© 2013, published by Flat World Knowledge 12-1 Information Systems: A Manager’s Guide to Harnessing Technology, version 2.0 John Gallaugher

© 2013, published by Flat World Knowledge 12-1

Information Systems: A Manager’s Guide to Harnessing

Technology, version 2.0John Gallaugher

© 2013, published by Flat World Knowledge

Published by:

Flat World Knowledge, Inc.

© 2013 by Flat World Knowledge, Inc. All rights reserved. Your use of this work is subject to the License Agreement available here http://www.flatworldknowledge.com/legal. No part of this work may be used, modified, or reproduced in any form or by any means except as expressly permitted under the License Agreement.

12-2


Chapter 12The Data Asset: Databases,

Business Intelligence, Big Data, and Competitive Advantage

12-3


Learning Objectives

• Understand how increasingly standardized data, access to third-party data sets, cheap, fast computing and easier-to-use software are collectively enabling a new age of decision making

• Be familiar with some of the enterprises that have benefited from data-driven, fact-based decision making

12-4


Data and Decision Making

• Big data: Massive amount of data available to today’s managers – Unstructured, big, and costly to work through

conventional databases– Made available by new tools for analysis and insight

• Decision making is data-driven, fact-based and enabled by:– Standardized corporate data– Access to third-party datasets through cheap, fast

computing and easier-to-use software

12-5


Data and Decision Making

• Business intelligence (BI): Combines aspects of reporting, data exploration and ad hoc queries, and sophisticated data modeling and analysis

• Analytics: Driving decisions and actions through extensive use of:– Data– Statistical and quantitative analysis– Explanatory and predictive models– Fact-based management

12-6


Enterprises that Have Benefited from Data Mastery

• Walmart - Entered the top of the Fortune 500 list• Harrah’s Casino Hotels - Grew twice as profitable as

Caesars and rich enough to acquire it• Capital One - Found valuable customers that

competitors were ignoring– Its ten-year financial performance was ten times

greater than the S&P 500

12-7


Learning Objectives

• Understand the difference between data and information

• Know the key terms and technologies associated with data organization and management

12-8


Organizing Data - Key Terms and Technology

• Database: Single table or a collection of related tables

• Database management systems (DBMS): Software for creating, maintaining, and manipulating data– Known as database software

• Structured query language (SQL): Used to create and manipulate databases

12-9


Organizing Data - Key Terms and Technology

• Database administrator (DBA): Job title focused on directing, performing, or overseeing activities associated with a database or set of databases– Database design and creation– Implementation– Maintenance– Backup and recovery– Policy setting and enforcement– Security

12-10


Key Terms Associated with Database Systems

• List of data, arranged in columns or fields and rows or records

Table or file

• Column in a database table• Represents each category of data contained in a record

Column or field

• Row in a database table• Represents a single instance of whatever the table keeps

track of like student or faculty

Row or record

12-11


Key Terms Associated with Database Systems

• Code that unlocks encryption • Field or combination of fields used to uniquely identify a

record, and to relate separate tables in a database like social security number

Key

• Most common standard for expressing databases• Tables or files are related based on common keys

Relational database

12-12


Learning Objectives

• Understand various internal and external sources for enterprise data

• Recognize the function and role of data aggregators, the potential for leveraging third-party data, the strategic implications of relying on externally purchased data, and key issues associated with aggregators and firms that leverage externally sourced data

12-13


Transaction Processing Systems

• Record a transaction or some form of business-related exchange, such as a cash register sale, ATM withdrawal, or product return– Transaction: Some kind of business exchange

• Loyalty card: System that provides rewards in exchange for consumers allowing tracking and recording of their activities – Enhances data collection and represents a significant

switching cost

12-14


Enterprise Software

• Firms set up systems to gather additional data beyond conventional purchase transactions or Web site monitoring

• Customer relationship management systems (CRM) - Empower employees to track and record data at nearly every point of customer contact

• Includes other aspects that touch every aspect of the value chain including SCM and ERP

12-15


Surveys

• Firms supplement operational data with additional input from surveys and focus groups

• Direct surveys can give better information than a cash register

• Many CRM products have survey capabilities that allow for additional data gathering at all points of customer contact

12-16


External Sources

• Organizations can have their products sold by partners and can rely heavily on data collected by others

• Data from external sources might not yield competitive advantage on its own– Can provide operational insight for increased

efficiency and cost savings– May give firms a high-impact edge

12-17


Data Aggregators

• Firms that collect and resell data• One has to be aware of the digital tracking of

individuals– Possible by the availability of personal information

online

12-18


Learning Objectives

• Know and be able to list the reasons why many organizations have data that can’t be converted to actionable information

• Understand why transactional databases can’t always be queried and what needs to be done to facilitate effective data use for analytics and business intelligence

• Recognize key issues surrounding data and privacy legislation

12-19


Reasons for Poor Information

• Incompatible systems– Legacy systems: Older information systems that are

incompatible with other systems, technologies, and ways of conducting business

• Operational data cannot always be queried– Most transactional databases are not set up to be

simultaneously accessed for reporting and analysis– Database analysis requires significant processing

12-20


Learning Objectives

• Understand what data warehouses and data marts are and the purpose they serve

• Know the issues that need to be addressed in order to design, develop, deploy, and maintain data warehouses and data marts

12-21


Data Warehouses and Data Marts

• Set of databases designed to support decision making in an organization

• Structured for fast online queries and exploration• Collects data from many different operational

systems• Data mart: Database or databases focused on

addressing the concerns of a specific problem or business unit

12-22


Data Warehouses and Data Marts

• Marts and warehouses may contain huge volumes of data

• Building large data warehouses can be expensive and time consuming

• Large-scale data analytics projects should build on visions with business-focused objectives

12-23


Figure 12.2 - Information Systems Supporting Operations and Analysis

12-24


Maintaining Data Warehouses and Data Marts

• Firms can address the broader issues needed to design, develop, deploy, and maintain its system through data:– Relevance– Sourcing– Quantity and quality– Hosting– Governance

12-25


Insights from Unstructured Big Data

• Hadoop - Made up of half-dozen separate software pieces and requires the integration of these pieces to work

• Advantages– Flexibility– Scalability– Cost effectiveness– Fault tolerance

12-26


E-Discovery

• Identifying and retrieving relevant electronic information to support litigation efforts– Firm should account for it in its archiving and data

storage plans– Data can be used later and therefore should be stored

in order

12-27


Learning Objectives

• Know the tools that are available to turn data into information

• Identify the key areas where businesses leverage data mining

• Understand some of the conditions under which analytical models can fail

• Recognize major categories of artificial intelligence and understand how organizations are leveraging this technology

12-28


Business Intelligence Toolkit

• Provide regular summaries of information in a predetermined format

Canned reports

• Puts users in control so that they can create custom reports on an as-needed basis

•By selecting fields, ranges, summary conditions, and other parameters

Ad hoc reporting tools

• Heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics

Dashboards

• Takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube

•Data cube: Stores data in OLAP report

Online analytical processing (OLAP)

12-29


Data Mining

• Using computers to identify hidden patterns in, and to build models from, large data sets– Customer segmentation and market basket analysis– Marketing and promotion targeting– Collaborative filtering and customer churn– Fraud detection, financial modeling, and hiring and

promotion• Prerequisites– Organization must have clean, consistent data– Events in that data should reflect trends

12-30


Problems in Data Mining

• Firm is overexposed to risk

Using bad data can give wrong estimates

• When the market does not behave as it has in the past, computer-driven investment models are not effective

Historical consistency

• Build a model with so many variables that the solution arrived at might only work on the subset of data used to create it

Over-engineer

12-31


Skills for Data Mining

Information technology Statistics

Business knowledge

12-32


Artificial Intelligence (AI)

• Computer software that seeks to reproduce or mimic human thought, decision making, or brain functions– Data mining has its roots in AI

• Neural network: Examines data and hunts down and exposes patterns, in order to build models to exploit findings

• Expert systems: Leverages rules or examples to perform a task in a way that mimics applied human expertise

12-33


Artificial Intelligence

• Genetic algorithms: Model building techniques where computers examine many potential solutions to a problem– Modifies various mathematical models that have to

be searched for a best alternative

12-34


Learning Objectives

• Understand how Walmart has leveraged information technology to become the world’s largest retailer

• Be aware of the challenges that face Walmart in the years ahead

12-35


Walmart - Data-Driven Value Chain

• Largest retailer in the world– Source of competitive advantage is scale

• Efficiency starts with a proprietary system called retail link– Retail link - Records a sale and automatically triggers

inventory reordering, scheduling, and delivery– Inventory turnover ratio: Ratio of a company’s annual

sales to its inventory• Back-office scanners keep track of inventory as

supplier shipments come in12-36


Data Mining Prowess

• Gets data from varying environmental conditions• Protects the firm from a retailer’s twin nightmares– Too much inventory – Too little inventory

• Helps the firm tighten operational forecasts– Enables prediction

• Data drives the organization– Reports form the basis of sales meetings and

executive strategy sessions

12-37


Sharing Data and Keeping Secrets

• Walmart shares sales data with relevant suppliers– Stopped sharing data with information brokers– Custom builds large portions of its information

systems to keep competitors off its trail– Other aspects of the firm’s technology remain

confidential

12-38


Challenges

• Finding huge markets or dramatic cost savings– To boost profits and continue to move its stock price

higher• Criticisms – Accusations of sub par wages and a magnet for union

activists– Poor labor conditions at some of the firm’s contract

manufacturers– Demand prices so aggressively low that suppliers end

up cannibalizing their own sales at other retailers

12-39


Learning Objectives

• Understand how Caesars has used IT to move from an also-ran chain of casinos to become the largest gaming company based on revenue

• Name some of the technology innovations that Caesars is using to help it gather more data, and help push service quality and marketing program successaa

12-40


Caesars’ Solid Gold CRM for the Service Sector

• Caesars Entertainment provides an example of exceptional data asset leverage in the service sector– Focus on how this technology enables world-class

service through customer relationship management• Leveraged its data-powered prowess to move: – From a chain of casinos – To largest gaming company by revenue

12-41


Collecting Data

• Caesars’ collects customer data on everything one might do at their properties– Used to track preferences and see if a customer is

worth pursuing• Total rewards loyalty card system– Opt-in: Marketing effort that requires customer

consent – Opt-out programs - Enroll all customers by default

12-42


Most Valuable Customers

• Customer lifetime value (CLV): Present value of the likely future income stream generated by an individual purchaser

• Tracks over ninety demographic segments– Each responds differently to different approaches– Iterative model of mining the data to identify patterns– Creates and tests a hypothesis against a control group– Analyzes to statistically verify the outcome– Profits come from locals and people 45 years and

older12-43


Data Driven Service

• Identifies the high value customers and gives them special attention

• Customers could obtain reserved tables and special offers

• Tracks gamblers suffering unusual losses and provides feel-good offers to them

• CRM effort monitors any customer behavior changes• Customers come back as they feel they are treated

better than competitors

12-44


Data Driven Service

• Focuses on service quality and customer satisfaction – Embedded in its information systems and operational

procedures• Employees are measured on metrics that include

speed and friendliness – Compensated based on guest satisfaction ratings

• Changed the corporate culture at Caesars – From very-property-for-itself mentality – To a collaborative, customer-focused enterprise

12-45


Innovation and Strategy

12-46


Challenges

• Gaming is a discretionary spending item, and when the economy tanks, gambling is one of the first things consumers will cut– Taken private: Publicly held company has its

outstanding shares purchased by an individual or by a small group of individuals who wish to obtain complete ownership and control

• Has been through a risky overly optimistic buyout

12-47

Documents

© 2013, published by Flat World Knowledge 12-1 Information Systems: A Manager’s Guide to Harnessing Technology, version 2.0 John Gallaugher