Data vs. Analytics vs. Insights: Understanding Their Differences and Importance

Business success used to be attributed largely to good instincts, but today’s business leaders rely on data to make strategic decisions. The proliferation of big data, along with the advancement of artificial intelligence (AI) technologies such as machine learning, enables business decision makers to leverage data analysis rather than rely on gut feelings.¹

Companies now have access to more data than at any other time in history, and savvy leaders can use this information to find new opportunities, reduce costs, optimize performance and increase customer satisfaction. However, before you can take advantage of data-driven decision-making, you need to understand how it works and how to use data effectively to make good decisions.¹

This article will examine the difference between data, analytics and insight and how all three are necessary for modern business leaders.

The word “data” has become a common business buzzword, but what does it actually mean? In the simplest terms, data is raw, unprocessed information. Although it’s commonly associated with artificial intelligence (AI) and analytical applications, data itself isn’t either of these things, and you don't need them to collect data. You can collect data by simply observing what’s in front of you.²

There are many different types of data, but quantitative data is usually the first type people think of. Quantitative data can be measured and expressed as numbers, such as age, date, number of customers, profits, cost per item, quantity sold and more.³

Qualitative data is language-based and can be more subjective. It expresses descriptive information, such as hair color or how customers feel about your brand. Both qualitative and quantitative data can be used as a basis for driving business strategy, but they are often handled differently.³

Without context, raw data alone is unlikely to help you make strategic business decisions. Raw data doesn’t clearly indicate what’s going on in your business operations, and without knowing the bigger picture, you can't build an effective strategy. Analytics is the process of examining and interpreting data. Data analytics highlights trends and provides necessary context to raw data, enabling business leaders to utilize data to fuel their business decisions.⁴

There are several different types of data analytics, and the type you need to perform depends on what you want to know. The enormous amounts of data that companies have access to today can form the basis of many types of analytics.

If you want to reflect on past trends relating to your business's performance, you can perform descriptive analytics. Descriptive analytics can answer questions like How many products did you sell last week?⁴

Predictive analytics tells you what’s likely to happen based on historical data and current trends. You can use predictive analytics to determine how much you’re likely to sell next month based on how much you sold last month or last year, as well as other relevant market conditions.⁴

Prescriptive analytics combines other types of analytics to recommend a course of action based on data. For example, a business can use prescriptive analytics to identify skill gaps in its workforce based on employee data and emerging technology.⁴

Finally, insights are the actionable conclusions you draw from analytics. They are the refined outcome of data analytics and can be used to inform your business strategies and drive decision-making. If data analysis tells you your sales are down and prescriptive analytics tells you expanding into a new market could increase your sales, the analytic insight you glean may be that you need to open a branch in a new location.⁵

Although data analytics and insights are closely related, and the terms are often used interchangeably, they are distinct concepts. So what's the difference between data, analytics and insights? Data is raw information while analytics is processed information, and insights are actionable conclusions. You’ll need all three to gain a competitive edge in today’s data-saturated business climate.⁵

Data-driven businesses rely on a continuously iterative process that begins with collecting raw data, and then leads to analysis and ends by generating insights. Using the insights to take action leads to new data, and then the process starts over again. This process, when performed strategically and efficiently, can lead to business success.

Each step in the process lays the foundation for the following step, so it's vital to ensure you're working with high-quality data and using the right analysis models. Without high-quality data—data that’s relevant, accurate and current—your analysis won’t be correct. Without good analysis models, your insights won’t be valuable.⁵

Business leaders have to make decisions in a complex landscape with difficult challenges. Tech advancements, climate change, sustainability concerns, a changing workforce and geopolitical conflicts are just a few of the numerous pressing issues they have to contend with. Data analysis and insights can help leaders navigate these challenges and make decisions that are good for business, employees, shareholders and society.⁶

Analytics can help uncover hidden trends and new opportunities that may not be apparent just by looking at raw numbers. Applying insights derived from analytics can drive innovation and solve business challenges, creating a long-term competitive advantage.⁶

Making effective, data-driven decisions is a complex undertaking that can be challenging. The data protection regulatory landscape is becoming increasingly complicated as cyber-attacks are more frequent with sometimes devastating results. Because of this, a lack of ability to collect relevant, comprehensive data can be a significant obstacle. You have to make sure you’re collecting data in a responsible, ethical and legal manner.⁷

Once you collect data, it needs to be high-quality to be useful. It has to be cleaned, which is the process of getting rid of duplicate, inaccurate, outdated or irrelevant data. Then it has to be analyzed without being misinterpreted or overanalyzed. This process requires people who are skilled in data analysis and understand how statistical models work. Once you have actionable insights, leaders must act on them and implement new initiatives to reap the rewards.⁷

Modern businesses need leaders who are skilled in data analysis and can use it to develop strategic initiatives. An Online Master of Science in Business Analytics from William & Mary can prepare you for this role. You’ll learn from industry experts in a highly-ranked program without having to relocate. You can earn an advanced degree and get ready for a new career, or advance your current career in just 15 months. Contact one of our admissions outreach advisors today to apply.