Organisations of all sizes across a range of industries generate vast amounts of data which needs to be analysed for it to be effectively interpreted and monitored, so in turn, it can be used to improve the performance of a business.
Information is everywhere, whether it be within statistics, academic papers or online articles, for example. Companies use data to improve their productivity and efficiency and to make effective decisions and drive better financial performance.
The role of business analytics is to use quantitative methods to obtain meaning from data to make well-informed business decisions.
According to the Institute of Analytics, better user experiences are now driving greater organisational intelligence and therefore improving the use of data in this continually developing industry.
The four main analysis types can be used individually or together to analyse past efforts and improve future business performance. These include:
Descriptive: In which historical data is used to identify patterns and trends.
Diagnostic: Where historical data is interpreted to determine how and why something has occurred.
Predictive: In which statistics are used to forecast outcomes in the future.
Prescriptive: Where users test techniques to determine which outcome will provide the best results in a particular scenario.
Getting the right qualifications in data analytics
Businesses rely on data in order to make crucial decisions about their products and services, whether they are small local companies or large multinationals. Organisations of all kinds need business analysts to help them capture, manage and interpret large datasets.
The proper qualification will prepare candidates for the challenges of this expanding sector of the workforce, and the online business analytics degree from Aston University will provide students with the knowledge they will need to start their careers.
The curriculum is designed to provide an understanding of data analytics and is relevant to a wide range of industries, including information technology, accounting and finance and advertising and sales.
However, for now, let’s keep it simple and take a deeper look into the different types of business analytics.
Descriptive analytics
Descriptive analytics involves breaking down data, then summarising its main characteristics and features, and then exploring what has happened, but not why or how it has happened. The findings are often presented using reports and visualisations such as histograms, pie charts, line graphs and box and whisker plots.
Generally regarded as a first step in understanding the data, it can describe patterns or trends but nothing more profound. It provides an excellent way to introduce analysts to previously unknown information. Descriptive analytics presents complex data in an easy-to-follow format, provides a direct measure of key data points, is inexpensive and does not require an in-depth knowledge of math to perform.
It also relies on data that companies already have and looks at a complete population rather than a sample, making it very accurate and faster than methods that involve data collection.
Data that can be summarised using descriptive analytics includes:
- Surveys
- Financial statements
- Website traffic
- Social media engagement
- Scientific findings
- Traffic reports
- Weather reports
Diagnostic analytics
Diagnostic analytics assists companies in better understanding the factors, both external and internal, that affect their outcomes, giving a comprehensive picture of individual situations so that organisations can make well-informed decisions. For example, if the organisation discovers that a specific marketing campaign produced higher sales, it can create similar campaigns for other products or services.
Some of the techniques used include:
Data drilling, in which diving deep into a dataset can show detailed information about which aspects of the data are driving the particular trends being tracked.
Data mining searches through large volumes of data to identify patterns and associations within it. This can be done manually or automatically using machine learning technology.
Correlations analysis is a technique that examines how strongly different variables are linked together – for example, the sale of ice cream and cold drinks may both rise steeply on hot days.
The stages of the diagnostic analysis process are:
Identifying anomalies and trends which are highlighted by descriptive analysis could require diagnostic analytics if the causes are not obvious.
Data discovery which looks for data that explains anomalies and trends could involve gathering both internal and external data. The latter could include finding new regulatory requirements, weather patterns or changes in supply chains that make an impact on trends.
Identifying causal relationships, although further investigations may be required to determine whether the associations discovered within the data are the real cause of the findings.
Predictive analytics
Predictive analytics makes predictions about future outcomes by using historical data in combination with statistical modelling, data mining and machine learning. Businesses often use it to work out patterns in data so that risks and opportunities can be identified.
Types of predictive modelling include:
Classification models which categorise data based on historical information, describing relationships within a specific set. These can be used, for example, to classify customers or prospects into groups.
Clustering models in which, for example, e-commerce sites can separate customers into similar groups, which are based on common features, thereby enabling companies to develop customised marketing strategies aimed at each one.
Time series models generally use data inputs at a specific time frequency, such as daily or weekly, in order to assess data for seasonality trends and cyclical behaviour. For example, a call centre could use this technique to forecast how many calls it will receive per hour.
Prescriptive analytics
Prescriptive analytics enables companies to analyse data and then provide recommendations on how to optimise business practices to suit many predicted outcomes. It takes what is known, analyses the data to predict what will happen and then suggests the best way forward based on a range of simulations. This helps data scientists and marketers understand what their data truly means and how it can be used.
Examples of where prescriptive analytics is used include investment decisions about venture capital, lead scoring within sales, algorithmic recommendations, fraud detection within banking and product development and improvement.
