4 reasons why big data needs to get smaller

Big data analytics has the potential to transform the way we work. It makes us take better decisions, predict failures, and unlock new revenue sources.

But are we using big data effectively? The latest advancements in technology is resulting in us generating much more data than we can possibly use or store. It is getting too big for us to handle, but the truth is that we only need a small cross section of this data for the questions that we need answers to.

Below are 4 reasons why BIG DATA needs to get SMALLER.

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Summary

Infrastructure Shortage

There is only so much we can store in data lakes and server farms

By 2020, the amount of data is expected to grow to 44 trillion GB.

At what point will we run out of infrastructure to store so much data?

Artificial Intelligence & Machine Learning

AI & ML programs are most effective when data is small & focused

Artificial Intelligence or Machine Learning algorithms require focused data sets to solve specific business challenges.

Larger data sets are not only difficult to process, label, and manage, but are a headache when it comes to regulatory compliance.

Real-Time Insights

Your Cloud ain’t helping you here!

With digital twins and advanced analytics coming in, companies are gunning for real-time insights. This requires computing at the edge.

Edge computing with today’s processing power can only manage small chunks of data.

Affordability

Can we really afford Big Data?

Companies neither have the time nor the money to index and collect information on millions of records.

The trick is to identify the right use-cases and capture focused data streams that will lead to a higher RoV (Return on Value).

Does Big Data Have a Place

Focus should be on small data programs

This doesn’t go to say that big data doesn’t have a place.

Small data serves as the foundational building block to big data programs.

If companies don’t find ways to perfect ‘small data’ programs, efforts to go big will most likely be a failure.