You’d be hard pressed to find a business today that doesn’t use analytics in some shape or form to inform business decisions and measure performance.
Worldwide spending on big data analytics solutions is predicted to be worth over $274.3 billion by 2022 – and it is not just large corporations investing. Research shows that nearly 70% of small businesses spend more than $10,000 a year on analytics to help them better understand their customers, markets and business processes.
The overwhelming majority of executives say that their organisation has achieved successful outcomes from Big Data and AI. Data can also have a big impact on your bottom line, with businesses who utilise big data increasing their profits by an average of 8-10%. Netflix reportedly saves $1 billion every year by using data analytics to improve its customer retention strategies.
So, what methods of data analysis are businesses using to generate these impressive results?
Descriptive, predictive and prescriptive analytics
Business Analytics is the process by which businesses use statistical methods and technologies for analysing data in order to gain insights and improve their strategic decision-making.
There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.
Whilst each of these methods are useful when used individually, they become especially powerful when used together.
Descriptive analytics
Descriptive analytics is the analysis of historical data using two key methods – data aggregation and data mining - which are used to uncover trends and patterns. Descriptive analytics is not used to draw inferences or make predictions about the future from its findings; rather it is concerned with representing what has happened in the past.
Descriptive analytics are often displayed using visual data representations like line, bar and pie charts and, although they give useful insights on its own, often act as a foundation for future analysis. Because descriptive analytics uses fairly simple analysis techniques, any findings should be easy for the wider business audience to understand.
For this reason, descriptive analytics form the core of the everyday reporting in many businesses. Annual revenue reports are a classic example of descriptive analytics, along with other reporting such as inventory, warehousing and sales data, which can be aggregated easily and provide a clear snapshot of a company’s operations. Another widely used example is social media and Google Analytics tools, which summarise certain groupings based on simple counts of events like clicks and likes.
Whilst descriptive data can be useful to quickly spot trends and patterns, the analysis has its limitations. Viewed in isolation, descriptive analytics may not give the full picture. For more insight, you need delve deeper.
Predictive analytics
Predictive analytics is a more advanced method of data analysis that uses probabilities to make assessments of what could happen in the future. Like descriptive analytics, prescriptive analytics uses data mining – however it also uses statistical modelling and machine learning techniques to identify the likelihood of future outcomes based on historical data. To make predictions, machine learning algorithms take existing data and attempt to fill in the missing data with the best possible guesses.
These predictions can then be used to solve problems and identify opportunities for growth. For example, organisations are using predictive analytics to prevent fraud by looking for patterns in criminal behaviour, optimising their marketing campaigns by spotting opportunities for cross selling and reducing risk by using past behaviours to predict which customers are most likely to default on payments.
Another branch of predictive analytics is deep learning, which mimics human decision-making processes to make even more sophisticated predictions. For example, through using multiple levels of social and environmental analysis, deep learning is being used to more accurately predict credit scores and, in the medical field, it is being used to sort digital medical images such as MRI scans and X-rays to provide an automated prediction for doctors to use in diagnosing patients.
Prescriptive analytics
Whilst predictive analytics shows companies the raw results of their potential actions, prescriptive analytics shows companies which option is the best.
The field of prescriptive analytics borrows heavily from mathematics and computer science, using a variety of statistical methods.
Although closely related to both descriptive and predictive analytics, prescriptive analytics emphasises actionable insights instead of data monitoring. This is achieved through gathering data from a range of descriptive and predictive sources and applying them to the decision-making process. Algorithms then create and re-create possible decision patterns that could affect an organisation in different ways.
What makes prescriptive analytics especially valuable is their ability to measure the repercussions of a decision based on different future scenarios and then recommend the best course of action to take to achieve a company’s goals.
The business benefit of using prescriptive analytics is huge. It enables teams to view the best course of action before making decisions, saving time and money whilst achieving optimal results.
Businesses that can harness the power of prescriptive analytics are using them in a variety of ways. For example, prescriptive analytics allow healthcare decision-makers to optimise business outcomes by recommending the best course of action for patients and providers. They also enable financial companies to know how much to reduce the cost of a product to attract new customers whilst keeping profits high.
A data-led future
Despite the clear benefits of using data analytics in decision making, many organisations are still lacking the skills they need to optimise them.
Data analytics is a complex discipline. Less than a quarter of businesses currently describe themselves as data driven and Forbes reports that nearly all businesses cite the need to manage unstructured data as being a problem for their organisation.
There is a growing skills gap for business professionals who can manipulate and interpret data.
“The ideal candidate for businesses in 2021 and beyond will be a person who can both understand and speak data — because in a few short years, data literacy will be something employers demand and expect. Those who want to get ahead are acquiring these talents now” says ThoughtSpot CEO Sudheesh Nair.
Studying for a Business Analytics online MSc gives you the data and decision analysis skills businesses need to turn big data into knowledge.
Run by the EQUIS-accredited Bath School of Management, the Business Analytics online MSc course offers you access to the latest data insights, management principles and industry expertise, providing you with essential skills to lead change in today’s data-focused industries.
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