Get Started with Analytics

Analytics is the core of most data endeavors. Once your data is stored and transformed, you’re ready to generate reports, dashboards, and do ad hoc analysis on the data. Here are a couple of categories of analytics to understand your organization’s current state and help you understand where you want to be.

These are the categories:

While Predictive and prescriptive analytics (with machine learning) is exciting, companies often jump into it without focusing on the earlier stages.

The other category is how actionable is your analytics is going to be. Types of Analytics:

Prioritize Data Foundation: Before investing a ton of resources into machine learning, take the time to build a solid data foundation. This means setting up the best systems and architecture across the data engineering lifecycle, as well as the machine learning lifecycle.

More often than not, get good with descriptive and diagnostic analytics before moving to machine learning. Often, this is a good place to start. As a next step using operational analytics to help your departments to become more efficient.

For many companies a machine learning initiatives never succeeds because they never had the required data foundations in place. Ofcourse, when a company starts their their data journey, data engineers should keep the serving stage of the data engineering lifecycle in mind for future initiatives. But, with the advent of amazing tools and platforms an MVP approach is always the way to go. Basically, start small, learn from it and keep iterating towards more sophisticated solutions.

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