business analytics: chapter 1

It is the responsibility of managers to plan, coordinate, organize, and lead their organizations to better performance. Ultimately, managers’ responsibilities require that they make strategic, tactical, or operational decisions.

Decision Making

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.

Strategic Decisions

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

Strategic 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.

Operational Decisions

the 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

Business Analytics

the tools of business analytics can aid decision making by creating insights from data, by improving our ability to more accurately forecast for planning, by helping us uantify risk, and by yielding better alternatives through analysis and optimization

Business Analytics

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

Descriptive Analytics

Examples are data queries, reports, descriptive statistics, data visualization including data dashboards, some data-mining techniques, and basic what-if spreadsheet models

Descriptive Analytics

request for information with certain characteristics from a database

Data Query

example, a query to a manufacturing plant’s database might be for all records of shipments to a particular distribution center during the month of March. This query provides descriptive information about these shipments: the number of shipments, how much was included in each shipment, the date each shipment was sent, and so on. A report summarizing relevant historical information for management might be conveyed by the use of descriptive statistics (means, measures of variation, etc.) and data-visualization tools (tables, charts, and maps)

Data Query

techniques can be used to find patterns or relationships in a large database

Simple descriptive statistics and data- visualization

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

Data Dashboards

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

Dashboards

use of analytical techniques for better understanding patterns and relationships that exist in large data set

Data mining

For example, by analyzing text on social network platforms like Twitter, data-mining techniques (including cluster analysis and sentiment analysis) are used by companies to better understand their customers. By categorizing certain words as positive or negative and keeping track of how often those words appear in tweets, a company like Apple can better understand how its customers are feeling about a product like the Apple Watch

Data Mining

consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another

Predictive Analytics

Linear regression, time series analysis, some data- mining techniques, and simulation, often referred to as

Risk Analysis

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

Simulation

example, banks often use simulation to model investment and default risk in order to stress-test financial models Simulation is also often used in the pharmaceutical industry to assess the risk of introducing a new drug

Simulation

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

Prescriptive Anaytics

any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software. IBM describes the phenomenon of big data through the four

Big Data

data are collected electronically, we are able to collect more of it. To be useful, these data must be stored, and this storage has led to vast quantities of data. Many companies now store in excess of 100 terabytes of data (a terabyte is 1,024 gigabyte

Volume

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.

Velocity

example, the New York Stock Exchange collects 1 terabyte of data in a single trading session, and having current data and real-time rules for trades and predictive modeling are important for managing stock portfolios

Velocity

addition to the sheer volume and speed with which companies now collect data, more complicated types of data are now available and are proving to be of great value to businesses.

Variety

data could have many missing values, which makes reliable analysis a challenge. Inconsistencies in units of measure and the lack of reliability of responses in terms of bias also increase the complexity of the data

Veracity

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

Hadoop

provides a divide-and conquer approach to handling massive amounts of data, dividing the storage and processing over multiple computers.

Hadoop

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

MapReduce

divides the data into manageable subsets and distributes it to the computers in the cluster (often termed nodes) for storing and processin

The map step

collects answers from the nodes and combines them into an answer to the original problem

The reduce step

the protection of stored data from destructive forces or unauthorized users, is of critical importance to companies

Data Security

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

Internet of Things (IoT)

ability to collect data from products has enabled the companies that produce and sell those products to better serve their customers and offer new services based on analytics

Internet of Things (IoT)

involves tools as simple as reports and graphs to those that are as sophisticated as optimization, data mining, and simulation

Business Analytics

sometimes referred to as advanced analytics. Not all companies reach that level of usage, but those that embrace analytics as a competitive strategy often do

predictive and prescriptive analytics

(1) has the mix of skill sets necessary to meet its needs,

(2) is hiring the highest-quality talent and providing an environment that retains it, and

(3) achieves its organizational diversity goals

Human Resource (HR) Analytics

one of the fastest-growing areas for the application of analytics

Marketing Analytics

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

Marketing Analytics

Descriptive, predictive, and prescriptive analytics are used to improve patient, staff, and facility scheduling; patient flow; purchasing; and inventory control

Marketing Analytics

the increase because of pressure to simultaneously control costs and provide more effective treatment

Health Care Analytics

the core service of companies is the efficient delivery of goods and analytics has long been used to achieve efficiency

Health Care Analytics

the core service of companies is the efficient delivery of goods and analytics has long been used to achieve efficiency

Supply-Chain Analytics

high level, concerned with the overall direction of the business

Decisions may be Strategic

mid level, concerned with how to achieve the strategic goals of the business

Decisions may be Tactical

day-to-day decisions that must be made to run the company

Decisions may be Operational

uncertainty

overwhelming number of alternatives

Two factors that makes the decision making difficult

assist by identifying and mitigating uncertainty and by prescribing the best course of action from a very large number of alternatives

Business Analytics

describes what has happened and includes tools such as reports, data visualization, data dashboards, descriptive statistics, and some data mining technique

Descriptive

use past data to predict futuree vents or ascertain the impact of one variable on another. These techniques include regression, data mining, forecasting, and simulation

Predictive

determine a course of action. This class of analytical techniques includes rule- based models, simulation, decision analysis, and optimization

Prescriptive

help us better understand the uncertainty and risk associated with our decision alternatives

Descriptive and Predictive analytics

often referred to as advanced analytics, can help us make the best decision when facing a myriad of alternatives.

Predictive and prescriptive analytics

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

Big data