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A Close Look at Agile Business Intelligence Best Practices

By Michelle Gardner | May 4, 2017
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Over the past decade, data science and business intelligence have become increasingly significant parts of our society. Michael Lewis’ “Moneyball” helped radically change the way baseball teams evaluate players and operate their franchises. Elections are analyzed—and sometimes even decided—based on business intelligence. And financial markets can move drastically based on data analytics.

Considering the impact data has on our lives, it is not surprising that a recent report issued by Zion Market Research projects that the global market for business intelligence will increase from $16.33 billion in 2015 to $26.50 billion by 2021, expanding at an 8.4 percent CAGR during that time period.

>> How to Optimize Your Development Lifecycle? Combine Agile and DevOps <<

As the market matures, businesses will see faster returns on business intelligence investments—as well as new challenges and pressures for application development teams. Faced with the pressure to deliver sophisticated dashboards and reports to their end users within their existing applications, many development teams are beginning to assimilate an agile framework into their business intelligence development cycles. Agile business intelligence rollouts are often more efficient and have fewer bugs than with other approaches.

How does agile business intelligence actually work? Below are some best practices to keep in mind:

Make UX Part of the Product Lifecycle: Business intelligence solutions can only produce ROI if they are successfully adopted. If your business intelligence solution doesn’t offer a quality user experience, widespread adoption becomes extremely challenging.

Above all else, users need to understand the value that agile business solutions provide them in their day-to-day work. If they find these tools clunky or confusing, employees are unlikely to ever clearly see their value. Integrating UX and design into agile business intelligence development allows teams to make tweaks and improvements during the entire lifecycle, ensuring users get a better product at the end of the process.

Right-Size Teams: The agile framework was created to drive speed and scalability, and agile business intelligence solutions are no exception. One of the most overlooked aspects of agile development is the size and makeup of product teams. Creating these teams is a delicate balance, as certain skill sets (coding, IT, project management, design, QA, etc.) are essential, but efficiency and scalability may be sacrificed when teams grow too large. In their efforts to build agile business intelligence solutions, organizations should look for well-rounded team members who are open to learning at least a little bit about the entire product lifecycle, even areas outside of their usual expertise.

Keep Sprints Consistent: Once a team decides on the length of its sprints—typically 2 to 4 weeks is optimal—it should try its best to leave that length in place unless there is a compelling and urgent reason to change it. Giving each team member defined commitments in regular time intervals is part of maintaining a culture of goal planning and accountability. And the deeper into the development cycle a team gets, the more it tends to settle into a rhythm that is created naturally by the consistency of sprint length—and it’s best not to disrupt that rhythm unless absolutely necessary.

 

About the Author

Michelle Gardner is the Content Marketing Manager at Logi Analytics. She has over a decade of experience writing and editing content, with a specialty in software and technology.

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