How to Make an Agile Team Work for Big Data Analytics

Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value for your Big Data products.

By Premjith B P K, Aufait Technologies.

The Agile process’s success in software development and the development of the latest technologies have made it very popular in the innovation industry.

The Agile development process is fundamentally based on the Agile Manifesto, which outlines its difference from the traditional approaches to software development. The Agile follows an iterative and incremental methodology in its flow.

The Agile Manifesto emphasizes putting interactions over tools, software over documentation, collaboration with the customer over contract negotiations, and responding to over mere following. The agile process tilts its balance more into the finer and current aspects of development.

Big Data forms the whole ambit of data and is difficult to define owing to its complex structure, size, and diversity. In simple terms, we may say it includes the entire data in terms of variety, volume, and velocity of its creation.

Now, when it comes to Big Data Analytics (BDA), the role of the Agile process is being considered widely. But the primary concerns in adopting an Agile approach in Big Data projects as per a study are:

  • Constitution of the right mix in team viz. managers, developers, and analyst.
  • Defining the scope of the project concerning the amount of data sources available
  • Safety limits of projects based on the dissemination of data

The right way to integrate Big Data projects would be to strongly consider the above factors and follow a five-step process of:

  1. Clearly defining the problem
  2. Identifying the knowledge gap
  3. Hypothesis formulation
  4. Testing the hypothesis
  5. Iterate, i.e., continue the cycle


Challenges in Integrating Agile process to BDA

One of the critical challenges in Big Data Analysis(BDA)using Agile philosophy is to identify the areas of integration. The parameters of data science must be able to fit well with Agile processes to mine out meaningful outputs.

The Agile methodology needs the project teams to be crisp, compact, and quick, which is quite easy on software or mobile app development projects. But in delivering Big Data projects data science with its span of analysts, designers, business developers, managers, data scientists, etc. make for an overarching team.

Yet another challenge is the overlapping of functions of various teams.

Agile Big Data Team Structure

Fig.1. An Agile Big Data team structure.

For example, the figure above demonstrates that assigning a storage function for raw data may fall within the ambit of a platform engineer as well as an applied researcher. The more multiple individuals perform a single task, the more complicated the communications between the individuals become. All this leads to the problem of increases delivery time.

Integrating Agile Framework and Data Science

The best way to integrate Agile Framework into data science to make the Big Data Analytics process agile is to embrace change with a degree of proactiveness, as Russell Jurney discusses.

The first step to make the interaction smooth is to shift the preference in favor of generalists over specialists.

Agile Team Generalists

Fig. 2. Generalists are pivotal in Agile Big Data teams.

It means that when building agile teams for Big Data analysis, there will be a focus on both the depth and width of knowledge among team members than on specialization. It doesn’t mean that specialists will be left out, but as a bridge to fill in the spaces in the work division, generalists will be expected to play a more significant part.

Hence a proficient, agile Big Data team will have:

  • Data scientists who do well with making applications and services on the web as well as research functions.
  • Web developers with an understanding of UI/UX, along with the capability to create applications.
  • Researchers who can check the source code, explain and handle data
  • Product managers who can handle all associated domains

Besides this, some of the essentials to integrate a Big Data Analytics team are:

  • Divide the project team into smaller teams
  • State of the art technologies to speed up processes example, high-level tools and platforms like PaaS (Platform as a Service, cloud computing, distributed systems
  • Share intermediary work, i.e., data continuously even if it is incomplete as nature of Big Data analysis demands regular periodic sharing

Once the primary hurdles are overcome, your agile team will finally function as described below.

The practical Agile Big Data team will thus consist of a small group of generalists acting as a bridge between other members. The generalists will use scalable, high-end tools, and cloud computing to perform iterations that add value to the raw data and thus refine it for a higher value.


Bio: Premjith leads the Digital Marketing team at Aufait Technologies, a top-notch SharePoint development company in India. With his four years of experience in online marketing, he helps clients expand their online presence and mushroom novel business ideas.