Business Analytics - History and Evolution

What is Business Analytics?

In a simple term; business analytics is analysing business data. The process takes historical data as input, processes it and gives results. Business analytics provides data insights to frame business strategies.

The current time is of data; every organisation is running with business data. Business Analytics uses statistical methods and modern technologies to analyse past data. Based on processed data results, fruitful business strategies can be framed to nurture the organisation and to gain more and more profits.

Analytical results allow organizations to identify trends, patterns, and correlations to frame informed decisions and business strategies. Business analytics is applicable in almost all of the areas including sales, marketing, finance, operations, and customer service.

Characteristics of Business Analytics

Some key characteristics of business analytics are as follows −

Data-Driven Approach − Business analytics is a data-driven approach which includes data processing, and data analysis.
Statistical Analysis − Business analytics includes statistical methods and quantitative techniques to find the results.
Data Mining − It is a process of extracting data from different sources and finding trends and relationships in data sets.
Predictive Modeling − It uses predictive models and algorithms to determine future trends based on historical data. The outcomes of predictive models help to frame business decisions.
Real-Time Analysis − The business analytics process can do real-time analysis of real-time data and produce quick and accurate results.
.Visualization − Visualization techniques are used to show the processed data results in graphical forms like reports, charts, graphs, and dashboards.
Reporting − The reporting feature of business analytics summarizes and presents data insights.
Data Integration − It integrates data technologies and platforms to manage different types of data to produce results like unstructured data from various sources.

Significances of Business Analytics

Some of the key significances of business analytics are as follows −

Data evaluation − Organizations can find efficiencies, optimize operations, and save money by evaluating data from different processes. This leads to greater productivity and profit. Analytics enables the monitoring and optimization of marketing initiatives, ensuring that resources are directed toward the most effective channels and tactics.
Converts data into valuable insights − Business analytics converts data into valuable insights that can be used to make strategic and operational choices, resulting in improved business outcomes and a sustained competitive advantage.
Contributes to planning − Business analytics contributes to financial planning, forecasting, and budgeting. It provides data insights for financial performance and allows its users to make more effective resource allocations. Human resource analytics results in talent acquisition, employee retention, and performance management.
Data-driven decisions − Business analytics allows businesses to make data-driven decisions rather than guesswork which makes more accurate and consistent results. It aids in the identification of trends and patterns in data which shows an understanding of market dynamics, client behaviour, and operational efficiency.
Supports decision-making − Business organisations can make fruitful decisions to compete in a rapidly changing market where new competitors emerge regularly and customers' opinions frequently change.
Significant to organizations − Business analytics is more significant to those organizations which prioritize business analytics to frame organisations' strategies using data-driven analysis.
Retain organisations in a competitive market − Organizations that use analytics well can retain themselves in a competitive market change by better understanding their customers and running more efficiently.

Business analytics emphasizes on prescriptive analytics, which integrates data mining, modelling, and machine learning to predict future events. Essentially, business intelligence addresses some common questions of businesses for organisations like "What happened?" and "What needs to change?" Business analytics also answers questions like "Why is this happening?", "What if this trend continues?", "What will happen next?", and "What will happen if we change something?"

Types of Business Analytics

The four most popular types of business analytics are descriptive, diagnostic, predictive, and prescriptive.

1. Descriptive Analytics

Descriptive analytics analyses historical data to determine that what happened. It monitors key performance indicators to deliver effective outcomes. Descriptive analytics includes data aggregation, data mining, data visualization, dashboards, and reports.

Examples of Descriptive Analytics

Some common and easy-to-understand examples of descriptive analytics are −

Monthly sales reports of an organisation showing trends and patterns over the last year’s sales.
Summarizes historical data, exchange of data, and social media usage.
Reporting general trends.

2. Diagnostic Analytics

Diagnostic analytics answers why happened? A user may understand the driving causes using correlations, data mining, drill-downs, and data discovery. Diagnostic analytics is most widely used in marketing, finance, cyber security, and other areas, this advanced analytics technique is typically used as a step before Descriptive Analytics.

Examples of Diagnostic Analytics

Some common and easy-to-understand examples of Diagnostic analytics are as follows −

To analyse why a marketing campaign led to an increase the sales in a particular region
Examine technical issues
Illumination on customer behaviour
Improving organization culture

3. Predictive Analytics

Predictive analytics uses past data trends to estimate the probability of occurring an event. It uses statistical modelling and machine learning techniques to predict the events. Overall, predictive analytics processes historical data to get to know future outcomes. It supports organizations by anticipating events, trends, and behaviours and gives a direction to them to make informed decisions and proactive strategies.

Examples of Predictive Analytics

Some common and easy-to-understand examples of Predictive analytics are as follows −

Forecasting future sales based on historical data
Predict customer choices
Product Recommendation

4. Prescriptive Analytics

To attain desired results and recommend strategies to achieve desired outcomes or optimize processes; prescriptive analytics suggests courses of action and tactics. It uses historical data to make suggestions on how to manage comparable future circumstances. It gives you insights into what may happen, when, and why.

Examples of Prescriptive Analytics

Some common and easy-to-understand examples of Prescriptive analytics are as follows −

Recommends the best business strategy to minimize costs and maximize efficiency
Improving equipment management
Suggest the best course of action
Price modelling