better productivity
  • It is important to understand the fact that unlike software development, Data Science is more experiment based than task based. Data Science helps explore data so it should be treated as multiple experiments
  • When dealing with software development, there are generally some perspectives. These include what the customers want, what the developers want, and what the business seeks. When working with Data Science, another perspective is added. This is what the data is telling you. You can’t make any sense out of the data unless you develop a basic understanding of it
  • Don’t deviate from the data-value pyramid. This pyramid represents the value that is achieved when raw data is refined, then followed by reports and predictions. In the first layer, you record data. Next is the raw data being covered into charts, tables, graph or other structured form. Then comes the reporting layer that deals with exploration and reasoning. The second last layer is of prediction, which is facilitated by the layers preceding it. The last layer includes actions. The insights you have derived will only be valuable if they can give rise to newer actions or advance existing ones

Data Science with Agile methodologies is a process that also includes defining goals and following the critical path to achieve them. The analysis during the process should be continuously documented instead of focusing just on the end product in order to climb the data-value pyramid. The points defined above should be used collectively for ensuring an Agile Data Science project.

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