Everyone seems to be talking about big data these days. The collection and analysis of data in huge quantities is having a significant effect on many organisations. Apparently, it can help them to achieve change; retain talent; improve their understanding of, and access to, customers; and identify the root causes of complex and costly problems. Many businesses are gaining a competitive advantage this way. But what's the big deal for CIMA students?
Under the 2015 CIMA syllabus, there are learning outcomes in El and E3 that refer specifically to big data (see table. next page). The topics concerned will therefore be examined in the objective tests. In addition, big-data analysis and its application can have far-reaching effects upon investment choices, the understanding of risk management and the strategic decision-making process, which means that big data is just as examinable in an integrated case study.
Note that big data also appears in the indicative syllabus content for E2 A (learning outcome 2b), P2 D (learning outcome 2b) and P3 B (learning outcome la), so it's an important topic throughout the syllabus.
The characteristics of big data
There is no single universally accepted definition for the term "big data", but in 2012 the Gartner IT consultancy defined it as "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".
A detailed understanding of the IT used to capture, store and analyse big data is beyond the scope of either El or E3, but big data requires exceptional processing power to crunch the vast quantities of data sufficiently quickly to enable effective decision-making in organisations. Most relational database management systems and desktop statistical management packages are not adequate for this task.
Big data is often also defined in terms of a set of distinguishing characteristics known as the "three Vs": volume, velocity and variety. The volume of material that an organisation generates and stores can come from a number of sources. It can include transaction-based data collected over several years, unstructured data collated from social media and/or increasing amounts of sensor data and material sent from machine to machine. As the cost of electronic storage space continues to fall, the challenge for organisations is to determine what is relevant in this huge amount of material and how to...