INTRODUCTION TO

BUSINESS ANALYTICS Lesson 1.1

INTRODUCTION Lesson 1.1.1
INTRODUCTION
• Three developments spurred recent explosive growth in the use of analytical
methods in business applications:
• First development:
 Technological advances, Internet social networks, and data generated from personal electronic
devices, produce incredible amounts of data for businesses.

 Businesses want to use these data to improve the efficiency and profitability of their
operations, better understand their customers, price their products more effectively, and gain a
competitive advantage.

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INTRODUCTION
• Second development:
 Ongoing research has resulted in numerous methodological developments, including:
 Advances in computational approaches to effectively handle and explore massive amounts of data
 Faster algorithms for optimization and simulation, and
 More effective approaches for visualizing data.

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INTRODUCTION
• Third development:
 The methodological developments were paired with an explosion in computing power and
storage capability.
 Better computing hardware, parallel computing, and cloud computing have enabled businesses
to solve big problems faster and more accurately than ever before.

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FIGURE 1.1 - GOOGLE TRENDS GRAPH OF
SEARCHES ON THE TERM ANALYTICS

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DECISION MAKING Lesson 1.1.2
DECISION MAKING
• Managers’ responsibility:
 To make strategic, tactical, or operational decisions.

• Strategic decisions:
 Involve higher-level issues concerned with the overall direction of the organization.
 These decisions define the organization’s overall goals and aspirations for the future.

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DECISION MAKING
• Tactical decisions:
 Concern how the organization should achieve the goals and objectives set by its strategy.
 They are usually the responsibility of midlevel management.

• Operational decisions:
 Affect how the firm is run from day to day.
 They are the domain of operations managers, who are the closest to the customer.

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DECISION MAKING
• Decision making can be defined as the following process
1. Identify and define the problem
2. Determine the criteria that will be used to evaluate alternative
solutions
3. Determine the set of alternative solutions
4. Evaluate the alternatives
5. Choose an alternative

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DECISION MAKING
• Common approaches to making decisions
 Tradition
 Intuition
 Rules of thumb
 Using the relevant data available

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BUSINESS ANALYTICS Lesson 1.1.3
DEFINED
BUSINESS ANALYTICS
DEFINED
• Business analytics:
 Scientific process of transforming data into insight for making better decisions.
 Used for data-driven or fact-based decision making, which is often seen as more objective
than other alternatives for decision making.

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BUSINESS ANALYTICS
DEFINED
• Tools of business analytics can aid decision making by:
 Creating insights from data
 Improving our ability to more accurately forecast for planning
 Helping us quantify risk
 Yielding better alternatives through analysis and optimization

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 A CATEGORIZATION OF
ANALYTICAL METHODS Lesson 1.1.4

AND MODELS
DESCRIPTIVE ANALYTICS
• Descriptive analytics: It encompasses the set of techniques that describes what
has happened in the past.

Examples - data queries, reports, descriptive statistics, data visualization (data dashboards),
data-mining techniques, and basic what-if spreadsheet models.

Data query - It is a request for information with certain characteristics from a database.

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DESCRIPTIVE ANALYTICS
• Data dashboards - Collections of tables,
charts, maps, and summary statistics that are
updated as new data become available.
 Uses of dashboards
 To help management monitor specific aspects of the
company’s performance related to their decision-making
responsibilities.
 For corporate-level managers, daily data dashboards might
summarize sales by region, current inventory levels, and
other company-wide metrics.
 Front-line managers may view dashboards that contain
metrics related to staffing levels, local inventory levels, and
short-term sales forecasts.

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PREDICTIVE ANALYTICS
• Predictive analytics: It consists of techniques that use models constructed from
past data to predict the future or ascertain the impact of one variable on another.
 Survey data and past purchase behavior may be used to help predict the market share of a new
product.

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PREDICTIVE ANALYTICS
• Techniques used in Predictive Analytics: contd.

Data mining

• Used to find patterns or relationships among

elements of the data in a large database; often used
in predictive analytics.

Simulation

• It involves the use of probability and statistics to

construct a computer model to study the impact of
uncertainty on a decision.

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PRESCRIPTIVE ANALYTICS
• Prescriptive Analytics: It indicates a best course of action to take
 Models used in prescriptive analytics:

Optimization models
• Models that give the best decision subject to constraints of the situation.

Simulation optimization
• Combines the use of probability and statistics to model uncertainty with
optimization techniques to find good decisions in highly complex and highly
uncertain settings.

Decision analysis
• Used to develop an optimal strategy when a decision maker is faced with
several decision alternatives and an uncertain set of future events.
• It also employs utility theory, which assigns values to outcomes based on
the decision maker’s attitude toward risk, loss, and other factors.

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PRESCRIPTIVE ANALYTICS
• Optimization models

Model Field Purpose

Portfolio models Finance Use historical investment return data to
determine the mix of investments that yield the
highest expected return while controlling or
limiting exposure to risk.
Supply network Operations Provide the cost-minimizing plant and
design models distribution center locations subject to meeting
the customer service requirements.
Price markdown Retailing Uses historical data to yield revenue-maximizing
models discount levels and the timing of discount offers
when goods have not sold as planned.

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BIG DATA Lesson 1.1.5
BIG DATA
• Big data: A set of data that cannot be managed, processed, or analyzed with
commonly available software in a reasonable amount of time.
 Big data represents opportunities.
 It also presents analytical challenges from a processing point of view and consequently has
itself led to an increase in the use of analytics.
 More companies are hiring data scientists who know how to process and analyze massive
amounts of data.

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BIG DATA
• Big data:
• Is defined as collections of datasets whose volume, velocity, or variety is so large that it is too
difficult to store, manage, process, and analyze the data using traditional databases and data
processing tools.

• Big Data has the potential to power next generation of smart applications that will leverage the
power of the data to make the applications intelligent.

• Big Data Analytics involves several steps

• Data Cleansing
• Data Munging (Wrangling)
• Data Processing
• Visualization

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BIG DATA EXAMPLES
Data generated by social networks including text, images, audio and video data.

Click-stream data generated by web applications such as e-Commerce to analyze user

Behavior.

Healthcare data collected in electronic health record (EHR) systems

Transactional data generated by banking and financial applications

CHARACTERISTICS OF BIG
DATA
VOLUME
•Volume in Big Data is Large.
•So Large that it would not fit on a single machine.
•It needs a specialized tools and frameworks to store process and analyze data.
•Examples:
• Social Media Applications process billions of data everyday.
• Internet of Things, Industrial, Healthcare
• Covid19 Data Visualizations
CHARACTERISTICS OF BIG
DATA
VELOCITY

• Velocity refers to how fast the data is generated.

• Velocity is a primary reason for the exponential growth of data.
• High Velocity of data results in the volume of data accumulated to become very large, in short span
of time.
CHARACTERISTICS OF BIG
DATA
VARIETY

• Variety refers to the forms of the data.

• Examples:
• Structured Data
• Unstructured Data
• Semi-Structured Data
• Text Data, Image, Audio, Video and Sensor Data

• Big Data Systems need to be flexible enough to handle such variety of data.
CHARACTERISTICS OF BIG
DATA
VERACITY

• Refers to how accurate is the data.

• To extract the value from the data, the data needs to be cleaned and remove noise.
• Data-Driven applications can reap the benefits of big data only when the data is meaningful and
accurate.
CHARACTERISTICS OF BIG
DATA
VALUE

• Value of Data refers to the usefulness of data for the intended purpose.
• The end goal of any big data analytics system is to extract value from the data.
• The value of the data is also related to the accuracy of the data.
DOMAIN SPECIFIC EXAMPLES
OF BIG DATA
WEB ANALYTICS

• Web Analytics deals with collection and analysis of data on the user visits on websites and cloud
applications.
• Analysis of this data can give insights about the user engagement and tracking the performance of
online advertisement campaigns.

CREDIT RISK MODELING

 Credit Risk Modeling are used by banking and financial institutions to score credit applications and
predict if a borrower will default or not in the future.
 Credit models generates numerical scores that summarize the creditworthiness of customers.
DOMAIN SPECIFIC EXAMPLES
OF BIG DATA
INTERNET OF THINGS
 Known as IoT, refers to things that have unique identities and are connected to the internet.
 The “Things” in IoT are the devices which can be perform remote sensing, actuating and monitoring.
 IoT devices can exchange data with other connected devices and applications (directly or indirectly).

CUSTOMER RECOMMENDATIONS
 Big Data can be used in Customer Recommendation system to analyze customer data such as
demographic data, shopping history, or customer feedback to predict customer preferences.
 With the use of the system, new products can be recommend based on the preference and
personalized offers.
 Customers with similar preferences can be grouped and targeted campaigns can be created for
customers.
ANALYTICS FLOW OF BIG
DATA
1. DATA COLLECTION
2. DATA PREPARATION
3. ANALYSIS TYPES
4. ANALYSIS MODES
5. VISUALIZATION
ANALYTICS FLOW OF BIG
DATA
1. DATA COLLECTION

• It is the first step for any analytics application

• Before the data can be analyzed, the data must be collected and ingested into a big data stack.
• The choice of tools and frameworks for data collection depends on the source of data and the type of
data being ingested.
ANALYTICS FLOW OF BIG
DATA
2. DATA PREPARATION

• Data preparation involves various tasks such as data cleansing, data wrangling or munging, de-
duplication, normalization, sampling and filtering.
• Data cleaning detects and resolves issues such as corrupt records, records with missing values, and
records with bad formatting.
• Data Wrangling or Munging deals with transforming the data from one raw format to another.
ANALYTICS FLOW OF BIG
DATA
3. ANALYSIS TYPES

• Analysis flow for big data helps in determining the analysis type for the application.
• This will help to decide various list or options for popular algorithms for each analysis type.
ANALYTICS FLOW OF BIG
DATA
4. ANALYSIS MODES

• with the analysis types selected for an application, the next step is to determine the analysis mode,
which can be either batch, real-time or interactive.
• The choice of mode depends on the requirements of the application.
ANALYTICS FLOW OF BIG
DATA
4. VISUALIZATIONS

• Visualizations helps in gaining good insights or knowledge discoveries.

• The choice of visualization tolls, serving databases and web frameworks is driven by the
requirements of the application.
• Visualizations can be:
• Static – used when you have analysis results stored in a serving database and wants a simple display results.
• Dynamic – it is used when application demands the result on regular updates.
• Interactive – it is used when your application wants to accept inputs from the user and display the results.
BUSINESS ANALYTICS Lesson 1.1.6
IN PRACTICE
FIGURE 1.2 - THE SPECTRUM OF BUSINESS
ANALYTICS

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BUSINESS ANALYTICS IN
PRACTICE
• Types of applications of analytics by application area
• Financial analytics
 Use of predictive models
 To forecast future financial performance
 To assess the risk of investment portfolios and projects
 To construct financial instruments such as derivatives

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BUSINESS ANALYTICS IN
PRACTICE
• Financial analytics (contd.)
 Use of prescriptive models
 To construct optimal portfolios of investments
 To allocate assets, and
 To create optimal capital budgeting plans.
 Simulation is also often used to assess risk in the financial sector

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BUSINESS ANALYTICS IN
PRACTICE
• Human resource (HR) analytics
 New area of application for analytics
 The HR function is charged with ensuring that the organization
 Has the mix of skill sets necessary to meet its needs
 Is hiring the highest-quality talent and providing an environment that retains it, and
 Achieves its organizational diversity goals.

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BUSINESS ANALYTICS IN
PRACTICE
• Marketing analytics
 Marketing is one of the fastest growing areas for the application of analytics.
 A better understanding of consumer behavior through the use of scanner data and data
generated from social media has led to an increased interest in marketing analytics.

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BUSINESS ANALYTICS IN
PRACTICE
• Marketing analytics (contd.)
 A better understanding of consumer behavior through marketing analytics leads to:
 The better use of advertising budgets
 More effective pricing strategies
 Improved forecasting of demand
 Improved product line management, and
 Increased customer satisfaction and loyalty

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FIGURE 1.3 - GOOGLE TRENDS FOR
MARKETING, FINANCIAL, AND HUMAN
RESOURCE ANALYTICS, 2004–2012

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BUSINESS ANALYTICS IN
PRACTICE
• Health care analytics
 Descriptive, predictive, and prescriptive analytics are used:
 To improve patient, staff, and facility scheduling
 Patient flow
 Purchasing
 Inventory control
 Use of prescriptive analytics for diagnosis and treatment

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BUSINESS ANALYTICS IN
PRACTICE
• Supply chain analytics
 The core service of companies such as UPS and FedEx is the efficient delivery of goods, and
analytics has long been used to achieve efficiency.
 The optimal sorting of goods, vehicle and staff scheduling, and vehicle routing are all key to
profitability for logistics companies such as UPS, FedEx, and others like them.
 Companies can benefit from better inventory and processing control and more efficient supply
chains.

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BUSINESS ANALYTICS IN
PRACTICE
• Analytics for government and nonprofits
 To drive out inefficiencies
 To increase the effectiveness and accountability of programs
 Analytics for nonprofit agencies
 To ensure their effectiveness and accountability to their donors and clients.

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BUSINESS ANALYTICS IN
PRACTICE
• Sports analytics
 Used for player evaluation and on-field strategy in professional sports.
 To assess players for the amateur drafts and to decide how much to offer players in contract negotiations.
 Professional motorcycle racing teams that use sophisticated optimization for gearbox design to gain competitive
advantage.

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BUSINESS ANALYTICS IN
PRACTICE
• Sports analytics (contd.)
 The use of analytics for off-the-field business decisions is also increasing rapidly.
 Using prescriptive analytics, franchises across several major sports dynamically adjust ticket prices throughout the
season to reflect the relative attractiveness and potential demand for each game.

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 END OF THE
PRESENTATION. - Mam Intin

THANK YOU