Business Analytics: What It Is, Types, and Examples

You hear about the importance of data all the time. You likely even hear how data needs to be part of your business decisions. But if you’re not sure how to use it, where to start, or even what some of the terms mean when people talk about data, you’ve come to the right place.

In this article, we’ll discuss how business analytics can help you make better decisions and find new opportunities. We will consider the fundamentals of business analytics, including the types, techniques, and tools. We’ll also examine the types of job roles that can use data and share examples of how different types of businesses can apply analytics to improve their outcomes.

Lastly, this article will cover the benefits of business analytics and share practical tips on how you can successfully start implementing business analytics for your organization.

What is business analytics?

Business analytics is a two-step process. The first step is gathering the information and data produced by your software tools, business events, and people in your company. The second is applying statistical methods, technologies, and data tools to analyze that data and uncover insights.

The goal is to gain insights that can help your organization make better strategic and operational decisions. If you’re a small business focused on selling handcrafted items, you may think data is less important. However, using the data you get from historical sales trends, customer questions, and employee hours can help your business streamline operations, focus on better customer satisfaction, and forecast future sales so you can better understand, for example, when to plan a vacation or when to schedule more employees to meet demand.

The benefits of data scale up as your business gets bigger. Large enterprises benefit from having data support their strategies, ensuring they can maintain a competitive advantage in their market. And, even if you’re an individual contributor and not making the big, strategic business decisions that directly influence your company’s bottom line, you can still use data to help your job function better align with overall company objectives.

How business analytics works

Ready for some basic definitions and components of business analytics? Business analytics involves several key components that work together to turn raw data into meaningful insights:

1. Data collection: This refers to gathering relevant data from various sources, including internal systems and tech tools, customer databases, sales records, social media, IoT devices, and market research.
2. Data preparation: Data is cleaned to remove inaccuracies, duplicates, and irrelevant information. This step ensures the data’s quality and reliability.
3. Data integration: This means combining data from different sources to better understand trends. For example, you could integrate data from point of sales tools with ad spend to see the effectiveness of your advertising or marketing campaigns.
4. Data management: This is the process of organizing and storing data in a way that ensures accuracy, consistency, and security. Having accurate, consistent data helps your team members build trust in data-driven decisions.
5. Data analysis: This refers to the ways you interpret the data and uncover insights. Data analysis can often involve processes, such as applying statistical techniques and algorithms, or simply visualizing data in charts or graphs.
6. Data visualization: This term means resenting data findings in an understandable format using charts, graphs, and dashboards. Some charts work better for different data types than others. If you want to analyze growth over time, using a line chart is one of the best ways to present this data. If you want to analyze foot traffic across multiple locations, a map would likely be one of the best ways to present this information.
7. Data storytelling: While individual charts or graphs can help you understand trends, putting multiple visualizations together can provide a clear direction. That’s data storytelling. Here’s an example: You can tell a data story about key drivers for sales by combining graphs showing sales by hour with a chart showing historical sales trends for the month and sales by employee. With data storytelling, your company can start to see not only what is happening but also why it is happening.
8. Decision support: Once you have data collected, integrated, analyzed, and combined to tell a story, you can start using data to make decisions and develop strategy. For example, here are some simple business questions you can ask to help inform common decisions you make:
   • What are our best-selling products or services? And how do we keep profiting from them?
   • Who are our most profitable customers? And how can we find more of them?
   • How can I streamline my sales processes? What are the peak times for our sales or customer visits?
   • How can I optimize production costs? Which products have the highest return rates?
   • What can I do to reduce time spent on customer complaints? Are there common reasons for customer complaints that I could fix?
   • How much money should I spend to acquire a new customer? What is the lifetime value of our customers?
   • Which products are frequently purchased together? Should I suggest this product pairing for all of my customers?
   • Which employees or teams are the most productive? Can they train other employees on their processes to improve overall productivity?

Business analytics vs business intelligence vs data analytics

While business analytics, business intelligence (BI), and data analytics are related, there are still a few key differences we want to call out:

• Business analytics is primarily used in the context of analyzing historical data to gain insights and predict future trends. It often includes advanced analytical techniques like predictive and prescriptive analytics.
• BI is similar, but the term is more often used in the descriptive analytics context. BI is about providing insight into past and current business operations through reports and data visualization tools. Some people use “BI” interchangeably with business analytics and data analytics.
• Data analytics is often used to encompass a broader field that includes both business analytics and BI. Frequently, this term can refer to digging deep into data, processing and analyzing large data sets, and uncovering deeper insights.

One way to summarize the difference between the three and how they relate is this: BI can provide the “what happened,” data analytics digs into the “why it happened,” and business analytics guides businesses on “what could happen” and “what should be done” in the future.

Benefits of business analytics

Data can give you perspective based on historical trends and helps you make decisions that are rooted in customers’ and employees’ past behaviors. Such historical trends can help organizations understand how customers or employees are more likely to behave in the future. Then, you can make decisions based on data analysis, rather than intuition or observation alone.

By using data, organizations can reduce uncertainty, improve accuracy in predictions, and develop more effective business strategies. Data makes it possible to boost confidence in decision-making while driving competitive advantage and innovation. Some additional benefits of business analytics include:

• mitigating risks
• making informed strategic decisions
• enhancing operational efficiency
• reducing costs
• identifying growth opportunities
• revealing market trends

Types and techniques of business analytics

There isn’t just one way to approach business analytics. Determining what you want your data to do for your organization can influence which techniques you deploy for business analytics in your organization. Here are a few options:

Descriptive analytics: Analyzing historical data

Descriptive analytics focuses on understanding past performance by summarizing historical data. It answers questions like “What happened?” and “What were the trends?”

Predictive analytics: Forecasting future trends

Predictive analytics uses past performance, statistical models, and machine learning algorithms to predict future outcomes based on historical data. It answers questions like “What is likely to happen?”

Prescriptive analytics: Recommending optimal actions

Prescriptive analytics goes a step further by not only predicting future trends, but also recommending actions to achieve desired outcomes. It answers questions like “What should we do?”

Types of business analytics tools

While you can do business analysis manually, it’s difficult to scale a manual process. Investing in a business analytics tool will be much more effective. Here are a few types of business analytics tools your organization could consider:

• Data visualization tools for interpreting insights. These help businesses interpret complex data by presenting it in visual formats, such as charts and graphs. These tools make it easier to identify patterns, trends, and outliers in data.
• Statistical analysis software for advanced modeling. These tools provide advanced capabilities, enabling businesses to perform complex data modeling and hypothesis testing.
• Machine learning algorithms for predictive analytics. Machine learning algorithms enable predictive analytics by identifying patterns and making forecasts based on large datasets. These algorithms improve over time as they learn from new data.

Roles in business analytics

While the goal is to have everyone in your organization access and use data, business analytics thrives best when you have people or teams in your organization that own the data processes and can act as subject matter experts about the data in your business. Depending on the size of your organization, you may need to upskill a current employee or hire a new one to fill one of the following roles:

Data analyst

Data analysts typically gather data from various sources, clean and analyze it, and present findings to stakeholders. They play a crucial role in uncovering insights that can drive business decisions.

Business intelligence manager

This role could have different titles, but BI managers oversee the quality and availability of data within an organization. They ensure that data is accurate, consistent, and accessible for analysis. They are also often the champion and subject matter expert for your organization’s BI tool, helping train other users and deploy it throughout your organization.

Data scientist

Data scientists develop advanced analytical models and algorithms to predict future trends and outcomes. Their work often involves creating machine learning models and conducting deep statistical analysis to support your company’s strategic planning.

Tips to implementing business analytics

A lot of this information can help you understand why business analytics matters for your company. Ready to see how to apply it? Here are a few tips to implement business analytics at your company:

1. Focus on data collection and preparation for using your data in analysis. Find out how you want to use your data, and then make sure the data has been cleaned, transformed, and loaded in a way that supports your data goals. This includes defining data sources, setting up data pipelines, and ensuring data is clean and ready for analysis. Doing the prep work before will ensure a more streamlined adoption of data across your organization.
2. Encourage teams, departments, and individual contributors to use data where and when they can. By building a data-driving culture within your organization, you’ll be able to realize the benefits of data at every level. This involves training employees on the importance of analytics, encouraging the use of data in everyday decisions, and fostering an environment where data insights are trusted and acted upon.
3. Then you need to persevere through challenges. You can run into problems like siloed data, learning curves with new processes and tools, and possibly even resistance to change. To overcome these challenges, businesses should invest in data integration tools, provide training for staff, and communicate the benefits of analytics to everyone in your organization.

Examples and use cases of business analytics

Data and business analytics work for every company in every industry. We are firm believers in seeing how other companies succeed with analytics and then thinking about ways to apply those successes to your own business. Here are some examples across industries of how businesses use analytics.

Retail

Freddy’s was growing fast and needed data to help optimize its business and growth. By combining data from franchises across the company, Freddy’s uses its business analytics to design its menu mix, understand guest preferences, and even help decide where to open new stores. By analyzing sales and customer data, Freddy’s can offer more impactful promotions or offers to help grow sales and customer loyalty.

Healthcare

GE Healthcare provides tools and technologies to medical providers. It uses its business analytics to save time and streamline operations. One aspect that has been particularly impactful for GE Healthcare is alerts. Team members receive alerts when specific metrics are reached, or when the data has had a material change. This allows the team to be agile in how they respond to business challenges and opportunities.

Financial services

As a global bank, BBVA has branches and offices in more than 25 countries. One problem it faced was that customer information was siloed, and it was difficult to get an accurate picture of their customers as a whole or make strategic decisions about customers in a specific region. By combining data in one location, BBVA can now understand how customers in each country use its banking services and optimize products to meet the needs of customers in different geographies.

Marketing

Business analytics can be impactful for marketers looking to optimize their ad spend or improve ROI on campaigns. Visit Orlando uses Domo to get real-time visibility into how viewers interact with marketing campaigns. Then, the marketing team uses that information to reduce cost per click and help grow Orlando into a top tourist destination.

Manufacturing

Taylormade has created a data-driven culture across their organization, encouraging employees to use hard data to back up their decisions. To make business analytics widely accessible, it had to provide user-friendly tools and training to help users see the value in data for their everyday decisions. Additionally, by combining data from multiple sources, Taylormade can get a quick view of the company’s overall health.

Sports

The NBA uses data to help them estimate and predict viewership for games. These insights, in turn, help the association make decisions about matchups, scheduling, TV contracts, and more. Questions the NBA can now answer with their data include: What would happen to the viewership if a team was on a winning streak? What would happen if the team’s superstar was injured? Are some fans so loyal that they will watch their favorite team play knowing they were not projected to win? In this way, the NBA uses business analytics to help grow the organization while improving the customer experience.

By implementing business analytics, organizations across sectors can unlock the full potential of their data, leading to smarter decisions, enhanced performance, and sustained competitive advantage. Learn how you can use Domo to get started today.