The Lifecycle of Data Analytics Projects is endless

Data analytics is the process of collecting, transforming, and presenting data to inform decision-making.

Developing an analytics solution begins with a requirements-gathering exercise.

From there the process continues to ingesting, processing, and exploring data.

Analysis and solution deployment are followed by requesting feedback from the business.

Finally, the analytics solution is optimized and the process begins again.

Deployment of an analytics solution may feel like a finish line, but it's important to evaluate the following questions to optimize the solution: 

If the product you have built doesn't adequately respond to their needs, there's work to be done.

Optimizing solutions and Implementing the feedback of your users is a logical first step to optimizing your analytics solution.

There may also be opportunities to remove latency in the process, for example, ensuring the data refresh occurs in the allotted time.

Optimization could also mean more accurately reflecting user needs by tweaking visual design or ensuring report visuals render quickly.

Begin again!

Exposing data and insights often leads to requests for more analysis, which leads to more feedback, and so on.

The data team must communicate with each other and be in dialogue with the business, to ensure solution development is responding to business needs and needs that may appear in the data.

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