Data Analysis - Approach & Techniques

1. Data Analysis – Approach and Techniques

2. Visualize
Data
Extract
Intelligence
Analyse
Data
Organise
Data
Extract
Raw data
Data analytics is the process of analyzing raw data in order to draw out meaningful,
actionable insights,
Data analytics as a form of business intelligence, used to solve specific problems and challenges
 It’s all about finding patterns in a dataset which can tell you something useful and relevant about
a particular area of the business
 Data analytics helps you to make sense of the past and to predict future trends and behaviors;
rather than basing your decisions and strategies on guesswork, you’re making informed choices
 Armed with the insights drawn from the data, businesses and organizations are able to develop a
much deeper understanding of their audience, their industry, and their company as a whole
 How certain customer groups behave, for example, or how employees engage with a particular
tool.

3. What is Data Analytics?
 All companies collect loads of data all the time—but, in its raw
form, this data doesn’t really mean anything. This is where data
analytics comes in. Data analytics is the process of analyzing
raw data in order to draw out meaningful, actionable insights,
which are then used to inform and drive smart business decisions.
 A data analyst will extract raw data, organize it, and then analyze
it, transforming it from incomprehensible numbers into coherent,
intelligible information. Having interpreted the data, the data
analyst will then pass on their findings in the form of suggestions
or recommendations about what the company’s next steps should
be.
 Data analytics can be considered as a form of business
intelligence, used to solve specific problems and challenges
within an organization.
 Data analytics helps you to make sense of the past and to predict
future trends and behaviors.

4. Data Analyst Vs Data Scientist
Data Analyst
 Data analyst is usually part of the Business Intelligence
team, and their work often has a direct impact on the
decision-making
 Interpreting data and identifying patterns using statistical
techniques.
 Developing databases and data collection systems to
optimize statistical efficiency.
 Filtering and cleaning data to ensure efficiency in data
collection.
Data Scientist
 Data scientist will work deeper within the data, using data
mining and machine learning to identify patterns and build
models to support hypothesis of predictions.
 Locating valuable sources of data and developing processes to
gather such data.
 Presenting findings and information using data visualisation
techniques.
 Suggesting solutions and strategies for overcoming business
problems.

5. WHAT
 Descriptive analytics
is a simple, surface-
level type of analysis
that looks at what has
happened in the past.
 Two main techniques
used in descriptive
analytics are data
aggregation and data
mining
WHY
 While descriptive
analytics looks at the
“what”, diagnostic
analytics explores the
“why”
 Probability theory,
regression analysis,
filtering, and time-
series data analytics
are used for
Diagnostic Analysis
FUTURE
 Predictive analytics
tries to predict what is
likely to happen in
the future.
 Predictive analytics
estimates the
likelihood of a future
outcome based on
historical data and
probability theory.
HOW
 Prescriptive
analytics advises on
the actions and
decisions that should
be taken.
 Prescriptive analytics
is more complex and
involve working with
algorithms, ML, and
computational
modeling procedures

7. Why you are
conducting
analysis and what
question or challenge
you hope to solve
Clearly defined
problem statement
Collect data from all
relevant data sources.
Structures or
unstructured Data
Internal system, public
datasets email
marketing tools
Thoroughly clean your
dataset.
Remove duplicates,
anomalies, or missing
data
Identify outliers and
remediate
Time consuming but
mandatory
How you analyze the
data will depend on the
question you’re
answering and the
kind of data you’re
working
Example Regression
analysis, cluster
analysis, and time-series
analysis
Transform data into
valuable insights
Present data in easy-to-
understand format
Story telling with data

8. Uses of Data Analytics
 Data is everywhere and actually has infinite
number of uses, across all businesses globally.
 Data Analytics is used to make faster and better
Business decisions.
 Helps reduce business cost and develop new and
innovative products and services.
 Profound impact on Market research and Time to
Market giving organizations a huge advantage over
its competitors.
 Prediction
 Future sales
 Purchasing behavior
 To protect against Fraud
 Marketing Campaign effectiveness
 Improve customer acquisition and retention
 Increase efficiency in operations, Supply chain etc.
5 Step Approach
 Why? Define the question! Why is the analysis
being conducted and what is the expected output?
 Define Problem statement
 What data is needed?
 Where will the Data come from?
 Collect the necessary Data
 Primary Data Sources like Internal Data
Sources (CRM Tools and Email marketing
tools)
 Secondary Data Courses or External Data
Sources (Publicly available Data. Web
scrapping etc..)
 Clean/Scrub Data to remove, Duplicates,
Anomalies, Missing Data
 Analyse the Data
 Regression Analysis, Cluster Analysis and
Time Series Analysis etc..

9. Measurable or countable data
Quantitative
Analysis
• How many? How much? How often?
• Fixed and universal measurements
• Statistical methodology used
• Mostly commonly used analysis
Descriptive data often expressed in words
Qualitative
Analysis
• Describes attributes, explains why and what?
• Dynamic and subjective, Open to multiple interpretation
• Data gathered through observations and discussion
• Categorise data into meaningful themes and groups
Classification of Analysis

10. Data analytics techniques
Regression
analysis
 Regression analysis is used to estimate the relationship between a set of variables.
 Regression analysis is mainly used to make predictions.
 This method is used to estimate or “model” the relationship between a set of variables.
 Correlation between two or more values will be used to predict another variable or a likely outcome.
 The aim of regression analysis is to estimate how one or more variables might impact the dependent variable, in order
to identify trends and patterns.
 Example
 Predicting future Sales revenue based on Marketing Spend.
 Marketing spend is an independent variable
 Sales revenue is dependent variable
 Determine the impact on revenue Vs the spend on marketing
 Decide to increase, decrease or maintain Marketing spend.
 Cause and Effect cannot be determined in this Analysis

11. Data analytics techniques
Factor
analysis
 Factor analysis is a technique used to reduce a large number of variables to a smaller number of factors
 It works on the basis that multiple separate, observable variables correlate with each other because they are all
associated with an underlying construct
 Helps Condense large data into Smaller more manageable samples and helps uncover hidden patterns
 This technique allows to explore concepts that cannot be easily measured or observed—such as wealth, happiness,
fitness, or, for a more business-relevant example, customer loyalty and satisfaction.
 Available data can be grouped into single underlying construct
 Example
 Customer survey data for healthcare product
 Cluster data based on age group, monthly income, location, ethinicity

12. Data analytics techniques
Cohort
analysis
 Cohort analysis is defined on Wikipedia as follows: “Cohort analysis is a subset of behavioral analytics that takes the
data from a given dataset and rather than looking at all users as one unit, it breaks them into related groups for analysis.
These related groups, or cohorts, usually share common characteristics or experiences within a defined time-span.”
 Dividing your data down into groups based on certain characteristics (based on Age, Product, Category, Event Etc..)
over a period of time and analyzing the outcomes rather than looking at a single or isolated snapshot of behavior.
 Cohort analysis is dynamic, allowing for valuable insights about the customer lifecycle. This helps identify patterns of
behavior at various points in the customer journey.
 Example :- Students who enrolled at university in 2021 may be referred to as the 2021 cohort.
 Ultimately, cohort analysis allows companies to optimize their service offerings (and marketing) to provide a more
targeted, personalized experience
 This analysis allows companies to tailor their service to specific customer segments (or cohorts).
 Let’s imagine you run a 50% discount campaign in order to attract potential new customers to your website.
 Once you’ve attracted a group of new customers (a cohort), you’ll want to track whether they actually buy anything
and, if they do, whether or not, they make a repeat purchase. With these insights, you’ll start to gain a much better
understanding of when this particular cohort

13. Data analytics techniques
Cluster
analysis
 Cluster analysis is an exploratory technique that seeks to identify structures within a dataset.
 The goal of cluster analysis is to sort different data points into groups (or clusters) that are internally homogeneous and
externally heterogeneous.
 This means that data points within a cluster are similar to each other, and dissimilar to data points in another
cluster.
 Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other
algorithms.
 Real world application example
 In marketing, cluster analysis is commonly used to group a large customer base into distinct segments, allowing for
a more targeted approach to advertising and communication
 Insurance firms might use cluster analysis to investigate why certain locations are associated with a high number of
insurance claims.
 in geology, where experts will use cluster analysis to evaluate which cities are at greatest risk of earthquakes (and
thus try to mitigate the risk with protective measures).
 While cluster analysis may reveal structures within your data, it won’t explain why those structures exist. With that in
mind, cluster analysis is a useful starting point for understanding your data and informing further analysis.

14. Data analytics techniques
Time
Series
analysis
 Statistical technique used to identify trends and cycles over time
 Time series data is a sequence of data points which measure the same variable at different points in time (for example,
weekly sales figures or monthly email sign-ups). By looking at time-related trends, analysts are able to forecast how the
variable of interest may fluctuate in the future.
 Main patterns that can be interpreted are :
•Trends: Stable, linear increases or decreases over an extended time period.
•Seasonality: Predictable fluctuations in the data due to seasonal factors over a short period of time. For example,
you might see a peak in swimwear sales in summer around the same time every year.
•Cyclic patterns: Unpredictable cycles where the data fluctuates. Cyclical trends are not due to seasonality, but
rather, may occur as a result of economic or industry-related conditions.
 Time series analysis and forecasting is used across a variety of industries, most commonly for stock market analysis,
economic forecasting, and sales forecasting.
 There are different types of time series models depending on the data you’re using and the outcomes you want to
predict. These models are typically classified into three broad types: the autoregressive (AR) models, the integrated (I)
models, and the moving average (MA) models

15. Data analytics techniques
Sentiment
analysis
 When you think of data, your mind probably automatically goes to numbers and spreadsheets. Many companies
overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people (especially
customers) write and say about you.
 One highly useful qualitative technique is sentiment analysis, a technique which belongs to the broader category of text
analysis—the process of sorting and understanding textual data. With sentiment analysis, the goal is to interpret and
classify the emotions conveyed within textual data
 From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand,
product, or service. Types of Sentiment Analysis
•Fine-grained sentiment analysis: Focuses on opinion polarity (i.e. positive, neutral, or negative). For example,
we can analyze star ratings given by customers, categorize the various ratings along a satisfaction scale ranging
from very positive to very negative.
•Emotion detection: This model often uses complex machine learning algorithms to pick out various emotions
from your textual data. You might use an emotion detection model to identify words associated with happiness,
anger, frustration, and excitement, giving you insight into how your customers feel when writing about you or
your product on, say, a product review site.
•Aspect-based sentiment analysis: This type of analysis allows you to identify what specific aspects the
emotions or opinions relate to, such as a certain product feature or a new ad campaign. If a customer writes that
they “find the new Instagram advert so annoying”, your model should detect not only a negative sentiment, but
also the object towards which it’s directed.