Analytics refers to the examination of data in order to discover useful patterns and insights. It differs from analysis in that it is heavily focused on large data sets, involving both statistics and mathematics and may also use quantitative analysis and predictive modelling. Individual analysis is often focused on processes and functions and requires smaller scale computational resources.
Today, analytics is a valuable tool for many businesses, specifically those that that use digital technologies. Business analytics utilises the vast amounts of data available in order to form insights that could prove beneficial across all aspects of an organisation’s output, such as sales, software development or customer support.
As data comes from a variety of sources, including smartphones, websites, emails, telephone calls and many other mediums, businesses can struggle not just with the amount of information that needs to be processed, but also the form and structure that it takes. As a result many companies are investing heavily in business analytics software and expertise in order to gain insights that will, hopefully, give their operations a competitive edge.
These tools will automatically collate and format the data being collected by a businesses, whether it originates internally and externally. Then, once businesses have an understanding of the kind of insights that they are pursuing, they can run the data through their analytics platform.
Analytics has potential uses across a broad range of industries, for example, the transportation sector. Every time a commuter uses their smart travel card, a piece of data is created. When this is combined with bus or train timetables, GPS tracking on mobile phones, and other transport influencers such as the weather, it adds up to a huge amount of unstructured data for local governments to process. Analytics software is used to make sense of these disparate data sets, so that useful insights can be gathered such as where transport bottlenecks and delays are likely to occur.
Another prominent aspect of analytics is using data to predict what is going to happen in the future, not simply making sense of the past. Predictive analytics uses the data that is available and runs a series of statistical procedures including data mining and machine learning to extrapolate potential trends.
The possibility to being able to predict future events before they occur is one of the major reasons that businesses are investing heavily in their analytics and Big Data programmes. If an organisation is able to foresee a trend before its competitor, the potential payoff could be considerable.
Other forms of analytics include web analytics, which specifically refers to websites and the monitoring of online traffic, and retail analytics focusing on sales figures and customer data.
In all of the above examples, the way that analytics tools display their outputs is crucial. These usually take some form of data visualisation, whether it is a chart or graph, but often businesses are able to customise their dashboard and data outputs in order to glean the insights that are important to them.
Due to the vast computational power required for analytics on a large scale, particularly when Big Data is involves, companies may decide to host their analytics solutions in the cloud for enhanced efficiency.
By purchasing analytics form a third-party service provider, organisations can alleviate some of the pressure on their in-house resources.
Digital technologies are now a part of almost every business and can no longer be relegated to the IT department. A huge amount of data is emerging as a result and analytics is the key to making sense of that data.