Unit 1

Introduction to Analytics
Analytics is the discovery, interpretation, and communication of meaningful
patterns in data. Especially valuable in areas rich with recorded information,
analytics relies on the simultaneous application of statistics, computer
programming and operations research to quantify performance. Analytics
often favors data visualization to communicate insight.

Organizations may apply analytics to business data to describe, predict, and

improve business performance. Specifically, areas within analytics include
predictive analytics, prescriptive analytics, enterprise decision management,
retail analytics, store assortment and stock-keeping unit optimization,
marketing optimization and marketing mix modeling, web analytics, sales
force sizing and optimization, price and promotion modeling, predictive
science, credit risk analysis, and fraud analytics.

4 Types of Data Analytics to Improve Decision-Making

1. Descriptive Analytics

Descriptive analytics is the simplest type of analytics and the foundation the
other types are built on. It allows you to pull trends from raw data and
succinctly describe what happened or is currently happening.

Descriptive analytics answers the question, “What happened?”

For example, imagine you’re analyzing your company’s data and find
there’s a seasonal surge in sales for one of your products: a video game
console. Here, descriptive analytics can tell you, “This video game console
experiences an increase in sales in October, November, and early
December each year.”

Data visualization is a natural fit for communicating descriptive analysis

because charts, graphs, and maps can show trends in data—as well as
dips and spikes—in a clear, easily understandable way.
2. Diagnostic Analytics

Diagnostic analytics addresses the next logical question, “Why did this
happen?”

Taking the analysis a step further, this type includes comparing coexisting
trends or movement, uncovering correlations between variables, and
determining causal relationships where possible.

Continuing the aforementioned example, you may dig into video game
console users’ demographic data and find that they’re between the ages of
eight and 18. The customers, however, tend to be between the ages of 35
and 55. Analysis of customer survey data reveals that one primary
motivator for customers to purchase the video game console is to gift it to
their children. The spike in sales in the fall and early winter months may be
due to the holidays that include gift-giving.

Diagnostic analytics is useful for getting at the root of an organizational

issue.

3. Predictive Analytics

Predictive analytics is used to make predictions about future trends or

events and answers the question, “What might happen in the future?”

By analyzing historical data in tandem with industry trends, you can make
informed predictions about what the future could hold for your company.

For instance, knowing that video game console sales have spiked in
October, November, and early December every year for the past decade
provides you with ample data to predict that the same trend will occur next
year. Backed by upward trends in the video game industry as a whole, this
is a reasonable prediction to make.

Making predictions for the future can help your organization formulate
strategies based on likely scenarios.

4. Prescriptive Analytics

Finally, prescriptive analytics answers the question, “What should we do

next?”
 Prescriptive analytics takes into account all possible factors in a scenario
and suggests actionable takeaways. This type of analytics can be
especially useful when making data-driven decisions.

Rounding out the video game example: What should your team decide to
do given the predicted trend in seasonality due to winter gift-giving?
Perhaps you decide to run an A/B test with two ads: one that caters to
product end-users (children) and one targeted to customers (their parents).
The data from that test can inform how to capitalize on the seasonal spike
and its supposed cause even further. Or, maybe you decide to increase
marketing efforts in September with holiday-themed messaging to try to
extend the spike into another month.

While manual prescriptive analysis is doable and accessible, machine-

learning algorithms are often employed to help parse through large volumes
of data to recommend the optimal next step. Algorithms use “if” and “else”
statements, which work as rules for parsing data. If a specific combination
of requirements is met, an algorithm recommends a specific course of
action. While there’s far more to machine-learning algorithms than just
those statements, they—along with mathematical equations—serve as a
core component in algorithm training.

Game changers and innovators in Analytics

In the field of analytics, "game changers" and "innovators" are individuals
or companies who have significantly advanced the capabilities of data
analysis through pioneering new techniques, technologies, or
applications, often leading to transformative insights and decision-
making across various industries; some notable examples include:
founders of leading data analytics platforms like Google Analytics,
pioneers in machine learning algorithms, developers of advanced
predictive modeling techniques, and companies like Netflix utilizing big
data for personalized recommendations; essentially, anyone who has
pushed the boundaries of what is possible with data analysis and made it
accessible to a wider range of users.
Key areas where game changers in analytics have made an impact:
 Machine Learning and AI:
Researchers and developers who advanced algorithms like deep
learning, neural networks, and natural language processing, allowing
for more complex data analysis and predictive modeling.
 Big Data Processing:
 Companies and individuals who developed frameworks like Hadoop
and Spark, enabling the processing and analysis of massive datasets
that were previously unmanageable.

 Cloud-based Analytics:
Pioneers in delivering data analytics services through cloud platforms,
making it more accessible and scalable for businesses of all sizes.
 Data Visualization:
Developers of innovative visualization tools and techniques that allow
for intuitive understanding of complex data patterns.
Some prominent examples of "game changers" in analytics:
 Jeff Hawkins (Palm Computing):
Early innovator in data collection and analysis through personal digital
assistants, paving the way for wearable tech and ubiquitous data
capture.
 Sergey Brin and Larry Page (Google):
Revolutionized search algorithms with their use of large-scale data
analysis, enabling highly relevant search results.
 Jerome Friedman, Trevor Hastie, and Robert Tibshirani (Stanford
University):
Developed statistical methods like "regularization" which are
fundamental to modern machine learning algorithms.
 Netflix:
Widely recognized for their use of sophisticated data analytics to
provide personalized movie recommendations based on user behavior.
 Amazon:
Pioneered data-driven decision making in retail, using customer data to
optimize pricing, inventory management, and product
recommendations.

Predictive analytics defined

Predictive analytics is the process of using data to forecast future outcomes. The
process uses data analysis, machine learning, artificial intelligence, and statistical
models to find patterns that might predict future behavior. Organizations can use
historic and current data to forecast trends and behaviors seconds, days, or years
into the future with a great deal of precision.
 How does predictive analytics work?
Data scientists use predictive models to identify correlations between different
elements in selected datasets. Once data collection is complete, a statistical
model is formulated, trained, and modified to generate predictions.

The workflow for building predictive analytics frameworks follows five basic steps:

1. Define the problem: A prediction starts with a good thesis and set of requirements. For
instance, can a predictive analytics model detect fraud? Determine optimal inventory
levels for the holiday shopping season? Identify potential flood levels from severe
weather? A distinct problem to solve will help determine what method of predictive
analytics should be used.
2. Acquire and organize data: An organization may have decades of data to draw upon, or
a continual flood of data from customer interactions. Before predictive analytics models
can be developed, data flows must be identified, and then datasets can be organized in a
repository such as a data warehouse like BigQuery.
3. Pre-process data: Raw data is only nominally useful by itself. To prepare the data for the
predictive analytics models, it should be cleaned to remove anomalies, missing data
points, or extreme outliers, any of which might be the result of input or measurement
errors.
4. Develop predictive models: Data scientists have a variety of tools and techniques to
develop predictive models depending on the problem to be solved and nature of the
dataset. Machine learning, regression models, and decision trees are some of the most
common types of predictive models.
5. Validate and deploy results: Check on the accuracy of the model and adjust
accordingly. Once acceptable results have been achieved, make them available to
stakeholders via an app, website, or data dashboard.

What are predictive analytics techniques?

In general, there are two types of predictive analytics models: classification and
regression models. Classification models attempt to put data objects (such as customers
or potential outcomes) into one category or another. For instance, if a retailer has a lot of
data on different types of customers, they may try to predict what types of customers will
be receptive to market emails. Regression models try to predict continuous data, such as
how much revenue that customer will generate during their relationship with the
company.

Predictive analytics tends to be performed with three main types of techniques:

Regression analysis

Regression is a statistical analysis technique that estimates relationships between

variables. Regression is useful to determine patterns in large datasets to determine the
correlation between inputs. It is best employed on continuous data that follows a known
distribution. Regression is often used to determine how one or more independent
variables affects another, such as how a price increase will affect the sale of a product.

Decision trees

Decision trees are classification models that place data into different categories based on
distinct variables. The method is best used when trying to understand an individual's
decisions. The model looks like a tree, with each branch representing a potential choice,
with the leaf of the branch representing the result of the decision. Decision trees are
typically easy to understand and work well when a dataset has several missing variables.

Neural networks

Neural networks are machine learning methods that are useful in predictive analytics
when modeling very complex relationships. Essentially, they are powerhouse pattern
recognition engines. Neural networks are best used to determine nonlinear relationships
in datasets, especially when no known mathematical formula exists to analyze the data.
Neural networks can be used to validate the results of decision trees and regression
models.

Uses and examples of predictive analytics

Fraud detection
 Predictive analytics examines all actions on a company’s network in real time to
pinpoint abnormalities that indicate fraud and other vulnerabilities.
Conversion and purchase prediction
Companies can take actions, like retargeting online ads to visitors, with data that
predicts a greater likelihood of conversion and purchase intent.
Risk reduction
Credit scores, insurance claims, and debt collections all use predictive analytics
to assess and determine the likelihood of future defaults.
Operational improvement
Companies use predictive analytics models to forecast inventory, manage
resources, and operate more efficiently.
Customer segmentation
By dividing a customer base into specific groups, marketers can use predictive
analytics to make forward-looking decisions to tailor content to unique
audiences.
Maintenance forecasting
Organizations use data to predict when routine equipment maintenance will be
required and can then schedule it before a problem or malfunction arises.

Experts view on Analytics

According to experts, analytics is a critical tool for businesses today,
allowing them to extract meaningful insights from data, enabling informed
decision-making, process optimization, and gaining a competitive edge
by identifying opportunities and mitigating risks; essentially turning raw
data into actionable intelligence to improve performance across various
aspects of an organization.
Key points about analytics from experts:
 Data-driven decision making:
Analytics empowers businesses to move beyond intuition and make
decisions based on concrete data analysis, leading to more accurate
predictions and better outcomes.
 Diverse applications:
Analytics can be applied across various industries and functions,
including marketing, finance, operations, customer service, and
healthcare, to identify trends, patterns, and customer behavior.
 Advanced techniques:
 Experts utilize sophisticated analytical methods like machine learning,
predictive modeling, and statistical analysis to uncover complex
relationships within data and generate valuable insights.

 Communication is key:
A critical aspect of analytics is the ability to clearly communicate
findings to stakeholders, translating complex data into understandable
insights that can be acted upon.
 Continuous improvement:
Analytics is not a one-time process but an ongoing cycle of data
collection, analysis, and refinement to adapt to changing market
dynamics and identify new opportunities.