What is Data Analytics?

Data is the new precious Oil in today’s business world. Data is a critical input for
business decisions and it can impact the growth of organizations. As said by Charlie
Berger of Oracle Corporation, “Without proper analysis, data is just text and numbers
and not useful to derive actionable information. It is something that you can exploit
today and something that your competitor may not have yet discovered.”

With the humongous growth in volumes of Data that is generated, it is important for
organizations to understand and analyze this data to derive trends and actionable
insights to address business problems. Data has a variety in today’s user context. It is
not only generated in the traditional data types that are structured but also unstructured
data types such as videos, social media content, audios, and images. This mixed Data
type gets generated across all possible sources at high velocity. For some
organizations, this might be tens of terabytes of data. For others, it may be hundreds of
petabytes.

What is Data Analytics?

Data Analytics involves collecting and analyzing raw data and applying qualitative,
quantitative techniques and processes to convert it into useful information for
decision-making and enhancing productivity and business gain. Raw data is categorized
and analyzed to identify user behavioral patterns. The techniques used for analyzing
data vary according to organizational / business requirements.
Six Important steps to perform Data Analysis
Step 1: Set your goals
You need to identify your business goals or objectives and define questions around it.
This is an important step as the data that you collect depends on the questions.
Collecting irrelevant data is a waste of time. Questions should be measurable, clear and
concise. For example, If you have a business and you are not satisfied with the Sales,
your questions could be “Is my commodity over-priced?”, “Are there similar competitive
products in the market?”, “What is the difference in competitive products?” The data you
collect could be your selling price for the commodities, production cost, prices of similar
goods in the market and so on.

Step 2: Setting up measurement priorities

Now that you know your goals you need to a) Decide what to measure b) How to
measure the data

What to measure
It is important to identify which data points you need to measure. Once the data
surrounding the primary question is identified, you need to work on getting answers to
the secondary questions. In the above question “Is my commodity over-priced”, your
data points to production cost. Secondary questions could be “What is the material
cost?”, “What is the labor cost?”, “What is the manufacturing overhead cost?” etc.

Once the data is collected for the primary question and the secondary questions, the
data can be converted into information that will assist the company in decision making.

How to measure the data

You need to decide how you are going to measure the data.

For example, while collecting data for the material cost would you be considering the
expenses of insurance and freight as a part of the material cost? So when you measure
the data, you need to be clear on what measures you would be counting in and
measures you plan to exclude.
Step 3: Data collection
Now that you know the measuring parameters and criteria, your next step is data
gathering. For data collection, you need to consider some important points.

● You may need to decide the time frame for the data collection. Would you need

data for the last 3 years or 5 years or would you like to collect data for a

particular season as buying habits could change seasonally?

● Identify who will collect data

● Decide where the collected data will be stored

● Check if any data is already available so you need not spend time collecting it

again.

● At times data collection may need a survey or interviewing people,

questionnaires, etc.

Step 4: Data cleaning

Once you have collected the data, the data needs to be cleaned up. Removing
superfluous data, replacing missing values, removing duplicates and so on. This data is
gathered due to multiple reasons such as lack of company-wide standards, having
many databases, and user errors. This is referred to as “dirty” data, and it can represent
a formidable obstacle to companies hoping to use that data. According to The Data
Warehouse Institute (TDWI), dirty data ends up costing companies around $600 billion
every year. To fully address this problem, businesses need to understand what causes
dirty data and how best to fix it. Data cleansing is not a solution for good quality data. It
is important to ensure that valid data is stored rather than wasting time in cleaning it up.

Step 5: Data Analysis

Once you have good quality data it is time for analyzing the data. It is important to
understand each type of data. Depending on your needs and the type of data you
collect, the right data analysis methods should be used. Type of analysis depends on
whether the data is quantitative or qualitative. Quantitative data deals with quantities
and hard numbers. This data includes sales numbers, marketing data such as
click-through rates, payroll data, revenues, and other data that can be counted and
measured objectively. Qualitative data is more interpretive and subjective. This includes
information taken from customer surveys, interviews with employees, and generally
refers to qualities. Hence, the analysis methods used are less structured than
quantitative techniques.

Measuring quantitative data

● Regression Analysis: Regression is a data mining technique used to predict a

range of numeric values given a particular dataset. Regressions measure the

relationship between a dependent variable (what you want to measure) and an

independent variable (the data you use to predict the dependent variable).

Regression is used across multiple industries for business and marketing

planning, financial forecasting, environmental modeling and analysis of trends.

● Hypothesis testing: Statistical analysts test a hypothesis by measuring and

examining a random sample of the population being analyzed. Hypothesis testing

is used to infer the result of a hypothesis performed on sample data from a larger

population. It also helps you understand how random variables could affect your

plans and projects.

● Monte Carlo Simulation: Monte Carlo simulation is applied to predict the

probability of various outcomes in a process. It is difficult to predict the outcomes

due to the presence of random variables. Monte Carlo simulation can be used to

address a range of business problems in almost every industry like finance,

logistics, and supply chain, engineering and science. To test a hypothesis or

scenario, random numbers and data are used to display a variety of possible

outcomes in various situations.

Measuring qualitative data
Unlike quantitative data, qualitative information requires more subjective approaches.
Yet, you can still extract useful data by employing different data analysis techniques
depending on your demands. Here are two techniques that focus on qualitative data:

Content Analysis

This method is a research technique to make valid inferences by interpreting textual

material. Content analysis can work well when dealing with data such as user feedback,
interview data, open-ended surveys, and more. This can help identify the most
important areas to focus on for improvement.

Narrative Analysis

This kind of analysis focuses on the way stories and ideas are communicated
throughout a company and can help you better understand the organizational culture.
This might include interpreting how employees feel about their jobs, how customers
perceive an organization, and how operational processes are viewed. It can be useful
when contemplating changes to corporate culture or planning new marketing strategies.

Step 6: Interpret Results

As you interpret the results of your data, ask yourself these key questions:

● Does the data answer your original primary question? How?

● Does the data help you defend against any doubts? How?

● Are there any limitations to your conclusions, any areas you have not

considered?

Data Visualization refers to information that is presented in visual forms, such as

graphs, charts, and tables or pictures. The main reason for this is to communicate the
information in an easily understandable manner. Even very complicated data can be
simplified and understood by most people when represented visually. It also becomes
easier to compare the data when it’s in this format. For example, if you need to see how
a business or product is performing compared to competitor’s product, all the
information such as price, specification, how many were sold in the last year can be put
into graph or picture form so that the data can be easily assessed and decisions made.
It is a quick way to view and help identify the source of the problem.

Types of Data Analytics

There are 4 types of Data analytics.

Descriptive Analytics
Descriptive Analytics answers the question “what happened?” For instance, a
healthcare provider will learn what made patients get hospitalized in the last month; A
retailer – what was the average weekly sales volume; A Manufacturer – what triggered
product returns for a past month and so on. Descriptive Analytics juggles raw data from
multiple data sources to give valuable insights into the past. However, these findings
simply signal that something is wrong or right, without explaining why. For this reason,
highly data-driven companies are not contented with descriptive analytics only and
prefer combining it with other types of data analytics.

Diagnostic Analytics
Here, historical data can be measured against other data to answer the question “Why”
something happened. With Diagnostic Analytics, there is a possibility to drill down, find
out dependencies and identify patterns. Companies often go for Diagnostic Analytics as
it gives in-depth insights into a particular problem.

Predictive Analytics
As the term suggests, Predictive Analytics tells what is likely to happen. It uses the
findings of descriptive and diagnostic analytics to detect tendencies, clusters, and
exceptions, and to predict future trends, which makes it a valuable tool for forecasting.
Despite numerous advantages that predictive analytics brings, it is essential to
understand that forecasting is just an estimate, the accuracy of predictive analytics
highly depends on data quality and stability of the situation.
Prescriptive analytics
The purpose of prescriptive analytics is to literally ‘prescribe’ what action to take to
eliminate a future problem or take full advantage of a promising development. Data
analytics requires not only historical data but also external information due to the nature
of statistical algorithms. Besides, prescriptive analytics uses advanced tools and
technologies, like machine learning, business rules and algorithms, which makes it
sophisticated to implement and manage. Before deciding to adopt prescriptive analytics,
a company should compare required efforts vs. an expected added value.

Application of Data Analysis

Customer Analytics
It is important for Digital Marketing professionals to get greater visibility into the buying
behavior of customers. Data Analytics helps to get patterns and insights from customer
behavior data, both structured and unstructured. It is possible to combine, integrate and
analyze all data in one instance to get the desired outcomes. For example, you can
apply analytics to design marketing campaigns that improve sales conversion rates. It is
also possible to analyze customers’ beyond their basic profile segmentation. This would
help re-target advertisements with even more precise communication so as to reduce
the risk of customer churn or drop off.

Fraud and compliance

You can use data analytics in fraud risk management processes, including assessment,
prevention, detection, investigation, and reporting. Data analytics identifies patterns
deep in your data to identify fraud and generates volumes of information to make
regulatory reporting much faster. It is essential to use larger data sets to identify fraud
patterns and make detection algorithms work more accurately. Banks around the world
have started to use Business Intelligence and Data Analytics platforms to enhance their
risk and regulatory compliance programs.

Healthcare
The application of big data analytics in healthcare has a lot of positive and also
life-saving outcomes. Specific health data of a population (or of a particular individual)
can be analyzed to predict outcomes and potentially help to prevent epidemics, cure
disease, cut down costs, etc. For years, gathering huge amounts of data for medical
use has been costly and time-consuming. With advanced Data Analytics tools and
applications, it has become easier not only to collect such data but also to get relevant
and critical insights that can be used to provide better healthcare. The objective and
purpose of Data Analytics in Healthcare are to use data-driven findings to predict and
solve a problem before it is too late. Besides, it also helps assess diagnostic methods
and treatments faster.

Sports
Sports Analytics includes the use of data related to sports such as players' statistics,
weather conditions, and pitch conditions. Coaches can use data to optimize exercise
programs for their players and develop nutrition plans to maximize fitness. You can see
some game-changing results by using data analytics in sports.

Digital Advertisement
A study based on a global survey of 900 advertising leaders across North America,
Europe, and the Asia-Pacific region states that:

● Advertisers are planning to increase the average number of integrated data

sources from 5.4 today to 6.2 in 2019 to gain greater advertising effectiveness

insights.

● 94% of advertisers rely on a broad base of CRM data, from transactions and

contact information to brand preferences, to track advertising effectiveness.

● 91% of advertisers have or plan to adopt a data management platform (DMP) in

the next fiscal year.

Data Analytics Tools
The data analytics tools help businesses to know its data trends, build patterns and
analyze the complexities, and present data by converting data into understandable data
visualization formats.

6 Most Used Data Analytics Tools for 2020

● Tableau is a BI (Business Intelligence) and analytics software. It can connect to

any database, drag and drop to create visualizations and share the data with just

a click.

● Talend is an open source data integration platform. It provides various software

and services for data integration, data management, enterprise application

integration, data quality, cloud storage, and Big Data. Talend provides a

development environment that enables users to interact with several Big Data

sources and targets without having to understand or write complicated code.

Talend Big Data Basics is an introduction to Talend components that are shipped

with several products that interact with Big Data systems.

● Apache Spark is a unified analytics engine for large-scale data processing. It is a

framework that has become one of the key big data distributed processing

frameworks in the world. Spark can be deployed in a variety of ways, provides

native bindings for Java, Scala, Python, and R programming languages, and

supports SQL, streaming data, machine learning, and graph processing.

● R is a popular and powerful open source programming language for statistical

computing and graphics. R implements various statistical techniques like linear

and non-linear modeling, machine learning algorithms, time series analysis, and

classical statistical tests and so on. R consists of a language and a run-time

environment with graphics, a debugger, access to certain system functions, and

the ability to run programs stored in script files

 ● MATLAB is a programming language dedicated to mathematical and technical

computing and it is designed for engineers and scientists. The desktop

environment has a natural way of expressing computational mathematics such

as linear algebra, data analytics, signal and image processing. MATLAB features

an application specific solution called ‘Toolboxes’. Toolboxes provide a set of

MATLAB functions which are called as M-files that solves a specific set of

problems. There are various areas where Toolboxes are available such as digital

signal processing, control systems, neural network, simulations, Deep Learning,

and many other areas

● Weka is a collection of machine learning algorithms for data mining tasks. The

algorithms can either be applied directly to a dataset or called from your own

Java code. Weka contains tools for data pre-processing, classification,

regression, clustering, association rules, and visualization. It is also well-suited

for developing new machine learning schemes. It is an open-source GUI

software that allows easier implementation of machine learning algorithms

through a platform. You can understand the functioning of Machine Learning on

the data without having to write a line of code. It is ideal for Data Scientists who

are beginners in Machine Learning.

Required Skills For Data Analytics

Data Analysts collect, organize and interpret statistical information to make it useful for
a range of businesses and organizations. With so many businesses today relying
heavily on data about their customers, products, processes, inputs and the market,
these organizations are increasingly in need of talented, skilled people who can extract
information and insights from the data.

But what skills are employers looking for? In Data Analytics, there are specific skills and
qualities employers require of all applicants, regardless of the position.
Education will enhance some of these skills and abilities. Others can be sharpened with
experience and practice.

Let’s look at some of the top skill requirements.

Business Acumen
As an analyst it is important to understand the business strategy, how the business
works, how different the business is from its competitors, what is the market position
and many such questions. You must be able to have the desire to understand business
and think beyond.

Technical Knowledge
As a Data Analyst you work with software, data and systems. Hence mathematics and
statistics proficiencies are important. You need to understand the data value chain
which will help you draw inferences and extract meaningful insights. Knowledge of
programming languages such as Python, R, MATLAB are essential.

Communication Skills
As a Data Analyst you need to communicate with stakeholders, colleagues, data
suppliers, system owners and many others in the process of developing insights for
decision-making. Apart from interpreting the data it is important to share this information
with the audience.

Critical thinking and problem solving

You need to ask yourself a lot of questions and think beyond. You need to explore
different angles and use visual analytics to look and data with different perspectives.

Data Visualization and Presentation skills

No matter which tool you use you should be able to paint a comprehensive picture of
your insights and interpretations. Displaying your data with a click of a button may not
help. You need to present your insights to the audience effectively, have a logical order,
and be prepared with a list of answers to the obvious questions that the stakeholders
may raise. Skills to use Data Visualization tools such as Tableau, Spot fire, etc. will add
significant value.

How can you learn Data Analytics?

Data Analytics tutorials are all over the net. However, for graduates, the usual entry
point is a degree in Statistics, Mathematics or a related subject involving Math, such as
Economics or Data Science. Other degrees are also acceptable if they include informal
training in Statistics as part of the course, for instance Sociology or Informatics.

Career/Job Market in Data Analytics

Data analyst will be one of the most in-demand jobs in the coming years. According to a
recent study by Great Learning in India, More than 97,000 analytics positions remain
vacant in India due to the shortage of talent. 38% of all jobs posted are from the
Banking sector.

The average yearly salary of a Data Analyst is among the highest, with figures ranging
from €30,000 to €50,000 for junior profiles, through to €99,000 for senior ones.

Data Analysts who turn data-driven insights into actionable business recommendations
are often called Business Analysts. They use tools like Excel, Tableau, and SQL.
Business Analyst salaries range from $54,700 to $69,000 at the entry level. Pay scales
vary with the industry. Salaries for transportation logistics specialists usually start
around $79,000.

Future of Data Analytics

Roughly 2.3 trillion gigabytes of data is generated every day across the world, and this
will only rise in the future. This is Big Data and it is everywhere. Other than phones and
computers there are smart watches, smart televisions, smart wearable gadgets, and
many more in the market that further gather data from consumers, giving the scope for
the huge production of data. According to the forecasts of the World Economic Forum,
by 2020 data analysts will be in high demand in companies around the world. ...

This is further confirmed by IBM, which claims that the annual demand for data
scientists, data developers, and data engineers will lead to 700,000 new recruitments by
2020. By 2020, data science and analytics (DSA) job openings are predicted to grow to
2.7 million, representing a $187 billion market opportunity.