Thursday, 27 August 2015

CIMA E3 - Big Data

There are many challenging E3 topics that you will need to understand in order to successfully pass your E3 exam. For example, Big Data is a subject which many students find difficult to grasp at first!

At Astranti we aim to make difficult subjects easy to understand. All of our materials are designed to ensure that our students are given in-depth knowledge of all the key subjects, but in a format that allows you to learn easily and effectively. 

As an example we'd like to share with you a new and updated section of our E3 Study Text which explains Dig Data in a straight forward and clear way. 

Big data and it's uses

Big data is a term used to describe sets of data so large that they simply cannot be analysed and interpreted by standard reporting facilities. The value of big data is that it allows you to draw from an enormous amount of different data as opposed to having many separate sets. As a result it can be possible to identify unusual business trends and correlations that would otherwise be impossible to spot.
Big data has the potential for almost universal application; here are some examples of big data being implemented in the real world:
  • Used by some hospitals to monitor patient details and the treatment sought, meaning they can assess the likelihood of readmission and if high make sure the issue is resolved there and then thus saving time and money further down the line.
  • Consumer goods companies monitoring facebook/twitter and as a result gaining key and an uninhibited insight into consumer behaviour which they then use in their marketing campaigns.
  • Governments can use them to measure crime rates as big data allows the inclusion of many other factors which in theory can help determine why crime rates are increasing/decreasing rather than just the fact that they are.

Gartner's Three Vs

In a 2001 research report Gartner outlined three key challenges faces organisations with their data. These three elements are:
Volume - increasing volumes of data mean there is a lot more to manage and it is harder to extract key information from it
Velocity – there is an increasing speed of data in and out, which means data can quickly change. This means that information analysis needs to be quick to spot and react to the latest change.
Variety – the range of data types and sources of data can be varied making analysis difficult. e.g. data in different IT systems in an organisation being hard to bring together to analyse linkages.
Gartner then came up with a formal definition of big data related to these 3Vs which is:

Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimisation.

The seven stages of the big data process

The seven key stages and challenges that make up the big data process are as follows:
Capture – What kind of data is needed and how is going to be captured. This is usually an indirect source (rather than manual data input), a prime example would be the barcode reader in a retail outlet.
Storage – As you might expect, the amount of data we are talking about cannot be simply saved on a laptop hard drive. Big data sets can require physical systems that take up entire rooms or even buildings. In addition to the sheer size needed both physically and memory-wise you will need to make sure the systems are adequately protected as you may have access to private customer information.
Curation – Once the data has been captured it then must be organised, controlled after and maintained in a way that allows it to be usable and re-usable, an on-going, day to day upkeep of the data in effect. This may involve the way it is structured on the system to enable it to be analysed.
Analysis – The process of interpreting the data, millions of bits of info means nothing unless you can use to help answer questions/illustrate results etc. This could be the ability to separate the data out by date, product, customer or make linkages between different types of data e.g. sales made by customer group at different times of the year.
Visualisation – The data which is analysed needs to be illustrated in a clear and digestible format so that it can be used to make decisions. This may take the form of graphs or condensed simple tables.
Search – When you have as much data as a big data system can compile you must find a way to search across the vast data landscape to find the info you want. An example of a search system would be google; which can accurately search through billions of web pages based on a few key search terms. Each 'big data' system needs it's own 'google' type search system to access the relevant data and help users access relevant information.
Data Sharing and Transfer – Data must be shared with those who need it so that relevant people can access the information produced and indeed relevant information is proactively sent to the people who can best use the information gained.
Big data as a strategic resource

Big data is increasingly becoming of strategic importance. As an example, retailers that understand their customers and their needs better by analysing big data are able to produce better products, target marketing campaigns better and price products in a way that attracts more custom based on past buying patterns. Together this can provide firms who use Big Data effectively a competitive advantage.

Astranti Financial Training.