5 reasons why you should use an open-source data analytics stack in 2020

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Open source data analytics

Open source data analytics-Today, almost every company is trying to be data-driven in some sense or the other. Businesses across all the major verticals such as healthcare, telecommunications, banking, insurance, retail, education, etc. make use of data to better understand their customers, optimize their business processes and, ultimately, maximize their profits.

This is a guest post sponsored by our friends at RudderStack.

When it comes to using data for analytics, companies face two major challenges:

Data tracking: Tracking the required data from a multitude of sources in order to get insights out of it. As an example, tracking customer activity data such as logins, signups, purchases, and even clicks such as bookmarks from platforms such as mobile apps and websites becomes an issue for many eCommerce businesses.

Building a link between the Data and Business Intelligence: Once data is acquired, transforming it and making it compatible for a BI tool can often prove to be a substantial challenge.

A well designed data analytics stack comes is essential in combating these challenges. It will ensure you’re well-placed to use the data at your disposal in more intelligent ways. It will help you drive more value.

What does a data analytics stack do?

data analytics stack is a combination of tools which when put together, allows you to bring together all of your data in one platform, and use it to get actionable insights that help in better decision-making.

Data analytics stack architecture example

Data analytics stack architecture example

As seen the diagram above illustrates, a data analytics stack is built upon three fundamental steps:

  1. Data Integration: This step involves collecting and blending data from multiple sources and transforming them in a compatible format, for storage. The sources could be as varied as a database (e.g. MySQL), an organization’s log files, or event data such as clicks, logins, bookmarks, etc from mobile apps or websites. A data analytics stack allows you to use all of such data together and use it to perform meaningful analytics.

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Article Credit: Packt