What is data analytics, and how is it used in business?

Organisations and individuals around the world create enormous amounts of data on a daily basis.  Lots of our personal data is created in the moment; a like on a social media post or a comment on a feed.

Often I’ve forgotten those interactions as quickly as I made them.  However, that data will have been stored, and whilst it won’t be very meaningful or valuable on its own, when added to other data to create a data set that will be growing bigger at an ever-increasing rate, it becomes a prized asset.  So how does that happen?

Data analytics makes raw data meaningful

It is important to understand that there is a difference between data, information and knowledge. Data is a collection of unorganised facts or figures that requires processing. Once that has happened, the data becomes information that can be used to enhance knowledge and be a basis for well-informed decision-making.  In essence, data analytics is the process of transforming raw data into knowledge by analysing it to identify patterns and trends which are meaningful.  These insights are valuable as they can provide organisations with competitive advantages.  For example, understanding the buying patterns of target customer groups can aid a range of business activities, from product development, marketing and customer retention to ensuring supply chains are managed efficiently.

Data analytics = business intelligence

Data analytics can be likened to a kind of business intelligence in as much as it can be used to identify opportunities in organisations to work smarter, be more focused and set priorities.  It does this by revealing trends that would otherwise be lost in the sheer volume of data, which can be used to predict the future and from which suggestions and recommendations can be made.

Four main types of data analytics

There are four main types of data analytics, all of which have slightly different roles in the overall process and can be used to answer different questions:

Descriptive analytics

Descriptive analytics involves the initial work of gathering data about what is happening now.  However, it may be more accurate to say, it looks at what happened in the past because data is out of date as soon as it is generated.   Analysts put the data into a format that summarises it and then looks for patterns in it.  However, they are not trying to explain the data or establish any cause-and-effect relationships.  Descriptive analytics simply determines and describes what happened and then presents it in a way that can be understood by non-technical as well as technical users.

Accountants use this type of analytics to create reports, maybe to understand an organisation’s income and expenditure, and to prepare financial statements.

Diagnostic analytics

Once descriptive analytics has established ‘what’ happened, then diagnostic analytics seeks to understand ‘why’ it happened.  This is done by looking for anomalies in the patterns that were identified by descriptive analytics and now trying to explain them.  For example, a spike or drop in revenue that is out of kilter with the general trend or expected seasonal variations.  Diagnostic analytics tries to uncover causal relationships.  It might involve looking at data from other sources to try and understand, for example, why sales spiked or dropped.  Maybe a celebrity endorsement coincided with a marketing campaign or perhaps unseasonably bad weather kept customers away.

Predictive analytics

The purpose of predictive analytics is to answer the question, ‘what is likely to happen in the future?’  It seeks to remove guesswork for decision-makers by coming up with insights grounded in data that are actionable.  For example, a manufacturing company may have analysed the runtime, downtime, and work queues for its machines so it understands the patterns in their usage and any anomalies that have arisen in the past.  It can then use predictive analytics to plan the machines’ workloads to optimise their efficiency.

Prescriptive analytics

Prescriptive analytics seeks to show how the outcomes that have been predicted can be exploited.  In other words, it suggested the best course of action to take.  Prescriptive analysts will explore a range of scenarios, given the predicted outcomes, and evaluate what decisions and/or actions an organisation might take.  A company may be considering increasing its service provision capacity, for example, so it requires more warehousing to meet predicted future demand.  Perhaps it could relocate to new premises or expand its current facilities?  What implications would that have for capital expenditure?  How long would it take to payback, given projected future costs and revenue?

Benefits of data analytics

Many of the techniques and processes of data analytics have been automated, aided by artificial intelligence (AI) and machine learning. Whilst this can pose some challenges, the technology is complex and is not always error-free, it does have significant benefits in the way of reducing the time needed to analyse and interpret vast amounts of data.  This in turn, means that decisions can be made quickly, enabling opportunities to be capitalised.

Gill Myers is a self-employed accounts consultant. She has taught AAT qualifications since 2005 and written numerous articles and e-learning resources.