Data Analytics vs Statistics: 5 Core Differences

With the big data industry reaching $103 billion by 2023, your business needs to understand the importance and differences between statistical and data analysis.

Years ago, both terms had a clear differentiation, but a blurred line between data analytics and statistical analysis has arisen because of the evolution in data analysis.

We’ll help you understand the gray area surrounding both the terms and give you a crystal clear idea of how is data analytics different from statistics.

So without further ado, let’s get started.

Data Analysis vs. Statistical Analysis

Over 95% of businesses believe they need to manage their unstructured and structured data to solve major business problems. In fact, the top companies have started using data analytics to gain the maximum benefits from their core data.

Data analysis and statistical analysis are two major components that can help businesses predict and solve major business issues.

But what’s the difference between the two?

If we boil it down, data analytics in data science focuses on making predictions about future events. On the contrary, statistics allow you to test your predictions and determine the best possible results.

It diverges deeper and figures out multiple differential points surrounding these terms.

Data Analysis Statistical Analysis
  • It is the collection, conversion, and analysis of data using tools and technologies like Python, R, and Hadoop.
  • There are no questions or null hypotheses involved in data analysis.
  • Prediction of new trends and patterns can be made using the data analyzed.
  • A professional data analyst converts the results into well-vetted reports for easy analysis.
  • Data analysis provides a wider perspective of data and focuses on multiple values.
  • The data results are extracted using statistics knowledge like Mean, standard deviation, and correlation.
  • Preferred answers are kept as null hypotheses, and different tests are performed on the data to prove the hypothesis right or wrong.
  • Statistical analysis obtains the answers to the predetermined questions based on that the future patterns and trends can be analyzed.
  • A professional statistician analyzes the data based on the set null hypothesis and presents the answer of the statistical analysis.
  • Statistical analysis has a shorter perspective over the data because it’s limited to the null hypothesis, and the actions revolve around that.

Now that we understand the stand-out differences between the two, let’s explore them.

What is Data Analysis?

Data analytics is extracting vital patterns and trends from the data that can help the business scale and improve. Statistics, core computer programming, and machine learning are the fundamental aspects of data analytics.

It delivers quantifiable predictions and performance results of the data and helps improve the business areas with finesse. Businesses use data analysis to predict, explain, and improve the overall business operations and performance.

Different data analysis areas include enterprise decision management, predictive analytics, retail analytics, sales department optimization and sizing, web analytics, credit risk analysis, and fraud analysis.

Once the data analyst looks at the raw data set, they don’t have preconceived notions or questions compared to a statistician. They discover patterns in the middle of data analysis and start forming questions for valuable information retrieval.

By the end of data analysis, they have all the information contained in the data that can help you predict future trends and business workflows.

Data analytics involves a basic data flow that can also be considered the data’s lifecycle: discovering and collecting data, data preparation for analysis, transforming the data, building models, publishing insights, and applying the data analytics results.

A data scientist has a data science toolbox like programming languages like R and Python or Apache Spark or Hadoop that they use to investigate the data and extract valuable insights.

A vast amount of data can be modeled, inspected, cleaned, and presented to non-technical scientists in a non-technical manner using data analysis.

What is Statistics?

Statistics is the analysis, collection, and interpretation of data. It is the fundamental tool for data scientists who gather and analyze huge amounts of unstructured and structured data to figure out their findings.

The statistician uses statistics to uncover the trends and patterns of the data. The statistician looks at the data set with a few concrete questions that need to be answered.

In the mid-way statistical analysis stage, it performs multiple tests where the preferred answers are kept as a null hypothesis.

By the end, the statistician has obtained the answers to a set of predetermined questions for the statistical analysis, and the business can use the answers to make well-informed decisions.

The statistical analysis is divided into five major stages: observation, formulating a hypothesis to explain observations, hypothesis testing, data analysis, and conclusion.

Multiple statistical knowledge is involved in the test process of the data and the extraction of vital information.

Different areas of data analysis are involved in statistical analysis like data visualization, high-dimensional analysis, and data optimization, among others.

Now that you have a better grip on data and statistical analysis, you need to decide which approach suits your business. Also, if you want to compare and understand data analytics with cyber security, check out our detailed guide on the difference between data analytics vs cyber security.

Frequently Asked Questions

Is data analyst and statistical analyst the same?

There is a thin borderline between the two skill sets. A data analyst has a wider lens of processing the data using advanced programming tools like R or Python and extracting the information. On the other hand, a statistical analyst has a shorter lens to approach data with a specific pre-defined question, statistical tools, and null hypothesis tests.

How can data analytics and statistics relate?

Data analytics helps you drive certain conclusions, and statistics can be used to test those conclusions. Data analytics techniques can mold the raw data sets and the drive patterns and trends that can be tested statistically to figure out an optimized solution for the business.

Should you hire both a data analyst and a statistician?

In the modern world, professionals in the data science field have a blend of both skills. They can use the advanced tool to explore the data available to them and use statistical knowledge and tools if they are uncertain about the solution but have a predetermined question. You can look for both the skills in a single professional to handle your dynamic business requirements and goals with finesse.

How is data different from statistics?

Statistics interprets raw data to show a relation with the variables, and data is used to create knowledge or information. Statistics involves using statistical tools to work on data to achieve the desired outcome. With data, you don’t know what you’ll get out of it by performing data analysis.

Why is data analytics important for business?

Data analytics helps businesses identify patterns and trends in unstructured or semi-structured data by processing the data using advanced tools. It can help businesses to create a better business environment to deliver a quality customer experience and strengthen the brand’s presence in the industry.

What Do You Prefer? Data or Statistical Analysis or Both

With the advancements in technology and data analysis, it has become the core business segment to scale a business positively.

But you can’t ignore the importance of modern statistics that focus on delivering better data science results.

It’s up to your business requirements and preference whether you want your data science professional to view the data with the statisticians’ or a data analyst lens.

Gaurang Bhatt

Written by

Gaurang Bhatt

Gaurang has 15+ years of experience solving complex business problems and enabling businesses with data-driven decisions using data analysis and predictive modeling tools like Tableau, Power BI, Looker, and Google Data Studio. His expertise lies in data visualization, reporting, and creating ETL pipelines. In addition, he is passionate about exploring different technologies like machine learning and AI. He shares his knowledge and learnings on the LabsMedia platform.