Developing your agile analytics approach

Agile project management is a multifaceted topic, and as such, there is far too much information to cover in one article. This article is the first part in a series to go over the benefits, approach and structure of using the agile methodology for analytics projects. For more information on this topic, read Optimizing your agile analytics scrum team.

Most businesses have vast amounts of company and consumer data; some are unsure of what they have, while others are unable to leverage theirs into strategic insights and take appropriate action. However, there are many that have already engaged in an analytics or data science project to collect, manage and apply such data.

Yet, not all analytics projects are successful in getting that valuable data into business users’ hands. As a business leader, can you imagine some statements you would not want to hear from stakeholders after engaging in such a project? Perhaps they are:

• “This is not what we asked for”
• “Why did this take so long?”
• “Isn’t [insert department or individual’s name here] providing this already?”

These kinds of reactions can be personally discouraging and even detrimental to such projects and the teams that work on those. So, what can be done to try and avoid this? Leveraging an agile project management methodology that engages users early and often, allowing for changes based on feedback, is a valuable tool in successful analytics project delivery.

What is an agile project management method?

Agile project management takes an iterative approach, relying on and incorporating user feedback in each release cycle. As such, the highest priority in agile is, “to satisfy the customer through early and continuous delivery of valuable software (1).” However, this methodology does not apply solely to software development. The key points of the Agile Manifesto apply directly to delivering analytics to business users (2):

• Individuals and interactions over processes and tools
• Working software