Data v Analytics v Metrics: What Are the Differences?

data analytics
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*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.

Data analytics allow you to decipher patterns in data sets. Metrics help paint a clearer picture of what your data says. Data are values about specific qualitative or quantitative categories. Use each element wisely to make the most of essential campaigns and resources.

Data analytics are essential to the modern economy. A data scientist uses the information to reach conclusions, make decisions, and share crucial insights. However, many people confuse data, analytics, and metrics. They often use the terms interchangeably, but that’s incorrect.

There is a significant difference between data, analytics, and metrics. Data scientists understand the nuances and substantial variations used to formulate meaningful theories and strategies. They also know you can’t perform data analytics without all the puzzle pieces in their appropriate places.

How are data, metrics, and analytics different, and why does it matter? Do individuals and businesses need to know this information, or can they hand the keys to a data scientist? While data analytics are complex, efficient use of the data requires a team effort. Here is what you should know to join the squad.

Why Do Data Analytics Matter?

Data analytics matters more than some people realize. It is the science of analyzing raw data to draw conclusions and make decisions.

“Intuitive data analysis techniques can help individuals and businesses feel more confident and informed.”

The use of data analytics is also crucial enough to inspire competition.

Many data analytics processes are automated using complex algorithms aligned with specific goals. A sharp analysis allows entities to optimize performance, increase productivity, operate more efficiently, and maximize profitability. Meanwhile, there are multiple approaches to data analytics depending on the objectives.

A data scientist will use data, metrics, and analysis to determine these three things:

  • What happened – descriptive analytics
  • Why it happened – diagnostic analytics
  • What could happen next – predictive analytics

Keen analytics can also reveal potential strategies to help recoup loss or maintain a cutting edge. A data scientist can manipulate the information provided to develop visual tools, reports, and programs that bolster diverse goals.

What Is Data?

Understanding the definition of data is crucial to differentiation. Simply put, data are values conveying information about quantity and quality.

“Data analytics involves formulating evidence-based facts, statistics, and measurements based on a specified data collection.”

Most businesses run on data. They use it to examine and evaluate various aspects of the company, its market position, or its staff. Some use data analytics to develop marketing campaigns, introduce new products, and create or design a brand. Many corporations also use data analytics to experiment without commitment.

There are two primary types of data in analytics:

  • Quantitative
  • Qualitative

These data analytics tools express countable values and statistics or describe perceptions. The two categories are then further broken down into four subcategories, which include:

  • Nominal
  • Ordinal
  • Continuous
  • Discreet

A data scientist typically utilizes different data types based on the analysis goals and metrics. An organized and strategic approach is necessary because of the sheer amount of data produced daily. According to a Forbes report, the amount is a staggering 2.5 quintillion bytes every 24 hours.

Exploring the Types of Data

Quantitive and qualitative data are two peas in the same pod. However, they are on opposite sides. You can’t measure or count qualitative data using numbers. A data scientist will categorize this data based on metrics instead of assigning it a value.

Qualitative data examples can include photos, images, texts, audio clips, identifying markers, and symbols. It helps describe perceptions, so market researchers use qualitative data to design stories, advertisements, and public relations campaigns. Most data scientists further categorize qualitative data into these two parts:

  • Nominal – Used to give variables labels without quantitative value (e.g., eye color, marital status, etc.)
  • Ordinal – Used to assign values based on natural order and relative position (e.g., letter grades, economic status, etc.)

Quantitive data can include discreet or continuous data. Discreet data just means the information is separate or distinct from other data. It is relatively obscure because a data scientist can’t always connect it with cause or meaning. Meanwhile, continuous data are fractional values representing information you can divide into smaller portions. An example is a person’s height or the length of an object.

Data Applications

Experts say data never sleeps, and the statistics agree. Big data analytics are essential to the modern economy. Data science jobs are everywhere, and that’s a good thing because data applications are never-ending.

Data analytics allow individuals, brands, and corporations to use their unique information effectively. They can also identify problem areas, scout for new opportunities, and reframe public perceptions with excellent data collection and analysis. This often leads to more astute business moves, increased productivity, and boosted profits.

Collecting data and performing analytics could bring these advantages also:

  1. Operational Cost Reduction – Determine where your company is wasting money. Then, develop a strategy to cut costs and reduce waste.
  2. Faster Decision-Making – Stay ahead of the competitive curve by using accurate data and relative metrics to select choices and launch campaigns.
  3. Better Consumer Relations – Keep your customers happy and maintain industry relevance with data analytics geared toward your target audience.
  4. Improved Product Development – Data helps companies create more attractive inventories for their target audience, often leading to increased sales and brand recognition.
  5. Effortless Evolution – Data analytics allow companies to pivot from one goal to another without sacrificing consumer trust or traffic.

The importance of data cannot be overstated. Data analytics is equally essential because it benefits any entity working with the public or private sector. Get a deeper understanding of your target market, create a comprehensive consumer database, or streamline your marketing strategies with data collection and analysis.

What Is Data Analytics?

Data analytics is a systematic computational approach used by a data scientist. They gather and analyze information to interpret or understand data points, data sets, and relative metrics. Many data science jobs also involve communicating significant patterns with teams to encourage informed decision-making.

A data scientist will tell you that data analytics are critical and complex. They would be correct. You must understand the various types of data analysis to sort through the enormous amount of collected information. Depending on your requirements, data analytics can involve one or all of these four types:

  1. Descriptive Data
  2. Diagnostic Data
  3. Predictive Data
  4. Prescriptive Data

Descriptive data analytics reveals the what, while diagnostic data analytics uncovers the why. Predictive data shows you what might happen if you maintain the trajectory, and prescriptive data demonstrates what you can do to change it. Data scientists generally use all five types of data analytics to develop a comprehensive picture for communication and evaluation.

Data Analytics Applications

Data analytics applications are vast because businesses and individuals can use the information in multiple ways. Many choose to optimize performance or communicate different concepts to diverse teams. Some use analytics to make savvier decisions or examine consumer trends before implementing new strategies.

“A data scientist can use data analytics to help companies adopt effective programs for improved public relations and acquisitions.”

This can lead to enhanced goods and services, streamlined operations, reduced expenses, and more efficient use of resources.

Who Uses Data Analytics?

Large and small companies use data analytics to drive campaigns and establish or achieve goals. Several sectors can adopt data science tools to understand their markets better. Some of the top industries using data analytics include:

  • Travel
  • Hospitality
  • Fashion
  • Education
  • Retail
  • Healthcare
  • Banking
  • Government

Data analytics allow people to embrace quick turnarounds and remain competitive. The technique collects crucial data to help executives determine the best possible methodologies. Data analytics also helps reveal potential problems and describes meaningful ways to fix them.

DID YOU KNOW: A data scientist can compare data analytics results to various metrics to meet or exceed evolving consumer demands.

What Are Metrics?

Metrics are numerical measurements of a single data point or set. They are a specific aggregation you can apply to values during data analytics.

“A data scientist might call metrics the headlines for your data.”

The reason is that they help describe the quantitative value of your data instead of telling the qualitative value.

The benefits of metrics involve objectivity rather than subjectivity. Objective metrics can help provide a more comprehensive look at data points and sets. Here are some examples of metrics in everyday life:

  • Miles Per Gallon – We use metrics to determine how fuel-efficient our vehicles are. This metric measures how many miles we can travel on a single gallon of gas. The objectivity makes this example an undisputed market standard.
  • Net Profit Margin – Businesses use this metric to balance their budgets and demonstrate industry relevance. This metric determines how profitable a company is after paying expenses. It’s an essential measurement for shareholders, employees, and executives.

Data scientists use metrics to assign value to or categorize data. However, a data scientist isn’t the only one who can benefit from these numerical headlines. We see metrics in every nook and cranny of our world, but we wouldn’t if there wasn’t accurate data to help make it possible.

DID YOU KNOW: You use a form of metrics to make your household budgets and shopping lists.

Why Do Metrics Matter?

What are the benefits of metrics? The most critical advantage is context. Metrics provide valuable insights into how something performs. However, the significance is that this performance analysis is comparative. Here is an example:

Say your car gets 30 miles per gallon. Meanwhile, your friend’s vehicle gets 25 miles per gallon. You can quickly and efficiently analyze which automobile gets better gas mileage by comparing the two options. Their MPG is the data, but their comparative value is the metric.

You use metrics in the context of all available vehicles to determine fuel efficiency and value. Some consumers might change their minds about buying or selling their cars based on this standard metric. In the same way, some businesses could retool specific strategies after analytics and metrics evaluations. Here is another example:

Suppose you own a machine shop and secure a net profit of 18%. That value becomes the benchmark you can use to compare your earnings with competitors. All else being equal, objective metrics can help you develop more profitable business strategies based on trends and expectations.

Using metrics to reveal quantitative values can be tricky, however. You must always compare various values on an apples-to-apples basis. Otherwise, your metrics could get skewed and disclose irrelevant or inaccurate information. Since evaluating metrics can have a significant impact, a balanced analogy is essential.

What’s the Difference Between Metrics and Analytics?

Metrics and data analytics are not the same things. Both are unique and effective strategies for collecting pertinent data. However, they’re different in multiple ways. For example:

“Metrics represent the collected data, while data analytics describe the conclusions drawn from said data.”

Organizations that use data analytics can create more impactful strategies and achieve goals faster. They utilize a data-driven technique built with key performance indicators (KPIs) to maintain their seats at the proverbial table. Data, metrics, and analytics work side-by-side to create a complete picture of a business, project, program, or perspective.

Types of Data Metrics

Just like there are multiple types of data analytics, there are also various types of data metrics. Metrics are typically categorized based on the specific aspects of the business they measure. For example, some metrics you may come across in your business include:

#1 Financial Metrics

Financial metrics measure your business’s financial health and performance and are used to gather insights on how sustainable and financially successful your business is or could be. Financial metrics are crucial to goal-setting as they help you understand what goals are attainable, mainly through a financial lens. These metrics could include cash flow, revenue, gross margin, ROIs, profitability, sales and liquidity.

#2 Marketing Metrics

Marketing metrics are used to gauge how effective your marketing campaigns are. These metrics can provide insights into how current marketing efforts are performing and how well they’re tracking toward reaching your set key performance indicators (KPIs). Without marketing metrics, your team won’t have the information they need to thoroughly evaluate what strategies are and are not working. These metrics include website traffic, conversion rates, click-through rates, cost per acquisition and customer lifetime value.

#3 Social Media Metrics

Social media metrics include impressions, shares, engagement, reach and follower growth. These metrics measure the impact and effectiveness of your company’s social media activity. Monitoring social media metrics can help you analyze how well you’re accomplishing the goals you set for your social media and identify areas for improvement. Social media metrics can also help you understand how you connect with your audience and how they engage with your brand on socials.

#4 Customer Metrics

Customer metrics describe the information you track about your customers, like customer satisfaction, retention rates, brand loyalty, engagement and customer lifetime value. Customer metrics are precious as they offer insights into the customer experience. You can then use these insights to better understand your customers’ wants and needs and how best to meet them.

#5 Employee Metrics

Employee metrics are measured to assess your employee’s overall performance, productivity and satisfaction with their job and workplace environment. You can use these metrics — like turnover rates, absenteeism, quality of work, time management and overall performance — to track and monitor your workforce’s effectiveness and identify any improvement opportunities.

#6 Operational Metrics

Operational metrics measure your company’s performance across all aspects, including financial, marketing, production and interpersonal, to name a few. Operational metrics show how your operations have performed thus far and where you can improve to increase efficiency and effectiveness. Operational metrics include production output, inventory turnover, churn rate and customer satisfaction.

How Data Analytics and Metrics are Connected

There are several key differences between data analytics and metrics, and it’s important to note these differences to ensure you’re using both effectively. With those differences in mind, data analytics and metrics are also intertwined and complement each other in the realm of data management.

Metrics are quantitative measurements. Metrics are the raw data points used for data analytics. You can think of metrics as building blocks in this situation because, without metrics, data analytics wouldn’t have anything to analyze, and by itself, metrics don’t tell a complete story.

Data analytics gives the raw metrics context and identifies patterns, trends and connections among the data, providing useful insights for your business. For example, your social media metrics may tell you that your engagement on posts has dropped. Data analytics can explain why that is by analyzing related metrics to paint a clear picture of what’s happening.

Moreover, this relationship between data analytics and metrics is cyclical. Metrics fuel data analytics, and through the analytics process, you may uncover other metrics your business should be tracking or even new ways to conduct your analyses.

Data Analysis Tools and Techniques

Data analytics is a broad field, and just like there are several different types of data, there are also several other methods of analysis your business can use. The method you choose will depend a lot on the data you’re working with and what your business goals are, but in general, some of the most common and useful data analysis techniques include:

  • Regression Analysis
  • Factor Analysis
  • Cohort Analysis
  • Cluster Analysis
  • Time Series Analysis
  • Sentiment Analysis

#1 Regression Analysis

A regression analysis is a statistical method that examines the relationship between a dependent variable — which is the primary variable you’re looking to analyze or predict — and any number of independent variables, which are the factors that could affect your dependent variable.

You can think of the dependent variable as your desired goal or outcome — something like a new product launch, business growth, or a new marketing plan — and the independent variables are all the factors that could impact or influence that goal.

Regression analyses are useful for identifying trends and patterns and can be especially helpful when making predictions and forecasting future trends.

#2 Factor Analysis

A factor analysis takes a large number of variables and reduces them to a smaller number of variables or factors. This process makes your data much easier to work with and can also help you uncover hidden patterns among your larger data sets that you may have missed otherwise.

For example, let’s say you’re a retail business and want to understand customer purchase behavior better, so you survey your audience with questions like, “Do our products meet your expectations?” “How often do you purchase our products?” and “Do you think the price is reasonable?” These questions — and their answers — are essentially your variables.

When you get the survey responses back, you could have hundreds of thousands of answers that offer a wealth of information about your customers wants, needs and behaviors. To make analyzing this data easier, you can use a factor analysis to condense these variables into a single factor that connects all the variables, like customer purchase satisfaction.

From there, you’ll have a more manageable set of information that’s easier to analyze, understand and act on. Plus, once you condense the data with a factor analysis, you can easily take that data in for further analysis if necessary, as it’s already cleaned up and ready to go.

#3 Cohort Analysis

A cohort analysis categorizes and divides data into groups or cohorts based on shared characteristics. A cohort analysis can also classify users or customers, making it easier for you to isolate, analyze and identify patterns, trends and behaviors over their lifecycle with your business.

This technique is useful because it looks at how various groups, or different types of customers, behave over time, rather than just looking at a single, isolated moment in the customer journey. This means you can examine customer behaviors at all points throughout the customer journey — from their first experience interacting with your brand to the moment they make a purchase and so on.

Armed with this information, you’ll be better equipped to target customers at key points in the customer journey, because you’ll have data to rely on that gives you insight into how they may react, what they’ll do next and if they need some kind of push to make it to the next phase of the customer journey, getting them closer and closer to making a purchase.

A cohort analysis is also a good tool for customer retention and can help your business learn how to optimize its offerings and marketing strategies, and even start providing a more personalized, targeted experience for customers interacting with your brand.

#4 Cluster Analysis

A cluster analysis is somewhat similar to both factor analysis and cohort analysis. It’s a statistical, exploratory method for processing data that identifies structures in a dataset and then sorts the data into groups — or clusters — based on how closely related they are. This means that the data points in cluster A are similar to each other but are different from the data points in cluster B, and so on.

A cluster analysis is most commonly used for classification purposes. For example, in marketing, you may use a cluster analysis to divide subjects and identify categories like age, location and household income. Doing this allows you to create a more targeted marketing approach.

It’s important to note that while cluster analyses can identify structures and similarities among your organization’s data, it doesn’t provide insight into why those structures or similarities exist. A cluster analysis is more than just a starting point in understanding your data and sets you up for further analysis.

#5 Time Series Analysis

A time series analysis is used to identify trends and cycles in data over time. In a time series analysis, you measure the same variable, or data point, at different points in time. This could include data like weekly social media engagement or monthly sales.

In a time series analysis, you’re looking out for patterns like trends, fluctuations due to seasonal factors — like a peak in boot sales in the winter months — and cyclical patterns that can occur as a result of fluctuations in the economy or general industry.

The ability to make informed predictions about the future is extremely valuable to any business, as it allows you to plan ahead and maintain a competitive edge.

#6 Sentiment Analysis

A sentiment analysis is a curveball as it deals with qualitative, rather than quantitative data. This technique is a type of text analysis that is automated and uses natural language processing to interpret and classify emotions conveyed in textual data, like customer comments and reviews.

Your business can use sentiment analysis to better understand how your customers feel about your brand and their experiences with your organization. Using machine learning algorithms, a sentiment analysis can detect emotions like happiness, anger and frustration by analyzing customers’ words in their feedback and reviews.

This automated process allows you to understand your customers’ thoughts and feelings and identify areas of your business that need improvement. When talking about data analytics, most people immediately think of numbers and tend to overlook qualitative data, but there are plenty of valuable insights that can be pulled from these data sets and fuel business decisions and enhancements.


Data analytics require accurate data plus relevant metrics to work correctly. You cannot create or analyze metrics without data. However, you won’t have any trends to compare without metrics. Finding substantial relationships within data sets is impossible when you ignore the crucial elements.

Data, metrics, and analytics go hand-in-hand, albeit vastly different from one another. Ask a data scientist for a more in-depth explanation if you have additional questions.


Tiffany Perkins-MunnAbout the Author

Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with the agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.

Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on
Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology,
statistics, and real-world applications. As a published author, coursework developer, and
Dissertation Committee Chair Tiffany still finds time for family and hobbies. Her non-linear career path
has given her an exclusive skill set that is virtually impossible to reproduce in another individual.

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