How to Build a Successful Data Analytics Platform

Business intelligence, advanced analytics, machine learning—each requires a robust data platform to be effective.

For some enterprises, building a data analytics platform is not always successful. Why? In general, it’s for one of these three reasons.

Why data analytics platforms go wrong

1. Data teams go rogue

Acceleration is the key to staying competitive in today’s business, which is why it can be tempting for data teams to build out their data platforms without communication from IT. 

This is a recipe for strife within an enterprise. It’s also an excellent way to get a project canceled before it even gets a chance to achieve liftoff.

Click here to learn more about how leveraging advanced analytics can help your business stay ahead of the competition.

2. Doing too little or too much

Every analytics or machine learning project is best served by starting with a specific project. However, sometimes that project—the proof of concept—is too trivial to demonstrate results that provide value to the business. 

Other times, enterprises try to boil the ocean and take on a year-long project that ends up burning through time and budget, leaving the data platform to flounder before it’s provided any results.

Want to learn more about how to be more successful with machine learning? Read our in-depth guide to AI/ML here.

3. Infrastructure time suck

While you certainly don’t want to rush things when it comes to building out a modern data analytics platform, spending too much time on setting up the scaffolding and ensuring every little component is right can leave everyone fatigued. It can also lead to sticker shock before an enterprise is even ready to code.

A solution to these problems

The Redapt DPI 30 is designed to help enterprises avoid each of the three pitfalls when it comes to developing a data platform. 

The 30 in the name stands for 30 days, which is the length of time we work with enterprises to build a data platform that fits their needs.

The critical components of this service are the frameworks we’ve built to handle everything from setting up infrastructure and moving data into lakes, to processing data for reporting. 

The result of DPI 30 is a horizontal slice of data from the source through reports in a month. This slice then serves as the foundation for an enterprise to scale their operations, whether its business intelligence, advanced analytics, or machine learning.

DPI 30 in action

One of the enterprises we’ve worked within the past is a major restaurant group with a nationwide footprint. This company was burdened by legacy point-of-sale systems. In addition, none of the various franchises controlled by the group were able to communicate with each other effectively. 

Through DPI 30, we were able to help the business centralize its data into a single movement pipeline to utilize analytics and machine learning to run the organization better. This pipeline provided segregated sets of data for each of the company’s franchises without the need for four separate data warehouses. 

As a result of this new approach, within 30 days, the enterprise was able to accelerate its analytics while also making it possible for IT to be more of an innovation partner rather than a utility company within the organization.

To learn more about Redapt’s DPI 30, or for more information on building data platforms, contact one of our experts. Otherwise, you can read our in-depth guide to leveraging advanced analytics here to learn more before you dive in.