This presentation briefly explains the difference between Data Analytics vs. Data Analysis

1. Data Analytics
vs.
Data Analysis
Dr. C.V. Suresh Babu

2.  Data analytics is the broad field of using
data and tools to make business
decisions.
 Data analysis, a subset of data
analytics, refers to specific actions.

3. Processes in data analytics
The data analytics practice encompasses many separate processes, which can
comprise a data pipeline:
 Collecting and ingesting the data
 Categorizing the data into structured/unstructured forms, which might also
define next actions
 Managing the data, usually in databases, data lakes, and/or data warehouses
 Storing the data in hot, warm, or cold storage
 Performing ETL (extract, transform, load)
 Analyzing the data to extract patterns, trends, and insights
 Sharing the data to business users or consumers, often in a dashboard or via
specific storage

4. Type of data analysis
 Text analysis. This is also referred to as Data Mining. This method discovers a
pattern in large form data sets using databases or other data mining tools.
 Statistical analysis. This analysis answers “What happened?” by utilizing past data
in dashboard form. Statistic analysis involves the collection, analysis,
interpretation, presentation, and modeling of data.
 Diagnostic analysis. This analysis answers “Why did it happen?” by seeking the
cause from the insights discovered during statistical analysis. This type of analysis
is beneficial for identifying behavior patterns of data.
 Predictive analysis. This analysis suggests what is likely to happen by utilizing
previous data. The predictive analysis makes predictions about future outcomes
based on the data.
 Prescriptive analysis. This type of analysis combines the insights from text,
statistical, diagnostic, and predictive analysis to determine the action(s) to take in
order to solve a current problem or influence a decision.

5. Data analysis Data analytics
Data analysis is a process involving the collection,
manipulation, and examination of data for getting a
deep insight.
Data analytics is taking the analyzed data and working
on it in a meaningful and useful way to make well-
versed business decisions.
Data analysis helps design a strong business plan for
businesses, using its historical data that tell about what
worked, what did not, and what was expected from a
product or service.
Data analytics helps businesses in utilizing the
potential of the past data and in turn identifying new
opportunities that would help them plan future
strategies. It helps in business growth by reducing risks,
costs, and making the right decisions.
In data analysis, experts explore past data, break down
the macro elements into the micros with the help of
statistical analysis, and draft a conclusion with deeper
and significant insights.
Data analytics utilizes different variables and creates
predictive and productive models to challenge in a
competitive marketplace.
Tools used for data analysis are Open Refine, Rapid
Miner, KNIME, Google Fusion Tables, Node XL, Wolfram
Alpha, Tableau Public, etc.
Tools used in Data analytics are Python, Tableau Public,
SAS, Apache Spark, Excel, etc.
Data analytics is more extensive in its scope and
encompasses data analysis as a sub-component.
The life cycle of data analytics also comprises data
analysis as one of the significant steps.
Data analysis is actually studying past data to
understand ‘what happened?’
Whereas data analytics predicts ‘what will happen next
or what is going to be next?’

6.  Through data analytics and data analysis,
both are essential to understand the data
as the first one is useful in estimating
future demands and the second one is
necessary for gaining insight by analyzing
the details of the past data.