Fundamental of Business Analytics - Chapter 1

Introduction to Business Analytics and Excel sheet

Three developments that Advanced Business Analytics

1. Ability to store and process large amounts of data

2. Research on Knowledge extraction from data

3. Advances in Computing power

First development

Techonological Advances

Second development

Ongoing research has resulted in numerous methodological developments

Third development

Explosion in computing power and storange capability

1. Strategic Decisions

2. Tactical Decisions

3. Operational Decisions

Three types of Decision making

Manager’s Responsibilities

To make strategic, tactical, or operational decisions.

Strategic Decisions

Involve higher-level issues concerned with the overall direction of the organization.

Strategic Decisions

Define the organization’s overall goals and aspirations for the future.

Strategic Decisions

usually the domain of higher-level executives and have a time horizon of three to five years

Tactical decisions

Concern how the organization should achieve the goals and objectives set by its strategy

Tactical decisions

Are usually the responsibility of midlevel management.

Tactical decisions

usually span a year and thus are revisited annually or even every six months.

Operational decisions

Affect how the firm is run from day to day

Operational decisions

they are the domain of operations managers, who are the closest to the customer.

1. Identify the Problem

2. Determine the Criteria

3. Determine the Alternatives

4. Evaluate the Alternatives

5. Choose an Alternative

Process of Decision Making (5)

1. Intuition

2. Tradition

3. Rule of Thumb

4. Relevant Data

Common approaches to making decisions include

Tradition

We’ve always done it this way

Intuition

Gut Feeling

Rule of Thumb

As the restaurant owner, I schedule twice the number of waiters and cooks on holidays

Business analytics

Scientific process of transforming data into insight for making better decisions.

Business analytics

Used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making.

Analytics

Encompassing the use of analytical techniques in the sciences and engineering as well, More broad than Business Analytics

1. Insights

2. Forecasting

3. Quantify Risk

4. Bettter alternatives

Tools of business analytics can aid decision making by (4)

Descriptive analytics

encompasses the set of techniques that describes what has happened in the past.

Descriptive analytics

Data Queries

Descriptive analytics

Reports

Descriptive analytics

Descriptive statistics

Descriptive analytics

Data Visualization - Data dashboards

Data Query

Request for information with certain characteristics from a database

Data dashboards

Collections of tables, charts, maps, and summary statistics that are updated as new data become available.

Data dashboards

Help management monitor specific aspects of the company’s performance related to their decision-making responsibilities

Data dashboards

For corporate-level managers, daily data dashboards might summarize sales by region, current inventory levels, and other company-wide metrics.

Data dashboards

Front-line managers may view dashboards that contain metrics related to staffing levels, local inventory levels, and short-term sales forecasts.

Data mining

Use of analytical techniques for better understanding patterns and relationships that exist in large data sets.

Unsupervised Learning Techniques

Which are descriptive methods that seek to identify patterns based on notions of similarity or Correlation

Cluster Analysis

Identifying Patterns based on Similarity

Association Rules

Identifying Patterns based on correlation

Predictive Analytics

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. (Predictive Analysis)

Supervised Learning Techniques

Which use past data to learn the relationship between an outcome variable of interest and a set of input variables.

Predictive Analytics

Linear Regression

Predictive Analytics

Time series Analysis

Data mining

Used to find patterns or relationships among elements of the data in a large database; often used in predictive analytics.

Predictive Analytics

Simulation

Simulation

Involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision.

Prescriptive Analytics

Indicates a course of action to take; that is, the output of a prescriptive model is a decision.

Predictive Analytics

Provide a forecast or prediction, but do not provide a decision.

Predictive Analytics

Predictive model combined with a rule creates _______

Rule-based models

Prescriptive models that rely on a rule or set of rules are often referred

Prescriptive Analytics

Use historical investment return data to determine the mix of investments that yield the highest expected return while controlling or limiting exposure to risk.

Prescriptive Analytics

Provide the cost-minimizing plant and distribution center locations subject to meeting the customer service requirements.

Prescriptive Analytics

Use historical data to yield revenue-maximizing discount levels and the timing of discount offers when goods have not sold as planned

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 & 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.

Utility theory

Assigns values to outcomes based on the decision maker’s attitude toward risk, loss, and other factors.

Big Data

Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software

1. Volume

2. Velocity

3. Variety

4. Veracity

Big Data 4 V’s

Volume of Big Data

Terabytes to exabytes of existing data to process.

Velocity of Big Data

Data in motion, Streaming data, miliseconds to seconds to respond

Velocity of Big Data

Real-time capture and analysis of data present unique challenges both in how data are stored and the speed with which those data can be analyzed for decision making

Variety of Big Data

Structured, Unstructured, text, multimedia.

Variety of Big Data

More complicated types of data are now available and are proving to be of great value to businesses

Nontraditional Information Sources

Analyzing Information is more complicated in part because of the processing required to transform the data into a numerical form that can be analyzed.

Veracity of Big Data

Data in doubt, Uncertainty due to data inconsistency & incompleteness, ambiguities, latency, deception, model approximations.

Hadoop

Open-source programming environment that supports big data processing through distributed storage and distributed processing on clusters of computers

MapReduce

Programming model used within Hadoop that performs the two major steps for which it is named: the map step and the reduce step.

Cloud computing,

Use of data and software on servers housed external to an organization via the internet.

Data security

Protection of stored data from destructive forces or unauthorized users, is of critical importance to companies.

Data Scientists

Who know how to effectively process and analyze massive amounts of data because they are well trained in both computer science and statistics

Internet of Things

Technology that allows data, collected from sensors in all types of machines, to be sent over the Internet to repositories where it can be stored and analyzed.

Advanced analytics

predictive and prescriptive analytics are sometimes referred to as

Accounting Analytics

1. Budget planning

2. Risk analysis

3. Financial health of companies

4. Forensic uses to check for fraud bribery embezzlement and money laundering

Financial Analytics

• Forecast financial performance.

• Assess the risk of investment portfolios and projects.

• Construct financial instruments such as derivatives.

• Construct optimal portfolios of investments.

• Allocate assets.

• Create optimal capital budgeting plans.

Human Resource (HR) Analytics

A relatively new area of application for analytics is the

Human Resource (HR) Analytics

• Has the mix of skill sets necessary to meet its needs.

• Is hiring the highest-quality talent and providing an environment that retains it.

• Achieves its organizational diversity goals

Marketing Analytics

fastest-growing areas for the application of analytics.

Sentiment analysis

Allows companies to monitor the reactions of their customers to the goods and services they offer and adjust their service and product offerings based on that data

Health Care Analytics

• Patient, staff, and facility scheduling.

• Patient flow.

• Purchasing.

• Inventory control.

Prescriptive Analytics

For diagnosis and treatment may prove to be the most important application of analytics in health care.

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 and FedEx.

• Companies can benefit from better inventory and processing control and more efficient supply chains.

Analytics for Government and Nonprofits

• Drive out inefficiencies.

• Increase the effectiveness and accountability of programs.

Sports Analytics

• Assess players for the amateur drafts.

• Decide how much to offer players in contract negotiations.

• Professional motorcycle racing teams use sophisticated optimization for gearbox design to gain competitive advantage.

• Teams use to assist with on-field decisions such as which pitchers to use in various games of a MLB playoff serie

Sports Analytics

Web Analytics

• The analysis of online activity

• visits to web sites and social media sites such as Facebook and LinkedIn.

• Leading companies apply descriptive and advanced analytics to data collected in online experiments to determine the best way to:

• Configure web sites.

• Position ads.

• Utilize social networks for the promotion of products and services.

To protect the data and to not misuse that data.

Companies have an obligation

understand trade-offs between allowing their data to be collected, and the benefits they accrue from allowing a company to collect and use that data.

• Clients and customers have an obligation to

General Data Protection Regulation

One of the strictest privacy laws is the General Data Protection Regulation.

General Data Protection Regulation

Went into effect in the European Union in May 2018.

Stipulations of General Data Protection Regulation

• The request for consent to use an individual’s data must be easily understood and accessible.

• The intended use of data must be specified.

• Must be easy to withdraw consent.

• The individual has a right to a copy of their data and the right to demand their data be erased.

Behave ethically. protecting data, being transparent about the data and how it was collected, and what it does and does not contain.

Analytics professionals have a responsibility to

Analysts must be

transparent about the methods used to analyze the data and any assumptions that have to be made for the methods used.

REPUBLIC ACT 10173 DATA PRIVACY ACT OF 2012

It is the policy of the State to protect the fundamental human right of privacy, of communication while ensuring free flow of information to promote innovation and growth. The State recognizes the vital role of information and communications technology in nation-building and its inherent obligation to ensure that personal information in information and communications systems in the government and in the private sector are secured and protected.

The National Privacy Commission

Responsible for administering and implementing the provisions of this Act, and to monitor and ensure compliance of the country with international standards set for data protection.