How Data Insights Improve Business Decisions

Written by Coursera Staff • Updated on

Explore how data insights help business professionals make informed decisions to meet their business goals. Also, discover four types of data insights you can gain from data analytics and three potential careers in the field.

[Featured Image] A data analyst sits at her office desk analyzing spreadsheets on her two computer screens to gain data insights about her work.

Data continues to become more crucial in helping companies operate and make business decisions in virtually every industry. Statista projects that the big data analytics market will reach $650 billion globally by 2029 [1]. The data insights that companies gain from gathering facts and figures pertaining to their business operations can help them make more intelligent, more targeted, and more efficient business decisions to save time, money, and resources. 

Discover the difference between data, data analytics, and data insights and how the three ideas work together to help you make more informed decisions. With this article, you can also learn about the four main types of data insights you can access, as well as three careers that use data insights to inform their work. 

What are data insights?

Data insights are pieces of information you gain when analyzing data, which is the process of looking for trends in data. Data insights are important for making informed business decisions and rely on the strength of data and your analytical capability. 

For example, let’s say that you run a lemonade stand. You can gather data, such as the number of lemonades you sell daily, the amount of supplies you use, and how much foot traffic your area has. Those numbers become meaningful when you analyze them by looking for trends. When you have analyzed enough data to be able to conclude from what you’re seeing, you have data insights. 

Your area might be busy on Friday afternoons but slow on Tuesday afternoons. You might use twice as much ice on hotter days than colder days. These glimpses into the data give you information that can be helpful to make business decisions. Using the lemonade data insights, you may decide to invest in a more insulated cooler to help prevent melting ice or hire someone to help you pour glasses faster on Fridays. With the information you gained from looking at the data, you would have a chance to make more targeted business decisions. 

Data insights across industries

Data insights are important for business but are also relevant to every industry that benefits from data. In health care, analytic professionals use data insights to improve patient outcomes, personalize treatment, and diagnose medical problems faster and more accurately. Data insights in education can help students succeed and give educators the information they need to be more effective teachers. The widespread need for data professionals in all industries helps drive demand for jobs working with data. 

Types of data insight derived from analytics

You can work with four main data types that allow you to gain insight: descriptive, predictive, diagnostic, and prescriptive. Let’s take a closer look at these categories to understand what each kind of data can teach you: 

  • Descriptive: In the example we used above, all of the data insights you gained from your lemonade stand data were descriptive analytics. In simple terms, descriptive analytics can tell you what happened, identifying patterns and trends. Descriptive analytics can help you create reports about how your business is currently performing.

  • Predictive: Using descriptive analytics, you can develop predictive analytics. With the data you gained about what happened, you can project what might happen in the future. In our example above, when you see the trend that your stand is busier on Friday afternoons, you use that data to project forward that next Friday is likely to be busy as well. 

  • Diagnostic: While descriptive analytics tell you what happened in the past and predictive analytics tell you what will happen in the future, diagnostic analytics tell you why. Diagnostic analytics rely on descriptive analytics to help determine the likelihood of predictive analytics. For example, diagnostic analytics might inform you that for the last four Fridays in a row, the neighbors of your lemonade stand hosted live music, which is likely why you saw such an increase in foot traffic. Instead of staffing for Fridays, you might consider staffing for when your neighbors host events, helping you make an even more targeted business decision. 

  • Prescriptive: The last type of analytics, prescriptive, helps you understand what you need to do next to achieve your business goals. Using your understanding of what’s happening, why, and what you expect to happen in the future, you can measure whether you’re on the right track to hit the metrics for success your company determined. If not, prescriptive analytics can help you learn what you need to do to get back on track. 

How data insights improve business decisions

Data insights can help you save money by using them to develop effective business strategies. Without data insights, you might spend time and resources developing solutions that don’t work, aren’t efficient, or miss their mark with customers. Instead of guessing, you can use data insights to make targeted decisions because you already know that your plan will be a success. 

In our lemonade stand example, when you decided to bring on another employee to coincide with your neighbor’s live events calendar, you needed that insider knowledge from data analytics to make a targeted decision. Without data, you might add employees at random times and use the process of elimination to sort out which days you require their help, wasting everyone’s time and the money required to pay your unneeded employees. With data insights, you can save time and labor costs by determining when and why you need extra help. 

Data insights can help you develop marketing strategies, determine new products to offer or markets to enter, listen to customer feedback, and adapt as needed. Although it’s possible for inaccurate data processing or collection methods to develop misleading data, data insights help decision-makers separate their intuition and personal tastes from business decisions and rely on a more scientific process to validate their ideas. 

Data insights challenges

While data insights benefit businesses in many ways, you may face challenges when using them. Some of these may include the following:

  • Overwhelming amounts of data: Many businesses capture more data than they may know what to do with, and it can prove challenging to identify critical information initially. Using the right tools and professionals to figure out what is and isn’t important can save businesses a lot of time and money.

  • Ensuring data quality: Businesses may find themselves with incomplete, duplicated, or other poor data sets. Gaining a holistic view of all your data can help make your data more accessible for better insights.

  • Ethical limitations: Businesses face important ethical concerns when managing large amounts of data and must comply with specific regulations to use data insights. Businesses must capture data ethically by ensuring users' consent.

  • Data storage issues: Data is typically stored in a data warehouse or cloud server. However, physical data centers can limit analyzers, and cloud storage often raises security concerns. 

Who uses data insights?

Many job titles in business can help you make smarter decisions using better data. Three potential careers that use data insights to inform their work include financial analysts, human resources associates, and product managers. 

Financial analyst

Average annual salary in the US: $79,833 [2]

Job outlook (projected growth from 2022 to 2032): 8 percent [3]

Education requirements: To become a financial analyst, you will likely need to earn a bachelor’s degree in business or a related field. 

As a financial analyst, you will use data insights to help companies and other organizations make business decisions. In this role, you may research potential investments, evaluate the financial trends for the company, assess the company’s value, or create presentations to communicate financial information to corporate stakeholders. Typically, you can specialize in buy-side—helping companies decide which investments to purchase—or sell-side—helping companies sell investments to consumers. 

Human resources associate

Average annual salary in the US: $55,926 [4]

Job outlook (projected growth from 2022 to 2032): 6 percent [5]

Education requirements: To become a human resources (HR) associate, you will likely need to earn a bachelor’s degree in human resources, business, or a related field. 

As an HR associate, you will be a human resources team member and help with aspects of staffing and recruiting, including determining how many employees to hire, conducting interviews, bringing on new hires, and onboarding new employees. In this role, you’ll work with data insights to determine how often employees miss work or how often to fill positions with new hires, for instance. 

Product Manager

Average annual salary in the US: $125,843 [6]

Job outlook (projected growth from 2022 to 2032): 6 percent [7]

Education requirements: To become a product manager, you will likely need to earn a bachelor’s degree in business or marketing. 

As a product manager, you'll oversee a product from the beginning stages of product development all the way through until customers are using and reacting to the product. Based on customer feedback, you will often help make decisions about product design, product pricing, marketing strategies, distribution, and recommendations for future changes or products. In this role, you’ll use data insights to make decisions at each step. 

Learn more with Coursera.

If you’re ready to take the next step in a data career, consider the From Data to Insights with Google Cloud Specialization offered by Google Cloud on Coursera. This four-course series can help you prepare for a job with a career certificate from Google Cloud. 

Article sources


Statistia. “Big Data Analytics Market Size Worldwide 2029,” Accessed February 29, 2024. 

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