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May 16, 2016updated 22 Sep 2016 12:01pm

Big data analytics

Big data analytics can be vital to providing your business with a key differentiator.

By James Nunns

The technology sector has become renowned for its buzzwords, whether it is IoT, Cloud, big data or convergence. What these buzzwords don’t do is explain what exactly it is they are.

An area such as big data analytics is so vast that the buzzword merely over simplifies the area and aids in increasing complexity.

Essentially big data analytics is the process of analysing data sets in order to uncover hidden patterns, correlations, market trends and various other useful pieces of business information.

The term big data is typically applied to data sets whose size or type is beyond the ability of traditional databases to capture, manage, and process the data with low-latency. Big data can come from a variety of different sources such as from sensors, devices, video, networks, log files, transactional applications and from web and social media.

Big Data

Big data analytics has been around in one form or another for decades and often bleeds into the area of business intelligence (BI), something else that isn’t exactly new.

With some variations, big data analytics is often batch processing and it can also be real-time processing or streaming analytics.
In the end though its primary goal is to help businesses to use their data to become more well informed about their customers, decisions they should be making, improving efficiency and various other use cases.

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The outcomes from processing data can be used by analytics, researchers, and business users to gain insights that were previously unknown or inaccessible.

The big data analytics market has an extremely large ecosystem of vendors that includes the likes of Hortonworks and Cloudera in the Hadoop ecosystem and others such as IBM, HPE and others.

The majority of vendors offer some kind of analytics function, whether that is SAP, Oracle, Salesforce or EMC. Basically, finding tools to analyse data is not difficult, there is an abundance of variations to suit different business needs.


The variations in techniques can include using techniques for text analytics, machine learning, predictive analytics, data mining, and natural language process.

Whether a company wants to take its data to the cloud and process data in Amazon Web Services or Google Cloud Platform, to processing data on a platform from a MapR, or using Analytics-as-a-Service vendor such as Blue Yonder, the choices are numerous.

The challenge for businesses is to effectively plan and prepare so that they understand what exactly it is that they are aiming to achieve and choose the big data analytics tool that best fits.

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