What's big data analytics and why's it so important?

Utilising big data insights is an important aspect of every successful organisation, with a lot of specialist work that goes into uncovering said information.

Unfortunately, only 30% of companies have a well-articulated data strategy in place – with many citing cultural challenges such as a lack of data literacy.

Therefore, if you have an interest in using data to drive decisions, knowing how to carry out big data analytics can help you establish yourself as a key player in your organisation’s future.

Understanding big data

Put simply, the term refers to data that’s too large, complex, or that’s arriving at too great a speed to be handled by traditional data processing software.

These three defining features are often referred to as the ‘three V’s’ of big data:

• Volume: Organisations gather a massive amount of data from a variety of sources, such as smart devices, social media feeds, electronic check-ins, and so on

• Variety: Data comes in many formats, from structured data via traditional databases to unstructured or semi-structured data like audio that needs pre-processing for its insights

• Velocity: Data is being streamed into organisations at incredible speeds; it needs to be processed efficiently or even in near real-time, depending on its purpose

Nearly every department in a company, regardless of industry, can benefit from the gathering of big data, but handling it can be a challenge. That’s why data specialists are in such high demand around the world.

• Internet users generate about 2.5 quintillion bytes of data per day - Forbes

• The big data market is predicted to grow to USD 103 billion by 2027 - Statista

• 90% of business leaders say data literacy is key to company success - Harvard Business Review

Understanding big data analytics

As its name suggests, big data analytics is the process of examining big data to obtain new information such as hidden patterns, customer habits, and market trends.

An organisation’s data specialists are the individuals responsible for conducting big data analytics. They’ll engage in what’s known as the data preparation process, which consists of these four stages:

1. Collect: This involves gathering data from a number of sources - typically a mix of semi-structured and unstructured data

2. Process: After data is collected and stored, it must be organised and configured properly for analytical queries

3. Clean: Data specialists will also tidy up the data by looking for any errors or formatting mistakes that could impact the results

4. Analyse: Finally, the data will then be run through advanced analytics software to extract insights from it

There are a wide a range of analytics processes that a data professional might use to gather these insights. Some examples include machine learning, predictive analytics, and data mining.

Making the most of data

Big data analytics helps organisations identify opportunities and tackle problems that they may not have been aware of otherwise.

Data-driven decisions can help businesses avoid making costly missteps. The speed of the analytics software used to feed into these decisions allows organisations to act – and react – faster as well.

This can lead to better customer service, new product iterations, more effective marketing, and enhanced operational efficiency. In today’s competitive global market, having such an advantage over similar businesses can be a definite gamechanger.

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