What’s the Difference Between Big Data & Business Intelligence?

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Many people bandy around the terms “big data” and “business intelligence” as if they are interchangeable. In some sense, they have distinct similarities and overlaps, especially when speaking conceptually.

However, when getting down to the technical aspects, they are not anywhere near the same thing. In fact, “big data” and “business intelligence” refer to two completely different processes that occupy unique roles within the same general sphere.

To set the record straight, this blog explains the major differences between big data and business intelligence.

Defining Big Data and Business Intelligence

These two definitions get down to the core differences between big data and business intelligence:

  • Big Data collectively refers to the act of generating, capturing and usually processing enormous amounts of data on a continuing basis.
  • Business Intelligence collectively refers to software and systems that import data streams of any size and use them to generate informational displays that point towards specific decisions.

As you can see, big data is a much more generalized, genericized and all-encompassing term. It has caught wind because digital systems are generating more data than ever, and new approaches are needed to handle and store this data.

Just an example: Wal-Mart’s customer checkout systems generate more than 2.5 petabytes of data every single hour. One petabyte is equal to 1,000 terabytes (10,000 gigabytes), and it would generate enough text to fill 20 million standard-sized filing cabinets. Clearly, data streams like these must come up with methods to transfer, store, access and process this data without bringing computer servers to a grinding halt.

Business intelligence, on the other hand, can utilize data streams of any size to analyze it and display crucial information. This process is known as “analytics” because it analyzes and then digests data streams in a way that is both easier to understand and points more obviously toward needed actions based on said data.

One of the biggest reasons people get confused about big data is that big data analytics is often lumped in conceptually with the task of grappling big data. A good way to separate the two would be to think about them this way:

“Big data is like having a massive kitchen storeroom that keeps billions of different ingredients from getting lost, spoiled or disorganized. Analytics is like using thousands of these ingredients to prepare a delicious meal, combining them into one simple-seeming dish.”

You Don’t Need Big Data for BI

A major consequence of people getting confused about big data and business intelligence (BI) is that they think that they need big data-sized streams or big data processing capabilities to utilize BI analytics.

The truth is that just like any great dish can be made with just a few ingredients, any amount of data can be enough to take advantage of it using BI software. This software takes an organization’s raw data from any type of data storage system or data warehouse and provides it with useful and relevant reports, graphs and charts – utilizing dashboards for easier access – for decision-makers to use to analyze business trends. Multiple datasets can be mined for relevant data so business leaders can make informed decisions.

For this reason, businesses of any size can benefit from what BI has to offer. Do not count its capabilities out just because buzzwords like big data make BI feel outside your grasp. Educate yourself on the options available, and you will likely soon realize that there is a BI solution for you that can cheaply, efficiently and adeptly add value to your organization on a consistent basis.

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