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Monday, February 26, 2024

Why Data Orchestration is Vital for Analysis

The vast majority of companies worldwide have low business intelligence and analytics maturity. This, in turn, prevents them from getting the most out of their data. And you may not blame business leaders considering improving your company and analytics maturity isn’t a simple undertaking.

If you have been having issues in collecting data, then you should consider taking advantage of a process called data orchestration. Taking this approach will certainly help improve your company’s maturity without pushing yourself to the limit. But what is data orchestration in the first place?

In a nutshell, this is the process of taking soiled data from multiple data storage locations, combining and organizing, after which you make it available for data analysis tools. It allows companies to automate and streamline data-driven decision making with ease.

Software relied upon to execute data orchestration connects your storage systems together so that your data analysis tools have easy access to the necessary storage system when they need to. As for the platform handling data orchestration, it doesn’t act as another storage system. Rather, it is a completely new piece of data technology that gives it a distinct advantage when it comes to breaking down data silos.

With data orchestration, you will have to follow three crucial steps. First, your data orchestration tools need to understand and organize the current data and new, incoming data. Once this is done, the data orchestration tools data in different formats and transform it so the data is in one standard format.

And the most important process of orchestration is making the data available to the tools that require it. That’s what is commonly known as activation, and happens when orchestration tools send data to the tools that your company relies on to operate day-to-day. This ensures the needed data is already there when you need it.

Remember, data orchestration is the ideal option for most companies with multiple data systems since it doesn’t require any massive migration or additional storage locations for your data. Actually, this can at times leave you with just another data silo.

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