What is Business Analytics?

Here are some examples to further illustrate this point.

Finance

A finance manager or director who looks after a department's finances or line of business would want to know the revenue, costs, profit margins, etc., for their line of business. On the other hand, a CFO would like to learn similar metrics at an aggregate level across all lines of business and be able to drill down into any line of business. The CFO might also want to know about interest expenses, the impact of currency exchange rates, taxes, etc., that might be beyond the scope of what a finance manager cares about

Marketing

A marketing manager responsible for demand generation would want to know the number of leads, opportunities, and closed deals. They would also examine how various online and offline demand generation channels perform. On the other hand, a marketing manager responsible for brand development would want to know how the company’s brand is perceived by its customers, partners, competitors, influencers, etc. A CMO would be interested in both brand and demand-related metrics and would want to know what the aggregate Return on Marketing Investment (ROMI) is

Sales

A sales manager with a territory and a quota to achieve would focus on their sales pipeline, which consists of opportunities created, won, and lost. They would also want to know the time it takes to close an opportunity to assess how many opportunities are needed to achieve quota targets. On the other hand, a VP of sales would want to know similar information at an aggregate level and be able to drill down to a sales rep or sales territory.

Operations

An operations manager focusing on a production line wants to ensure that products get out the door promptly while keeping defects to a minimum and maintaining the right inventory level to meet market demand. They would, therefore, want to know how many units are being processed in a production line, the time it takes for a unit to get through the process, the rate at which a process is delivering output, the number of units failing the quality test, etc.

Human resources

A human resources manager focusing on onboarding, retention, and offboarding employees would be interested in the number of open headcounts, the number of candidates in the interview pipeline, the number of employees leaving the company voluntarily or involuntarily, and other related statistics.

C-suite

A company's CEO examines all its facets and is interested in all the examples noted above. They want to be able to view aggregate metrics for every aspect of the business and drill down into a particular area to learn more. The CEO would also like to know how the company compares with similar companies in the market.

Business analytics involves several different types of data analysis. Each type helps organizations make informed decisions despite their increasing complexity and sophistication.

Descriptive analytics

Descriptive analytics tracks key performance indicators (KPIs) and other operational metrics to understand a business's current state. It analyzes past performance to answer the question, "What happened?" and summarizes historical data to identify trends, patterns, and insights.

For example, a retail company examines last quarter’s sales data to identify peak shopping seasons, popular products, and customer demographics.

Diagnostic analytics

Descriptive analytics looks for trends, but diagnostic analytics attempts to discover the reason behind trends. It goes beyond description to understand why something happened. It uses data mining, correlation analysis, and drill-down capabilities to uncover root causes.

For example, an e-commerce platform notices a drop in sales and uses diagnostic analytics to investigate. By analyzing cart abandonment rates and customer feedback, they discover that a recent website update slowed checkout, leading to lost sales.

Predictive analytics

Predictive analytics attempts to predict future trends. It leverages statistical modeling, machine learning, and AI to answer the question, "What will happen?" Analyzing historical data helps businesses anticipate trends, risks, and opportunities.

For example, a bank uses predictive analytics to assess customer credit risk. By analyzing past loan repayment histories, income levels, and spending patterns, the bank predicts the likelihood of default and adjusts its lending policies accordingly.

Prescriptive analytics

Prescriptive analytics uses predicted trends to inform business decisions. It goes further by recommending actions to optimize outcomes and improve business processes. It combines artificial intelligence (AI), machine learning (ML), and optimization algorithms to guide the organization's response to future challenges and opportunities.

For example, a logistics company uses prescriptive analytics to optimize delivery routes. By factoring in real-time traffic conditions, weather forecasts, and fuel costs, the system suggests the most efficient routes to minimize delivery time and expenses.

Cognitive analytics

Cognitive analytics uses AI, natural language processing (NLP), and deep learning to interpret unstructured data (text, images, videos) and provide human-like decision-making. AI systems analyze data after understanding the context and sentence meaning or recognizing certain objects in an image and improve their decision-making over time. Cognitive analytics reveals specific patterns and connections that simple analytics cannot.

For example, a customer service chatbot uses cognitive analytics to analyze customer queries, detect sentiment, and provide personalized responses, improving customer satisfaction.

Data analytics is an umbrella term for all types of data analysis. It includes everything from cleaning and processing data to complex modeling and visualization, regardless of whether the goal is business-related. Business analytics is a specialized subset of data analytics focusing on solving business problems and driving operational improvements.

Applications

Business analytics focuses on decision-making, profitability, and operational efficiency. It is typically applied in corporate settings where data drives strategic actions. In contrast, data analytics can be more exploratory, aiming to uncover patterns and insights that may not necessarily have an immediate business application. It is used in scientific discovery, social research, and engineering problem-solving.

Examples

A company uses business analysis to gain insights into customer purchase behavior and recommend personalized products, improving future outcomes for the business. In contrast, a researcher using data analytics might analyze satellite images to identify deforestation and climate change patterns or use public health data to predict disease outbreaks.

To reap the benefits of business analytics, you need three things.

Focus

Ask questions that are relevant to your business. It is easy to fall into the trap of asking irrelevant questions that may lead you down the wrong path or make you do a lot of work to get answers to questions that don’t help you.

Data

Access to accurate data to help you answer the questions is often easier said than done. To get the data you want, you must instill a data-oriented culture in the organization (top-down and bottom-up) and have data management processes to capture data faithfully and accurately.

Systems & tools

Have the means to process and analyze data. We live in an information economy where businesses collect data in terabytes and petabytes, which are located in disparate databases tied to various hardware and software systems. You will need systems or tools to help you extract the data, process it, analyze it, and visualize it later.