Explain Data Analysis: Methods, Process, and Types
Today, we
reside in an era that can be addressed as a ‘data-rich age’. The call of the
modern era is to understand the process of analyzing and extracting digital
insights. You might have a question ‘how to extract data from the huge volume
of data created daily?’ It’s possible by learning the methods of data analysis in research.
In this
article, you will explore the basics of analyzing data in the real world. You
will also come across a few data
analysis tools that help execute data analysis methods and techniques.
Now,
before we dive deep into the details of the data analysis methods, we shall
foremost have a clear understanding of what data analysis is.
What is Data Analysis?
The
methodology of collecting, copying, and analyzing data is known as data analysis.
Data analysis is done to draw out insights that support decision-making. There
are many unique methods to perform analysis. The choice of the particular data
analysis technique depends on the industry and the aim of the investigation.
Data
analysis can be also understood as modeling & processing raw data for
extracting actionable and applicable information that helps businesses make
informed decisions. Data
analysis is helpful to cut off the risks associated with decision-making. How?
By distributing useful insights and statistics in the form of charts, images,
tables, and graphs. Data analysis has a huge contribution to processing big
data into useful information.
What are the types of data analysis methods?
Talking
the types of data analysis methods,
they are broadly classified into 5 types as follows:
1. Descriptive Analysis
Descriptive
analysis is the 1st and most important type for conducting statistical
analysis. Here, one gets informed about the distribution of data. Descriptive
analysis helps observe outliers & pinpoint variable associations. This type
of analysis is carried out with a huge set of data where data is broken down
into two categories; i.e., descriptive analysis for each variable and
combinations of variables.
2. Regression Analysis
Regression
analysis is considered a presiding data analysis technique used in the industry
on a huge scale. This is a technique that involves identifying the relationship
between two or more variables of interest. For this, one has to plot the data
on a chart.
3. Dispersion Analysis
Dispersion
is another type of data analysis where distribution is stretched or squeezed.
Mathematically, dispersion analysis is where one needs to find the difference
of values among themselves. It can also be defined as identifying the
difference between the average value. The dispersion is less provided the
difference between the value and average is very low.
4. Factor Analysis
Factor
analysis is yet another popular kind of data analysis technique. This technique
is used for detecting underlying structures in a set of variables. By locating
the independent variables in the data set one can describe the patterns and
models of relationships.
5. Time Series
Time
series analysis deals mainly with time-series data or trend analysis. You must
have a question; what is time-series data? Time series data is data present
within a series of distinct time intervals or periods.
What does the Data Analysis Process look like?
Now that
you know the meaning of data analysis, let’s move ahead with the data analysis
process. The data analysis process comprises collecting all necessary
information, processing & exploring the data. Once the processing is done,
the final step is to detect patterns.
Below we
have highlighted the 7 steps of data
analysis simply.
1. Data Requirement Gathering
The 1st step is to question yourself on why you need to do this analysis. Also, include the questions how & which data to include in the data analysis procedure.
2. Data Identification
After
gathering answers to certain questions comes the next step is data
identification. A business must identify the type of data for data analysis.
Why? Because the business owner must know the customer journey framework.
Here, you
have to consider your customer’s touchpoints. Begin guessing the areas where
you can collect the most meaningful data. You can consider the types of tools
that you will use for data collection.
3. Data Collection
After
identifying data, move on to the 3rd step data collection. You have to work
hard to gather data from your sources. A few faithful sources include case
studies, investigations, interviews, opinion polls, direct observation, and
focus groups. Remember to organize the collected data for data analysis.
4. Data Cleaning
After
completing the 3rd step you will be left with a huge & massive amount of
collected data. Fact is told, all the data you collect won’t be useful, so you
ought to clean it up. Data cleaning is a process where you have to eliminate
general errors like white spaces & duplicate records.
5. Data Analysis
Now, you
have finally landed on the most important step data analysis. Here, you will
have to use analysis software and other tools for interpreting and
understanding the data. Data analysis
tools that are helpful include Excel, Python, R, Chartio, Metabase, and
Microsoft Power BI.
6. Data Interpretation
You have
successfully executed data analysis & now you have your results. In this
6ts step, you have to decode the results and present the breakthrough of
action.
7. Data Visualization
This is
the last and final step in the entire data analysis process. Why? Because
visualization assists in driving valuable insights. Data visualization helps
establish a comparison between several datasets.
Data
visualization means presenting your information graphically. The data
visualization must be executed such that the audience can read and understand
it. Use bulletins, charts, graphs, maps, & other methods for presenting the
visualized data.
How to Become a Data Analyst?
By now you
must be well-aware of deep & clear data analysis. In case you are
interested in building a career in data analytics, you must analyze what it
takes to become a data analyst. You can do this easily by opting for online
data analytics courses. You can obtain certifications and begin a career here.
