Business analytics refers to the methodology and practice of compiling, analyzing and interpreting data in order to develop data-driven insights that can then be used to make smarter, more impactful business decisions. All sorts of organizations across industries and sectors can benefit from making decisions informed by hard data.

Business analytics is also a field of study. You can major in this field as an undergraduate business student or continue your education in business analytics with a graduate business degree.

Grand Canyon University offers bachelor’s and master’s degrees teaching a wide range of business competencies, including business analytics. Enroll in the Bachelor of Science in Business Analytics degree or the Master of Science in Business Analytics degree and examine both foundational and advanced topics in data mining, data visualization and more. Fill out the form on this page to request a consultation with a university counselor who can help you plan your future at our private Christian college.

Business analytics can be useful across all departments of a company. From finance, operations, human resources and beyond, business analytics can help companies run more smoothly, reduce inefficiencies and improve their impact. For example, marketing executives can use business analytics-derived insights to better understand their target demographics and, consequently, develop more effective marketing campaigns.

There are many potential benefits of incorporating business analytics into a company's decision-making. These can include:

• Understand consumers better: Through business analytics, marketing and sales teams can develop a better understanding of the characteristics of their customers, as well as their buying behaviors and preferences. They can segment their target demographics into hyper-specific groupings and tailor marketing campaigns for each of them.
• Enhance the customer experience: To be successful, businesses need to thrill their customers. The customer experience is critical. Business analytics can help companies improve the customer experience by creating user-friendly customer service processes.
• Increase operational efficiency: Different departments of a company rely on standard processes and procedures, but these aren’t always efficient. Business analytics can identify inefficiencies and help streamline processes, reducing costs along the way.

In short, business analytics can help companies reduce costs, boost revenue, make better strategic decisions and build a loyal customer base.

All types of organizations can benefit from making smarter decisions informed by data. Take the hypothetical example of a toy company. The executives at the toy company want to expand their product line and make new toys available in time for the holiday shopping season. 

They could take one of the following actions:

1. Make a guess regarding which toys are most likely to be enticing to children and appealing to their parents.
2. Use data-fueled insights to evaluate trends in consumer demand and create new toys based on those sales trends and consumer behaviors.

The toy company executives might get lucky with the former route, but they’re more likely to achieve stronger sales with the latter strategy. In order to use the second strategy, the toy company executives need the insights produced by business analytics.

In short, business analytics can allow an organization to increase revenue and cut costs by making smarter decisions that are aligned with the organization’s strategic objectives.

Main Types of Analytics

In the field of business analytics, professionals rely on four primary types of analytics to interpret data and guide decision-making. The four main types of business analytics are:^{(See disclaimer 1)}

1. Descriptive: Business analysts use descriptive business analytics when they need to understand and summarize historical data, such as past consumer trends.
2. Diagnostic: When the data reveals a potential problem, such as a drop-off in sales, business analysts can use diagnostic analytics to find the reason. This involves applying data mining techniques to identify patterns and correlations. Diagnostic analytics can also identify new opportunities for the company and figure out how changing one area of the organization might affect another area.
3. Predictive: Not all data focuses on the past. Business analysts can use predictive analytics tools, like machine learning and artificial intelligence, to identify trends that are likely to happen in the future. Through predictive analytics, companies may identify possible market trends and consumer behavior in the future.
4. Prescriptive: Prescriptive analytics is a natural add-on to predictive analytics. Whereas predictive analytics tries to project future occurrences and trends, prescriptive analytics develops recommendations for actions to take in order to capitalize on anticipated future trends. Prescriptive analytics helps business leaders develop strategic responses to anticipated outcomes.

When comparing the main types of business analytics, it’s important to note that one type isn’t “better” than the others. Rather, all four are used together at varying times to generate robust business insights that fuel informed decision-making.

Business analysts rely on sophisticated tools and techniques — far beyond simple data review — to transform raw information into strategic insights. Some of the commonly used tools, approaches and techniques include the following:

• Text analysis: This approach uses statistics, linguistics and machine learning to structure text-based data, identify patterns and develop quantitative insights.
• Statistical analysis: This technique extracts insights from raw data following the analytics life cycle — from data preparation to analysis to reporting.
• Machine learning algorithms: This tool uses AI-based processes to analyze data, identify trends and make future projections.
• Data visualization: This approach creates visual representations of data and data-driven insights to support idea generation and enable better communication of findings to business decision-makers.
• Data warehousing: This tool collects data from various sources, such as social media or spreadsheets, and creates a central data repository to enable data mining, machine learning and data analysis.
• Data mining: This process examines large data sets and identifies patterns or other useful information.

Business analytics and data analytics are similar and overlapping areas. Both areas focus on data cleansing and data visualization, along with tools and techniques related to data mining and machine learning. Business analytics primarily focuses on solving business problems and gaining insights on business performance, beyond traditional accounting and finance techniques. Data analytics is a broader discipline that can encompass multiple areas including business performance, healthcare, sciences and more.

The field and practice of business analytics can be supported by several hard and soft skills. Some of the most important skills used in business analytics include the following:^{(See disclaimer 2)}

• Communication skills, including active listening, written communication and verbal communication
• Technology literacy and tech applications
• Critical thinking skills to help assess information and make decisions
• Problem-solving skills, as well as problem identification skills
• Interpersonal skills, which can help professionals work well with a wide variety of stakeholders
• Research skills, which are essential for uncovering the problems a company is dealing with and the potential solutions that can solve them
• Strategic thinking to focus on the organization’s long-term goals
• Cost-benefit analysis, which is essential for predicting how operational changes may affect a company
• Documentation and presentation skills, which are used when presenting findings to business decision-makers

These skills are critical for thriving in business analytics roles in industries like finance, marketing and technology.