The Difficulties Of Operationalizing Big Data

Big Data Management

Operationalizing Big Data

Practically everything is digital now – if not at the moment, it definitely will be in the near future.

This big change caused the creation of something we call Big Data. Each time people use their smartphone, create a new profile on a website, purchase an item or update their social platform, the amount of available data grows.

Most companies have become aware of the massive potential of using Big Data, but detailed analysis of such data needs to become the norm in this modern age. However, many are experiencing difficulties with data consolidation, and don’t really know how to operationalize Big Data. There are a number of problems associated with this process that need to be addressed.

big-data-infographic

(Infographic Source: Domo)

The Numbers Are Huge

An amazing and a bit of a scary fact about Big Data is that over 2.5 exabytes of new data is created on a daily basis. But that’s not all – those numbers are growing month after month. It’s very difficult to comprehend the amount of information available, let alone manage it properly. This information, which eventually creates a data pond, doesn’t come just through the internet, but also through customer data collected by businesses and large corporations.

During the last 20 years or so, businesses have based their advertising strategies on this type of data. In one survey that has been conducted recently, with about 1000 participants from different industries, over 70% of them stated that Big Data is of crucial importance to their business.

Diversity Is a Problematic Factor

If something is measurable, it can be managed. Obviously, we’re aware of the growing amounts of data arriving each second – that’s actually equal to all of the information stored on the internet twenty years ago – but that doesn’t make things any easier, because of one problematic little factor – diversity.

It doesn’t really matter where the data is coming from – social platforms, GPS signals, smartphones, etc. – because most of it has one thing in common; they are relatively new. This incredibly fast jump forward in all areas of technology makes it quite difficult for Big Data to be properly stored and efficiently managed.

Most businesses are starting to migrate to the cloud. As this continues to happen, the diversity of Big Data will keep on growing, expanding and become more and more difficult to control.

Most Companies don’t have the Proper Methodology

The way that companies currently control and manage these huge data sets is by placing them in a pattern that can be properly analyzed by already existing methodologies. As we just mentioned – the way business is done is changing, so it’s a matter of time when this will turn into a huge problem. It’s not enough to just load a large amount of data without an efficient way of structuring it to allow for greater accessibility and information.

Where to Start?

The general problem with Big Data is that it’s being improperly structured. Well, actually, not structured at all. Look at it this way – imagine a huge storage space that’s filled with piles and piles of items that are nothing alike. The first thing to do here is to get those piles sorted into categories.

Things like metadata tags that would allow you to structure the data more efficiently, and rules that can be set up to determine the way certain types of data need to be stored and modified, and if any information needs to be masked. In other words, the data that is loaded has to form a comprehensive “skeleton” that allows data analysts to quickly extrapolate tactics and strategies. Additional attention should be paid to securing all this information.

All in all, some changes are in order. Each and every customer or client is a data transmitter, so to speak, and are witnessing a sort of informational evolution. With huge amounts of information gathering into data lakes, it is important to set ground rules that help create a basic structure for storing all the available data, so that it can be efficiently accessed and analyzed. After all, it is the monotonous grunt work that sets the stage for grand business strategies.

By Pavle Dinic

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