Data Analytics

Data Analytics in Business: A Complete Overview

Written byJohn Terra
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Updated onFebruary 27, 2024

In these challenging economic times, businesses seek ways to secure a competitive edge and keep their commerce thriving. One of the primary keys to business success is making sound decisions, and one of the best ways of assuring your choices are good is to base them on accurate information. And for there to be accurate information, you need business analytics.

This article explores the subject of data analytics in business. We will define the term, explain its benefits, why it’s essential, and how data analytics can improve business management. We’ll also share to learn critical skills and tools through online data analytics training.

Let’s get the ball rolling by defining data analytics in business.

What is Data Analytics in Business?

Data analytics in business describes collecting, processing, analyzing, and interpreting vast volumes of data, extracting meaningful insights, trends, and patterns that can guide and inform strategic decisions, improve operational efficiency, and foster overall business growth. Data analytics involves using diverse techniques, tools, and methodologies to change raw data into actionable information, which can then be used to make better-informed choices and optimize different aspects of the business.

Also Read: Data Analytics Applications: Types, Use Cases, and Top Tools

How Do You Conduct Business Analytics?

Business analytics typically breaks down into the following steps:

Data Collection. Collecting relevant data from different sources, which includes marketing campaigns, customer interactions, operational processes, sales transactions, and external market data.
Data Processing. Cleaning, organizing, and preparing the collected data for analysis to ensure its accuracy and consistency.
Data Analysis. Applying mathematical, statistical, and machine learning techniques to discover correlations, patterns, and insights within the processed data.
Data Interpretation. Interpreting the data analysis results in deriving actionable insights and conclusions that can improve the likelihood of better decision-making.
Decision Making. Employing the insights from data analysis to make informed decisions that will impact different areas of the organization, such as marketing strategies, resource allocation, product development, etc.
Continuous Improvement. Monitoring and evaluating decision outcomes based on data analytics, then refining and adjusting the strategies over time-based on subsequently obtained data and insights.

The Difference Between Data Analytics and Business Analytics

Many people use business and data analytics interchangeably, but they are subtly different. Data analytics is a subset of business analytics, which uses data to analyze current and past business performances to obtain insights that help executives make better-informed decisions. Let’s show the differences by exploring what each analyst does:

Data Analysts

Work with business leaders and stakeholders to define problems or business needs
Identify and source data
Clean and prepare data for analysis
Analyze data, looking for patterns and trends
Visualize data to make it easier to comprehend
Present data so that it tells a compelling story

Business Analysts

Evaluate the company’s current functions and IT structures
Review processes and interview team members to identify critical areas for improvement
Present findings and recommendations to management and other appropriate stakeholders
Create visuals and financial models to support any business decisions
Train and coach staff in new systems

So, while both positions use data to make better business decisions, they take different paths up the mountain.

Also Read:Tutorial: Data Analysis in Excel

The Four Types of Data Analytics

All forms of data analytics fall under one of the following four categories.

Descriptive Analytics

Descriptive analytics asks, “What has happened?” Descriptive analytics doesn’t look forward but provides a comprehensive picture of past events unfolding. The chief advantage of descriptive data analysis is that it helps people understand what happened and why it happened. Typical examples include:

Sales performance
Dashboard reporting
Fraud detection
Product demand forecasts

Diagnostic Analytics

Diagnostic analytics asks, “Why did this happen?” It examines the factors that led to an event to answer why an issue occurred. This kind of analysis can help organizations understand what has happened, why, and how it can be prevented from happening again. Common examples of diagnostic analytics are:

Root cause analysis
Retrospective analysis
Drill-down
Regression analysis

Predictive Analytics

Predictive analytics asks, “What will happen in the future?” It uses existing data to forecast future outcomes or trends. Organizations typically use predictive analytics when developing new products or services since it gives them a good guess of what their customers will want in the future based on past behavior. Examples of Predictive analytics include:

Direct Marketing
Customer Pricing
Retail Sales Forecasting

Prescriptive Analytics

Prescriptive analysis asks, “What should we do?” This analysis form takes predictive analytics one step further by recommending future actions based on past trends and data. This form of data analysis is especially helpful in optimizing resources and spotting new business opportunities (e.g., expansion). Also, prescriptive analytics can be employed in making decisions or providing recommendations that let others make better decisions faster. For example, prescriptive models could recommend whether a business should:

Launch a new product line or end a current one
Construct a new factory or shut down an existing one
Put in a bid on a new project, and at what price
Hire additional staff in a given department
Send a targeted ad to particular customers

Why is Data Analytics Important for Business?

Data and business analytics lets businesses create reports and spot patterns to help organizations operate more efficiently. Analyzing relevant data can also enhance decision-making by allowing the company to predict industry trends or customers’ wants. These predictions help companies remain on the cutting edge and stay competitive.

Data analytics can also help understand the different types of customers who visit an establishment. For example, if a restaurant discovers that most customers are families with children, it may want to emphasize more family-friendly fare. On the other hand, if students make up most of the regular clientele, the restaurant may want to offer student discounts as part of their marketing incentives.

The restaurant could also use data analytics to assess employee performance based on the sales data collected from each server. If a particular server has low daily sales, the supervisor may want to check on the employee to see if they need more training or to perform up to expectations.

We live and work in the era of big data, where today’s business leaders have access to more information than ever. Analyzing (and monetizing) this information is an essential skill for any professional involved in leadership.

Also Read:What is Exploratory Data Analysis?

The Advantages of Data Analytics in Business

Data analytics can be a valuable tool today’s businesses can leverage to stay competitive and survive in rocky financial markets. The advantages of data analytics in business include:

Improving efficiency. Data analytics lets businesses collect vast amounts of data, which can then be analyzed and used to identify weaknesses in their business models. Companies don’t often notice inefficiency immediately because it’s easy to slip into (and perpetuate) bad habits or practices that may have worked at one point but don’t anymore. Also, organizations tend to focus on other things. However, inefficiency can cause a noticeable drain on profits and perhaps even lead to the business’s end. Efficiency is critical, but it can be challenging to spot inefficiencies. Data analytics can help.
Making better decisions. One of the primary advantages of data analytics in business is that it helps companies make better decisions. Understanding what has occurred in the past, what is happening now, and what could happen in the future can be a game-changing advantage for any business. Companies using data analytics can predict customer behaviors and needs, making them more likely to provide the kind of goods and services that customers prefer.
Reducing costs. Using the company’s data information is a great way to stay budget-conscious and help an organization run more efficiently, typically by pointing out underperforming elements.
Increasing revenue. Data analytics helps businesses increase their revenue by giving them insights into making better decisions regarding pricing and product offerings. Data analysis could show that most customers who buy a particular product also tend to buy another given product. A business could bundle these two products in a discounted package deal, and customers love a good deal!
Making the business more competitive. Data analytics allows businesses to move ahead of their competition by providing better insights into their customer base and how they can best reach them. Analytics also helps organizations identify what they’re doing wrong and how to change it.

How Can Data Analytics Improve Business Management?

Let’s run through a brief list of the ways data analytics can specifically help improve business management:

Customer insights. Data analytics offers a deeper understanding of customer behavior, buying patterns, and preferences, allowing businesses to tailor products and services to the consumers’ needs.
Competitive advantage. Leveraging data analytics gives businesses a competitive edge by staying current on market trends, responding quickly to changes, and outperforming their competitors.
Informed decision-making. Data analytics lets businesses strategically use real-time insights and trends, reducing reliance on guesswork, hunches, and intuition.
Identifying hidden opportunities. Organizations can discover otherwise hidden opportunities, emerging trends, and market gaps by analyzing large datasets, letting them acquire new revenue streams.
Operational efficiency. Businesses can use data analytics to optimize processes and workflows, identifying inefficiencies, bottlenecks, and areas for improvement.
Performance tracking. Businesses can monitor their key performance indicators (KPIs) via data analytics, letting them measure success, identify improvement areas, and adapt strategies as necessary.
Personalized marketing. A company can develop customized marketing campaigns that jibe with individual preferences by analyzing customer data, leading to higher engagement and conversion rates.
Predictive insights. Data analytics lets businesses predict future trends and outcomes, which can improve long-term planning and strategy development.
Resource allocation. Businesses can use data to assign resources more effectively, understanding which initiatives yield the best returns, thus ensuring optimal resource utilization.
Risk management. Data analytics helps identify risks and vulnerabilities by analyzing historical data, allowing organizations to introduce proactive risk mitigation strategies.

Also Read: How To Become a Data Analytics Manager

Data Analytics Use Cases in Business

Here are a couple of use cases for data analytics in business.

Customer Service

Without customers, businesses have nothing. Companies can employ data analytics and artificial intelligence to gain deeper insights into customer behavior. Use cases include:

Providing customers with personalized content and specifically tailored recommendations
Identifying common complaints that customers have regarding specific products or services
Reducing the cost of delivering support (e.g., providing self-service options)
Resolving issues faster and more effectively using a better understanding of customer history and needs
Predicting what products or services the customer will probably buy next
Automating processes like payment processing and fraud detection

Marketing and Sales

Data analytics truly shines in this area. More companies are increasingly turning to data analytics to facilitate marketing and sales. Both sectors benefit from the use of data analytics in separate ways:

Determining the effectiveness of different advertising and marketing campaigns
Finding the best combination of products for a given customer
Finding the best price for a specific product or product bundle
Identifying which customers will be most likely to respond to a given offer
Identifying new markets for existing goods and services

Human Resources

Employees are a significant investment for any business or organization, so investing in data analytics helps management assemble a more efficient workforce.

Analyzing employee performance, retention risks, and attrition patterns
Assessing training and development needs
Evaluating a training program’s effectiveness
Determining the impact of internal promotions on employee morale
Making better hiring/promoting decisions by analyzing past employee performance and recruitment campaigns to find the best methods for attracting top talent
Spotting trends that highlight possible issues with staff retention

Here’s How You Can Learn More About Data Analytics

If you’re interested in learning how to apply data analytics in business, consider this 24-week data analytics bootcamp. Through live online instructor-led sessions and hands-on projects, you will learn methods of transforming raw data into actionable insights using various tools and technologies. Additionally, you will study generative AI and prompt engineering and gain practical exposure to tools such as ChatGPT, DALL-E, and Midjourney.

Indeed.com reports that data analysts can earn an annual average of $76,787. If you want a career change or want to improve your data analytics skills, check out this highly instructive online course.