What Is Business Analytics? With Components, Types And Uses

Updated 22 September 2022

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Many data-driven businesses use business analytics to resolve their business problems and get a competitive market advantage over their business contenders. They employ skilled analysts to analyse their data and use the resulting insights to make informed business decisions. By understanding the purpose and benefits of business analytics, you can use them effectively to advance business goals. In this article, we answer, 'What is business analytics?', discuss the components of business analytics, list the types of business analytics, explain ways to use business analytics and provide examples.

What Is Business Analytics?

The simple answer to 'What is business analytics?' is that it is a technique for analysing historical business data. The data may come from sales, marketing, HR, finance and other organisational departments. It is analysed with the help of various methodologies like statistical analysis, data mining, visualisation, scenario modelling and predictive analytics. The data analysis businesses obtain from these methodologies can help them gain an essential understanding of their current and upcoming business practices. They can identify trends, patterns and causes and use this information to anticipate business outcomes. This can enable them to improve their strategic decision-making processes.

What Are The Components Of Business Analytics?

The main components of a typical business analytics dashboard are:

Data collection and aggregation: It is necessary to gather relevant business data from surveys, transactional tracking, interviews, focus groups, observation, online tracking, spreadsheets, social media and devices. Businesses organise, filter and keep this data in a centralised location such as a cloud database for easy access.
Data mining: The collected datasets undergo sorting with the help of data mining techniques like clustering, association, data cleaning, data visualisation, classification, prediction, machine learning, neural networks, data warehousing and outlier detection. Data mining enables businesses to improve market segmentation and identify patterns, trends and relationships.
Association: This is a technique of finding and examining associations and relationships between the items in the datasets. By using the association rule, it is possible to use an if-then statement to identify relationships between the items and predict the presence of an item based on the occurrence of associated items.
Text mining: This is the process of exploring and organising unstructured textual data and transforming it into a structured format. That makes it easier to analyse the textual data and identify keywords, topics, concepts, patterns, insights and other data attributes.
Forecasting: Forecasting is the process of analysing historical business data from specific periods or market research data gathered through surveys, polls, observation and focus groups to predict future consumer behaviours and market events. You might also know it as statistical analysis, as it uses statistical tools and techniques.
Optimisation: Optimisation is a process that businesses use to devise and implement new actionable business plans to improve operational efficiency, work performance and cost-effectiveness. They may perform simulation techniques to determine best-case scenarios and the optimisation methods and practices that can apply to internal and external business operations.
Data visualisation: Data visualisation involves representing business data in visual forms like charts, graphs, infographics, maps, images and animations. Such visualisation makes it easy for stakeholders to understand complex data, identify patterns and trends and obtain insights from them.

What Are The Types Of Business Analytics?

The types of business analytics are as follows:

Descriptive analytics

Descriptive analytics examines current and historical data to identify trends, patterns and relationships. The data is obtained from various sources using data aggregation and data mining techniques, and various charts can show visual representations of the resulting insights. Descriptive analytics make it possible to understand what is happening in the industry and why it is happening. Companies use it to get an overview of their business operations through data reports about inventory, warehousing, sales and annual revenues. They may also use social media and analytics tools to analyse clicks and likes.

Predictive analytics

Predictive analytics involves using data mining and creating predictive models with different statistical techniques to extract relevant information from datasets. If there is any missing data, the machine learning algorithms make informed guesses based on available data. Businesses can then get a predictive score for future outcomes that they can use to resolve issues, identify opportunities and make future organisational decisions. Predictive analytics also uses deep learning to imitate the way people make decisions and make more accurate predictions.

Prescriptive analytics

Prescriptive analytics involves gathering relevant historical data from descriptive and prescriptive sources and using various statistical methods to analyse it. That can give companies insights into their past performances and they can also get recommendations for handling similar situations in the future. Prescriptive analytics focuses on providing actionable insights and informing companies about the best options they can take in a given situation.

With the help of machine learning algorithms, it is possible to create a range of future decision patterns and determine the different ways in which they may affect the business. That can enable companies to make business decisions that are beneficial to them.

Ways To Use Business Analytics To Help A Business

Here is a list of ways to use business analytics:

To make informed business decisions

Companies can use business analytics to predict how internal and external changes might affect their business operations in the future. For example, a company may attempt to find out how changes to product varieties and prices can affect the consumer demand for those products. By referring to insights derived from available data, such as market trends and customer preferences, they can determine the steps they can take to adapt to these changes, mitigate potential risks and make profitable business decisions.

To improve the efficiency of business operations

As data analytics involves gathering and analysing relevant data about business operations, it can help companies find out about existing operational issues such as bottlenecks, inventory shortages and production delays. They can use historical supply chain data to predict the problems they might face in the future and take appropriate steps to handle them. For example, they can find out about the shortages of the raw materials they might face during specific seasons and holidays and arrange to obtain these in advance from different vendors to avoid production delays.

To reduce business risks and handle setbacks

Companies face a range of risks and setbacks regularly that, if not handled appropriately, can endanger their business operations and business existence. Some of these include customer theft, employee theft, employee safety, uncollected receivables and legal liabilities. With the use of business analytics, companies can better understand the security risks they face and the business losses they can incur due to them. The statistical models they use in business analytics can help them determine how to resolve security issues and handle setbacks.

To customise the customer experience

Data analytics helps companies process customer and sales data obtained from retail stores, e-commerce platforms, social media and various other channels. By analysing this data, companies can find out who their target customers are and get detailed insights into their buying habits. They can then use this knowledge of customer behaviour to deliver personalised customer experiences. They can create targeted promotional campaigns and increase sales by directing customers towards product categories that they are likely to buy.

To strengthen data security

Data breaches can enable hackers to steal corporate intellectual data and personal data. As a result, the targeted companies can be at the risk of operational downtime, loss of sensitive data, reputation damage, loss of revenue and legal actions from customers. By using data analytics, companies can review their audit logs and create statistical models to locate and patch vulnerabilities. The models can also work with monitoring systems to detect any abnormal activities in the computer systems and issue immediate alerts to prevent attacks.

What Are Some Examples Of Business Analytics?

The following are a few examples of business analytics:

Looking for criminal behaviour patterns among consumers to prevent retail and e-commerce fraud
Finding promotional opportunities in the target markets to cross-sell products and services
Optimising marketing campaigns to ensure that the advertising messages reach the target customers
Reviewing past behaviours of customers to determine which people are likely to default on payments
Using past business outcomes in specific areas to predict the chances of future business success
Analysing medical images, medical reports and patient medical history to recommend the best health treatment for patients
Determining if product price reductions can attract customers and lead to more sales