Agile Methodologies in Data Analysis Projects

Agile management has evolved tremendously and has impacted the world of work significantly since it was implemented in software development back in 2001. Today, this form of management can be felt in all types of organizations. In essence, there is now Agile in retail, manufacturing, organizational culture, human resources, and petroleum. Data analysis likewise has not been left behind, given the ever-increasing adoption of Agile in the field. 

What Is Agile Exactly?

According to Ahmed Sidky, Agile is a mindset described by the four values and twelve principles contained in the Agile manifesto of 2001 and manifested through an unlimited number of practices, tools, and processes. Therefore, implementing the methods, tools, and procedures without the Agile mindset, values, and principles highlighted in the manifesto is not Agile.  

Scrum was the most renowned Agile methodology during the formative times of the manifesto. It focused on sizeable groups that self-organize and work in brief stages under a solution owner and scrum master. That said, the typical characteristics of Agile include:

  • Prioritizing the delivery of value to clients.
  • Work is done by sizeable groups that self-organize and operate in brief cycles (sprints.)
  • An ever-ceasing endeavor to eliminate cumbersome regulation processes and top-down management. Therefore, the company functions as a cooperating network of teams, all keen on giving value to clients.
  • Adjusting to tweaks over following a plan. 
  • Emphasis on collaborating with customers over negotiating contracts. 
  • Functioning software over comprehensive documentation.
  • Teams and cooperation over tools and processes. 

What is the future of Agile? As the globe continues to become more data-driven, there have been claims that the Agile methodology might soon become irrelevant. However, this forecast cannot be further from the truth, given the constant evolution of Agile. More specifically, there are massive rapid changes and growing complexities that are sure to make the Agile revolution unstoppable.

Agile and Business Intelligence

BI refers to the technologies, applications, and strategies for collecting, integrating, analyzing, and presenting business info. When the word Agile is combined with bus. intelligence, we get Agile bus. intelligence. Simply, it is an approach to intelligence that entails Agile software methodologies to boost the results of bus. intelligence strategies. 

How does the Agile approach support bus. intelligence projects? For one, since Agile bus. intelligence projects are developed, tested, and implemented continually, they allow for frequent improvements, thus enabling a firm to adapt to shifting market conditions and organizational goals faster. Moreover, Agile bus. intelligence enables companies to minimize costs while also fostering a culture of understanding and flexibility. 

Data Analysis Is One of the Biggest IT Trends and Is Expected to Grow

As the world becomes more insights-driven, the adoption of data analysis by companies has skyrocketed. It is mainly because, by evaluating various sets of data, firms can derive useful insights that go a long way in driving revenues through sales intelligence and developing strategies for effective expansion. 

According to Research and Markets, the global analytics tools and business intelligence software markets are forecasted to reach $26.78 billion by 2020. In light of this statistic, it is safe to conclude that data analysis projects will continue to play an integral role in business. 

So, as we wrap up, note that it makes sense to consider complementary solutions, more specifically, Agile methodologies, to ensure that the most will be gained from analyzing data.

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