Data vs Information
Data usually refers to raw data, or unprocessed data. It is the basic form
of data, data that hasn’t been analysed or processed in any manner.
For
example: Jeetendra,Jaiswal,36,Gamdevi,Road,Bhan,dup,West,78,Mum
Information is data that has been interpreted so that it has
meaning for the user. Once the data is analysed, it is considered as
information.
For
example: Jeetendra Jaiswal
365, Gamdevi Road
Bhandup (West),
Mumbai-400078
What is Data Analytics?
Data
Analytics is the science of using various techniques and processes for
examining raw data for extracting information from them. Data is extracted and
processed using various techniques based on organizational requirements with
the purpose of finding patterns and drawing conclusions about that information.
Organizations
collect and analyse both real time and historic data related to their
customers.
Using
data analytics, organizations can identify and optimize their operations,
identify the needs of their customers and provide them the best possible
service, proactively identify the future trend and respond to it leading to a
gain in this competitive market over others.
All
these can help the organizations to optimize costs and increase their revenues.
Types of data analytics applications
Data
Analytics can be broadly classified under 2 categories
1)
Exploratory Data Analysis (EDA)
An approach to analysing data sets to summarize their
main characteristics, often with visual methods. A statistical model can be
used or not, but primarily EDA is for seeing what the data can tell us beyond
the formal modelling or hypothesis testing task.
2)
Confirmatory Data Analysis (CDA)
An approach where you evaluate your evidence using
traditional statistical tools such as significance, inference, and confidence.
Data
analytics can also be classified as
I.
Quantitative Data Analysis (QtDA)
An approach where numerical data is analysed with
quantifiable variables that can be compared or measured statistically.
II.
Qualitative Data Analysis (QlDA)
An approach where we understand the content of
non-numerical data like text, images, audio and video, including common
phrases, themes and points of view.
More
advanced types of data analytics
A.
Data mining
Data Mining is the analysis of large quantities of
data to extract previously unknown, interesting patterns of data, unusual data
and the dependencies.
B.
Business Intelligence
Business Intelligence techniques and tools are for
acquisition and transformation of large amounts of unstructured business data
to help identify, develop and create new strategic business opportunities.
C.
Statistical Analysis
Statistics is the study of collection, analysis,
interpretation, presentation, and organization of data. The 2 main types of
Statistical Analysis are:
1) Descriptive
statistics - data from the entire
population or a sample is summarized with numerical descriptors such as mean,
frequency, percentage, etc.
2) Inferential
statistics − It uses patterns in the
sample data to draw inferences about the represented population or accounting
for randomness. These inferences can be −
·
answering yes/no
questions about the data (hypothesis testing)
·
estimating
numerical characteristics of the data (estimation)
·
describing
associations within the data (correlation)
·
modelling
relationships within the data (E.g. regression analysis)
D.
Predictive Analytics
Predictive Analytics use statistical models to analyse
current and historical data for forecasting (predictions) about future or
otherwise unknown events.
E.
Text Analytics
Text Analytics, also referred to as Text mining
provides a means of analysing documents, emails and other text-based content.
Use of Data Analytics
Data
analytics initiatives support a wide variety of business uses. Below are few
examples
1)
Banks and credit
card companies analyse withdrawal and spending patterns to prevent fraud and
identity theft.
2)
E-commerce
companies and marketing services providers do clickstream analysis to identify
website visitors who are more likely to buy a product or service based on
navigation and page-viewing patterns.
3)
Mobile network
operators examine customer data to forecast churn so they can take steps to
prevent defections to business rivals; to boost customer relationship
management efforts, they and other companies also engage in CRM analytics to
segment customers for marketing campaigns and equip call centre workers with
up-to-date information about callers.
4)
Healthcare
organizations mine patient data to evaluate the effectiveness of treatments for
cancer and other diseases.
Fundamentals to Data Analytics
1)
Identifying the Data
It is important for a data analytics project to
identify where the valuable information resides and map it based on the 4V
framework, defined by Volume, Variety, Velocity and Veracity.
2)
Data Quality
Data quality is the core aspect in data analytics that
decides its intended use in business operations and decision making. The
correctness and consistency of the data demonstrates its quality and fitment
for use.
3)
Business Objectives
Clarity in defining your goals and objectives are
essential for achieving success through analytics. Analysts need to have in
mind the big-picture while building the conceptual framework and process useful
in data analytics.
4)
Data Availability & Access
Data availability and access is the fundamental
requirement to data analytics. Authorized personnel should be able to access
the internal organizational data, and, the information external to the
organization must be collected from reliable resources.
5)
Insight
The success of a data analytics project depends on the
quantifiable insight it generates. The derived system should be able to provide
timely and accurate answer to the business questions. This presents a valuable
actionable insight that marks a way to tread and adds up to the value chain.
6)
Data Visualization
For a meaningful insight, it is a must to present the
information in an appealing and insightful manner to the intended audiences.
The business story and the user story should be represented with advanced visualization
techniques for better clarity, with scope for interactive exploration.
7)
Data Practices
The right data analysis framework, a standard
architecture for data interoperability, and strict compliance to data security
& privacy norms creates a trusted environment for data analysis. These
enablers help organizations to undertake projects that assure high data
security for a project’s success and stakeholders’ buy-in.
Challenges in Data Analytics
1)
Handling Enormous Data in Less Time
Handling the data of any business or industry is
itself a significant challenge, but when it comes to handling enormous data,
the task gets much more difficult. Critical business decisions should be taken
effectively, but we need to have strong IT infrastructure which can read the
data faster and delivering real-time insights. To overcome this challenge, you
can use Apache Hadoop’s MapReduce that helps in splitting the data of the
application in small fragments. This process makes the data measurable.
2)
Visual
Representation of Data
Another important task is the visual representation of
data. You need to represent the data in an easy format that makes it readable
and understandable to the audience. Handling an unstructured data and then
representing in a visually attractive manner could be a difficult task. To
recover this issue, the data analyst can utilize different types of graphs or
tables to represent the data.
3)
Application
Should Be Scalable
The major factor to consider is the scalability factor
of the of the applications. Several organizations are facing the same issue
where the volume of data has been increasing each passing day. Due to the
multiple layers between the database and front-end, the data traversal takes
time. To overcome this issue, the organizations should take care of the
application’s architecture and technology to reduce performance issues and
enhance scalability.
Guidelines for effective use of Data
Analytics
1)
Define the Questions
Your questions will define your work process. So,
define your questions and ask measurable and clear questions. Define your
problem clearly and design the question in such a way that it either qualify or
disqualify potential solutions.
2)
Set Appropriate Measurement Priorities
This point covers two different scenarios, i.e. decide
what to measure and how to measure. You need to think about these situations.
Deciding on how to measure the data is important before the data collection
phase as it also has its own set of questions.
3)
Collect
Data
After defining the questions and setting up the
measurement priorities, now you need to collect the data. Try to keep your
collected data in an organized way.
4)
Analyse
and Make Data Useful
Now is the time to analyse the data. You can
manipulate the data in multiple ways by plotting and searching correlations or
by building a pivot table. The pivot table will help in sorting and filtering
data and calculate the maximum, minimum, mean and standard deviation of your
data.
5)
Interpret Results
Data Analytics is incomplete without compelling
visualization. This is the time to interpret your data. Interpreting the data
will answer all the data-related questions.
Conclusion
Data
analytics is helping businesses to consider hundreds of parameters to predict
with reasonable accuracy what will happen. Both structured and unstructured data
are growing exponentially. With proper use of technology, this huge data can be
used to make more accurate decisions which can help the organizations improve
its operations, reduce costs, improve sales, provide better service to
customers and improve its efficiency.
