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The Power of Agile Analytics

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Understanding The Need For Agile Analytics

Trusted and governed Analytics require agile methodologies, there’s no doubt about it. They are a vital part of any organization in its journey to become data-driven. But to understand why agile analytics is so valuable to organizations, we need to step back in time to understand where the need for it came from. 

Back in “pre-historic” times, the same people in IT would be the report developers and provide the reports to users. The users would then be able to refresh the report built for them. The level of “self-service” was that users could filter the data and put some parameters, but that was the extent of it. Back then, it did the job but the side effects were that it was slow. There was no agility or technology available to do this and equally, it caused issues because reports were built by IT who didn’t have the full business knowledge to understand properly what was requested.

Today, business and technological changes have enabled so many more possibilities. People can’t afford to wait, they need to trust their Analytics from the word go and they want to be free to do it themselves. Let’s take a look at what organizations can put in place to be more agile in their Analytics operations. 

 

An Introduction To The Agile Manifesto 

Back in 2001, 17 representatives from the software world created the Agile Manifesto to help people to think about software development and organizations in new and more agile ways. Through this work and the creation of Agile Alliance, these professionals came to value:

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Rather than focusing on fixed processes, agility is about focusing on what’s best for the customer and delivering something timely and “as promised”. You see that the Agile Manifesto dates back to 2001 and so agility isn’t a new concept, but it’s a concept that is now becoming more and more popular due to today’s need to deliver, for example in the context of Data Analytics, more and more quickly. The manifesto defines 12 key principles of agile, which are far from what traditional corporate organizations stand by. But times have changed and organizations need to adapt to the market changes. 

 

Key Strategies For Agile Analytics

Often agile can be interpreted as ad hoc, undisciplined or wayward but in fact, it’s the ability to create a nimble and adaptable environment that is the true meaning behind the word. In Analytics, this nimbleness and flexibility are paramount in delivering what you promised to your customers or users because the environment is constantly changing. We’ve gathered some of the key strategies that you can put in place in order to be more agile in your Data Analytics:

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CI/CD

CI/CD is the combined practice of continuous integration and continuous delivery/deployment that allows organizations to bridge the gap between development and operation activities through building, testing, and deploying. When applied to Analytics, CI/CD works on the notion of “my dashboard works today but will it still be working tomorrow?”. It requires continuous testing from the very beginning of the dashboard development stage so that when it is finally pushed to production, there are no doubts that the Analytics displayed are accurate, up-to-date, and performant. However, to make sure dashboards are consistently accurate, continuously monitoring and measuring Analytics is imperative. You can discover more about the rise of CI/CD in Analytics in our article here.

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Test-Driven Development

Test-Driven Development (TDD) is widely used in many areas of software development but is becoming increasingly popular in the BI world. It involves writing test cases, then writing the code so that the test case passes, and repeating these steps on a regular basis. However, in the BI space, using TDD becomes more complicated due to the heavy reliance on third-party solutions such as Tableau, Power BI, etc, the nature of BI dashboards, and the different expertise of BI developers. With traditional web frameworks, even the most simple test cases can be difficult to set up. Nonetheless, it is possible with Wiiisdom Ops automated testing solutions.  

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Analytics Culture Creation

Part of encompassing agility is being flexible and allowing every user to consume and publish their own Analytics without the need for the IT team to hinder progress. To help towards this self-service approach, promoting an Analytics culture across an organization will empower people to embrace Analytics and eliminate any siloes between the Business and IT. In order for this to be successful, data literacy plays an important role. Data and Analytics leaders need to set the narrative for data literacy and demonstrate the business value it can add to all the teams involved. According to the Gartner Annual Chief Data Officer Survey, data literacy remains the second-biggest internal roadblock to the success of the CDO’s office. By ensuring a shared language and fluency of data and analytics, organizations will be another step closer to having agile analytics.

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Cross-Functional Collaboration 

Agility requires organizations to value humans over the process. They should be at the heart of the development and given the autonomy that they need. It requires close collaboration, in particular cross-functional collaboration with regular communication, self-organized teams, and a trusted environment they can work in. This collaboration will also help break down the siloes between the business and IT.

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Iterative Development

The agile way of development involves delivering often to allow for feedback on each delivery, instead of the traditional waterfall model where the end project is delivered after being designed, implemented, and tested. To get this frequent feedback, a minimum viable product (MVP) can be delivered; a product with enough features to do something with where the customer can test and feedback for iteration. When applied to Analytics it allows to build up data literacy and an Analytics culture within an organization thanks to the collaboration across teams. This is often referred to as the Agile Scrum methodology; a specific project management framework that focuses on continuous feedback to provide end-users more business value in a shorter amount of time.

Be More Agile 

Analytics has evolved over time with companies requiring faster Analytics at the highest quality to allow people across a whole company to consume it themselves. Agile methodologies such as CI/CD and TDD provide scalability, cost reduction, and transparency, all while taking away the problems of manual testing, high development costs, and a poor user experience. Agile Analytics needs to be embraced by organizations to ensure a self-service culture with Analytics everybody can trust. 

Are you ready to put in place agile methodologies? We can help. Get in touch with us today.

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