Data Analysis Vs Data Analytics


data-analysis-vs-data-analytics



1. Objective

Data analysis is a subset of data analytics, a broader field that includes data management and methods.

Data analysis is a retrospective process that focuses on understanding past events. 


While data analytics is forward-looking and aims to predict future occurrences.



2. History and Evolution

The Origins of Data Analysis

Data analysis, the elder sibling, has roots tracing back to ancient civilizations.

It was a method employed to study patterns, primarily in trade and astronomy.


The Rise of Data Analytics

Data analytics emerged with the digital revolution, bringing various tools and techniques that leveraged computational power for insights.



3. Definition

Data analysis refers to inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.

Data analytics involves the exploration, interpretation, and communication of meaningful patterns in data to derive insights and support decision-making.



4. Activities

Data analysis activities include:

  • Inspecting, cleaning, and interpreting data
  • Discovering patterns
  • Deriving meaningful conclusions
  • Understanding past data patterns
  • Using tools like Excel

    • Data analytics activities include:

  • Identifying data
  • Acquiring and filtering data
  • Extracting data
  • Validating and cleaning data
  • Aggregating and representing data
  • Analyzing data
  • Visualizing data
  • Creating data stories
  • Using data, machine learning, statistical analysis, and computer-based models


    • 5. Focus

    Data analysis primarily focuses on examining data sets to identify trends, patterns, and insights that can be used to understand past events or predict future outcomes.


    While data analysis deals with the examination of data itself, data analytics focuses more on the broader process of extracting value from data.

    It includes data analysis but also includes activities like data visualization, predictive modeling, and data-driven decision-making.



    6. Techniques

    Data analysis involves various techniques such as statistical analysis, descriptive analysis, diagnostic analysis, exploratory data analysis (EDA), etc.


    Data analytics involves a combination of statistical analysis, machine learning, data mining, and other analytical techniques to uncover insights from data.



    7. Applications

    Data analysis is widely used in fields like business, science, healthcare, finance, and many other tasks such as market research, performance analysis, risk assessment, and scientific research.


    Data analytics finds applications in various domains including business intelligence, marketing analytics, customer relationship management, fraud detection, and optimization of business processes.




    Resources