By: Markus Witcomb
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Big Data is the latest industry buzzword to describe large volumes of structured and unstructured data that can be difficult to process and analyse but could potentially be used by organisations to improve their efficiency and make more informed decisions.
Much of the tech industry follows Gartner’s ‘3Vs’ model to define Big Data. Data that is high in:
The volume of data organisations handle can progress from megabytes through to terabytes and even petabytes. In terms of velocity, data has gone from being handled in batches and periodically to having to be processed in real time. The variety of data has also diversified from simple tables and databases through to photo, web, mobile and social data, and the most challenging: unstructured data.
When data sets get so big that they cannot be analysed by traditional data processing application tools, it becomes known as ‘Big Data’.
As different companies have varied ceilings on how much data they can handle, depending on their database management tools, there is no set level where data becomes ‘big’.
This means that Big Data and analytics tend to go hand-in-hand, as without being able to analyse the data it becomes meaningless.
We are now able to collate huge amounts of data from all kinds of sources – websites, social media, customers, staff, financial and sales reports and more. But this information isn’t always easy to analyse and use to our advantage. However, Big Data is about using modern analysis techniques to combine datasets and contrast information in different ways and thus extract meaningful information that will support decision-making, quickly.
In today’s dynamic business environment, analytics can become very complex; aggregating data from multiple projects and applications and in various different formats into a single set for analysis is an intricate challenge. This is why single analytics platforms that are embedded within core operational systems are growing in popularity; they ensure key data are integrated, not stored in silos where their insights are lost.
There are two high-level approaches to analytics: real-time and batch. Real-time analytics perform analysis on live data, for example, a services project that is currently in progress, detecting issues or problems as they happen, allowing them to be rectified before they negatively impact the project. Batch analytics are generally used to identify trends over time or for historical analysis, with one or more data sets scheduled for analysis at a set time. Both these methods have their own advantages and uses in different contexts and their use is determined by the needs of the business.
Our real-time analytics platform gives you the ability to discover, interpret, communicate and action insights quickly and easily. Our platform gives you the ability to drill down into graphs and charts, in order to unlock more meaningful insights and take pre-emptive action sooner.
Our platform is used across a variety of industries, with out-of-the-box reporting templates ready to get you up and running in days, supporting businesses within the Recuritment, Education, Financial and Retail sectors make better informed decisions about their business and unlocking their growth potential.