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.


Table of Contents

Why Do Data Analytics Matter?

What Is Data?

Exploring the Types of Data

Data Applications

What Is Data Analytics?

Data Analytics Applications

Who Uses Data Analytics?

What Are Metrics?

Why Do Metrics Matter?

What’s the Difference Between Metrics and Analytics?



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 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 uncover 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.


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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