IT and big data projects can be complex and are often ambiguous by nature. In the case of big data projects, hypotheses about the information contained within the data are either proved or rejected, with new hypotheses then arising. Similarly, assumptions about project requirements are often built into IT projects and stacked on top of one another in advance.
Yet, despite its complex nature, there has been a growing interest in big data. According to Forbes, more than half (53 percent) of all companies are now adopting big data analytics, compared with less than one-fifth (17 percent) back in 2015. Nevertheless, 70 percent of big data projects in the UK fail to realize their full potential.
In order to improve success rates, it is important for organizations to adopt a more fluid methodology, which allows them to roll with the punches. In this article, we explore the Agile approach to IT and big data project management and explain how investing in Agile project management training for project leaders can benefit your organization.
Understanding Agility
First, it is important to define precisely what is meant by an “agile” project management methodology. Although broad in scope and difficult to summarize succinctly, the focus tends to be on testing hypotheses or assumptions incrementally early on so they can be verified before any inaccuracies or errors can cause major disruption. For this reason, Agile methodology can reduce risk exposure.
It is a cross-functional, collaborative strategy based on teams working to continually test products, data, and conclusions. In relation to IT projects, the focus is on creating software that is bug-free and fully functional. This is achieved by building minimally viable products, testing them, and adapting them based on comments or reactions. In this sense, user responses are a critical part of the process.
As Gino Marckx, writing for Epam, explains, “By delivering the work in small increments of working—even production-ready—software, those assumptions are all validated early on. All code, design, architecture and requirements and (sic) validated every time a new increment is delivered. … It also allows projects to learn from the feedback.”
Benefits of the Agile Approach
The primary benefit of adopting an Agile methodology is the fluidity it provides. Crucially, this enables projects to seamlessly adapt to changing assumptions, hypotheses, and requirements.
In the most extreme circumstances, this can allow IT and big data projects to change direction entirely, without having to start over. However, more typically, it allows for smaller changes to be made and for these changes to be made easily and early.
A high-quality Agile project management training program or DevOps training can allow project leaders and their businesses to adapt to unexpected changes in budget or consumer expectations without the entire project becoming derailed. It can therefore reduce risk and result in lower overall project costs.
An Agile approach can be especially beneficial when it comes to big data projects because the approach allows analysts to gain valuable insights quickly, even from large data sets. This is primarily made possible by the way Agile projects break data sets down into smaller increments and is also assisted by the continuous testing process.
The Agile Approach in Action
The concept of Agile project management has become mainstream, with one of the most significant adopters being Google. In fact, Google gave a presentation at the recent Agile Business Conference in London, sharing the insights gained during “Project Aristotle,” an initiative designed to help the company understand team effectiveness.
They found, for example, that one of the single most important dynamics of a successful team is “psychological safety.” Essentially, this is a sense among team members that they are free to take risks, ask questions, make mistakes, and challenge conventions without suffering negative consequences. Clearly, the Agile approach can help with this, because it allows projects to adapt to experimentation more easily.
The company also makes use of an internal process that has become known as “dogfooding.” Essentially, this refers to a business using its own products internally within the workplace on a daily basis. One of the key advantages of this is that it allows the business or project team to test its products in a way that exactly replicates their real-world usage before releasing them to the public.
This process ties in strongly with the Agile approach to project management because it means that employees using the products during their day-to-day work activities can test their functions, evaluate the coding, and validate the numerous assumptions the projects are built on. Crucially, this can start to take place prior to the product being released to the general market, but it can also continue after the project’s eventual roll-out.
As a result, failures can be identified and corrected. They can also be learned from much earlier in the project’s life cycle than they would be with other approaches to project management. This means that any alterations or adaptations can be made with minimal disruption and, in many cases, before end users are affected.
Seamless Changes
Ultimately, IT and big data projects are complicated by nature and reliant on assumptions being made, hypotheses being drawn, and these two things being continually tested. If a particular hypothesis is disproved, another must take its place, and this all cries out for an approach that allows for seamless changes, real flexibility and genuine experimentation, questioning, and other unexpected changes to occur as seamlessly as possible. It is here that the Agile approach to project management truly excels.
A solid DevOps course, with an emphasis on Agile project management, can help project managers, analysts, and directors to implement a more fluid methodology. The methodology itself centers on cross-functional collaboration and on testing occurring earlier in the project life cycle. This then leads to minimal disruption and lower overall project costs; with a view toward developing bug-free projects, free from issues.
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