What is Big Data analytics? A guide
Big data is a term that has been bandied around for the last five or so years and has become more mainstream as companies look to manage and take advantage of the enormous amounts of data they possess. But how do organisations go about analysing Big Data to help their business?
Big data refers to the vast volumes and different types of information that organisations can now collect and process using increasingly more powerful computer systems. This data can come from sources within an organisations or external sources, such as customers, suppliers, IoT sensors, and so on. It can be structured (for example, retail figures) or unstructured (such as internet search results).
While data should be put into a structured format to be analysed in a data warehouse, organisations are looking to technologies such as Hadoop, MapReduce, Spark and NoSQL databases that support the processing of large and varied data sets across grouped systems.
Big data can be analysed using mathematical and statistical algorithms to derive meaning from the it by unearthing buried patterns, unspecified correlations, trends, and consumer preferences. This information can in turn be used to make better, more informed business decisions.
Organisations need to be aware of the three types of analytics that can be deployed with Big Data.
The first is descriptive – for example, dashboards, notifications and alerts. These tell you what has happened in the past, but doesn’t give the reasons why it happened or what may change.
Next is predictive, which is a more useful form of analytics. This uses past data to model what might happen in the future. For example, how a customer may respond to marketing campaign or how sales could be affected by marketing conditions.
