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Information Systems:
A Manager’s Guide to Harnessing Technology

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Chapter 11
The Data Asset: Databases, Business Intelligence, and Competitive Advantage

Learning Objectives

  • Understand how increasingly standardized data, access to third-party datasets, 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
  • Understand the difference between data and information
  • Know the key terms and technologies associated with data organization and management

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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
  • 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

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Learning Objectives

  • Recognize key issues surrounding data and privacy legislation
  • 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
  • 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

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Learning Objectives

  • Recognize major categories of artificial intelligence and understand how organizations are leveraging this technology
  • Understand how Wal-Mart has leveraged information technology to become the world’s largest retailer
  • Be aware of the challenges that face Wal-Mart in the years ahead

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Learning Objectives

  • Understand how Harrah’s 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 Harrah’s is using to help it gather more data, and help push service quality and marketing program success

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Introduction

  • Increasingly standardized corporate data, and access to rich, third-party datasets, all leveraged by cheap, fast computing and easier-to-use software, are enabling an age of data-driven, fact-based decision making
  • Business intelligence (BI): A term combining aspects of reporting, data exploration and ad hoc queries, and sophisticated data modeling and analysis
  • Analytics: A term describing the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions

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Introduction

  • Data leverage and data-driven decision making is important for obtaining competitive advantage
  • It can be a tough slog getting an organization to the point where it has a data asset that it can leverage
  • In many organizations data lies dormant, spread across inconsistent formats and incompatible systems, unable to be turned into anything of value
  • Many firms have been shocked at the amount of work and complexity required to pull together an infrastructure that empowers its managers

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Data, Information, and Knowledge

  • Data: Raw facts and figures
  • Information: Data presented in a context so that it can answer a question or support decision making
  • Knowledge: Insight derived from experience and expertise

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Understanding How is Data Organized: Key Terms and Technologies

  • Database: A single table or a collection of related tables
  • Database management systems (DBMS): Sometimes called “database software”; software for creating, maintaining, and manipulating data
  • Structured query language (SQL): A language used to create and manipulate databases
  • Database administrator (DBA): Job title focused on directing, performing, or overseeing activities associated with a database or set of databases
  • Includes database design, creation, implementation, maintenance, backup and recovery, policy setting and enforcement, and security

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Understanding How is Data Organized: Key Terms and Technologies

  • Key concepts that all managers should know:
  • A table or file refers to a list of data
  • A database is either a single table or a collection of related tables
  • A column or field defines the data that a table can hold
  • A row or record represents a single instance of whatever the table keeps track of
  • A key is the field used to relate tables in a database

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Understanding How is Data Organized: Key Terms and Technologies

  • Table or file: A list of data, arranged in columns (fields) and rows (records)
  • Column or field: A column in a database table. Columns represent each category of data contained in a record (e.g., first name, last name, ID number, data of birth)

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Understanding How is Data Organized: Key Terms and Technologies

  • Row or record: A row in a database table. Records represent a single instance of whatever the table keeps track of (e.g., student, faculty, course title)
  • Key: A field or combination of fields used to uniquely identify a record, and to relate separate tables in a database. Examples include social security number, customer account number, or student ID
  • Relational database: The most common standard for expressing databases, whereby tables (files) are related based on common keys

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Where Does Data Come From?

  • For organizations that sell directly to their customers, transaction processing systems represent a fountain of potentially insightful data
  • Transaction processing systems (TPS): A system that records a transaction (some form of business-related exchange), such as a cash register sale, ATM withdrawal, or product return
  • Transaction: Some kind of business exchange
  • The cash register is the primary source that feeds data to the TPS
  • TPS can generate a lot of bits, it’s sometimes tough to match this data with a specific customer

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Where Does Data Come From?

  • Enterprise software (CRM, SCM, and ERP)
  • Firms set up systems to gather additional data beyond conventional purchase transactions or Web site monitoring
  • CRM or customer relationship management systems are used to empower employees to track and record data at nearly every point of customer contact
  • Supply chain management (SCM) and enterprise resource planning (ERP) systems touch every aspect of the value chain

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Where Does Data Come From?

  • Surveys
  • Firms supplement operational data with additional input from surveys and focus groups
  • Direct surveys can tell you what your cash register can’t
  • Many CRM products have survey capabilities that allow for additional data gathering at all points of customer contact

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Where Does Data Come From?

  • External sources
  • If your firm has partners that sell products for you, then you’ll likely rely heavily on data collected by others
  • Data bought from sources available to all might not yield competitive advantage on its own, but it can provide key operational insight for increased efficiency and cost savings

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Data Rich, Information Poor

  • Many organizations are data rich but information poor
  • Factors holding back information advantage
  • Legacy system: Older information systems that are often incompatible with other systems, technologies, and ways of conducting business
  • Most transactional databases aren’t set up to be simultaneously accessed for reporting and analysis

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Data Warehouses and Data Marts

  • Data warehouses: A set of databases designed to support decision making in an organization
  • Structured for fast online queries and exploration
  • May aggregate enormous amounts of data from many different operational systems
  • Data marts: A database or databases focused on addressing the concerns of a specific problem (e.g., increasing customer retention, improving product quality) or business unit (e.g., marketing, engineering)

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Data Warehouses and Data Marts

  • Marts and warehouses may contain huge volumes of data
  • Large data warehouses can cost millions and take years to build
  • Large-scale data analytics projects should start with a clear vision with business-focused objectives

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Figure 11.2 - Information systems supporting operations (such as TPS) are typically separate, and “feed” information systems used for analytics (such as data warehouses and data marts)

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Data Warehouses and Data Marts

  • Once a firm has business goals and hoped-for payoffs clearly defined, it can address the broader issues needed to design, develop, deploy, and maintain its system:
  • Data relevance
  • Data sourcing
  • Data quantity and quality
  • Data hosting
  • Data governance

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The Business Intelligence Toolkit

  • Query and reporting tools
  • Canned reports: Reports that provide regular summaries of information in a predetermined format
  • Ad hoc reporting tools: Tools that put users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters
  • Dashboards: A heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics

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The Business Intelligence Toolkit

  • Online analytical processing (OLAP): A method of querying and reporting that 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: A special database used to store data in OLAP reporting

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Data Mining

  • Data mining is the process of using computers to identify hidden patterns in, and to build models from, large data sets
  • Key areas where businesses are leveraging data mining include:
  • Customer segmentation
  • Marketing and promotion targeting
  • Market basket analysis

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Data Mining

  • Collaborative filtering
  • Customer churn
  • Fraud detection
  • Financial modeling
  • Hiring and promotion
  • For data mining to work, two critical conditions need to be present:
  • The organization must have clean, consistent data
  • The events in that data should reflect current and future trends

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Data Mining

  • Problems associated with the use of bad data:
  • Wrong estimates from bad data leaves the firm overexposed to risk
  • Problem of historical consistency:
  • Computer-driven investment models are not very effective when the market does not behave as it has in the past
  • Over-engineer
  • Build a model with so many variables that the solution arrived at might only work on the subset of data you’ve used to create it
  • A pattern is uncovered but determining the best choice for a response is less clear

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Data Mining

  • A data mining and business analytics team should possesses three critical skills:
  • Information technology
  • Statistics
  • Business knowledge

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Artificial Intelligence

  • Data Mining has its roots in a branch of computer science known as artificial intelligence (or AI)
  • The goal of AI is create computer programs that are able to mimic or improve upon functions of the human brain

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Artificial Intelligence

  • Neural network: An AI system that examines data and hunts down and exposes patterns, in order to build models to exploit findings
  • Expert systems: AI systems that leverages rules or examples to perform a task in a way that mimics applied human expertise
  • Genetic algorithms: Model building techniques where computers examine many potential solutions to a problem, iteratively modifying various mathematical models, and comparing the mutated models to search for a best alternative

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Data Asset in Action: Technology and the Rise of Wal-Mart

  • Wal-Mart demonstrates how a physical product retailer can create and leverage a data asset to achieve world-class supply chain efficiencies targeted primarily at driving down costs
  • Wal-Mart is the largest retailer in the world
  • It’s key source of competitive advantage is scale

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A Data-Driven Value Chain

  • The Wal-Mart efficiency dance starts with a proprietary system called Retail Link
  • Retail Link records the sale and automatically triggers inventory reordering, scheduling, and delivery
  • Back-office scanners keep track of inventory as supplier shipments comes in
  • Wal-Mart has been a catalyst for technology adoption among its suppliers

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Data Mining Prowess

  • Wal-Mart mines its data to get its product mix right under all sorts of varying environmental conditions, protecting the firm from a retailer’s twin nightmares: too much inventory, or not enough
  • Data mining helps the firm tighten operational forecasts, helping it to predict things
  • Data drives the organization, with mined reports forming the basis of weekly sales meetings and executive strategy sessions

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Sharing Data, Keeping Secrets

  • Wal-Mart shares sales data with relevant suppliers
  • Wal-Mart has stopped sharing data with information brokers
  • Other aspects of the firm’s technology remain under wraps
  • Wal-Mart custom builds large portions of its information systems to keep competitors off its trail

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Challenges Abound

  • As a mature business, Wal-Mart faces a problem
  • It needs to find huge markets or dramatic cost savings in order to boost profits and continue to move its stock price higher
  • Criticisms against Wal-Mart
  • Accusations of sub par wages and remains a magnet for union activists
  • Poor labor conditions at some of the firm’s contract manufacturers
  • Wal-Mart demand prices so aggressively low that suppliers end up cannibalizing their own sales at other retailers

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Challenges Abound

  • The firm’s data warehouse wasn’t able to foretell the rise of Target and other up-market discounters
  • Another major challenge - Tesco methodically attempts to take its globally honed expertise to U.S. shores

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Data Asset in Action: Harrah’s Solid Gold CRM for the Service Sector

  • Harrah’s Entertainment provides an example of exceptional data asset leverage in the service sector, focusing on how this technology enables world-class service through customer relationship management
  • Harrah’s has leveraged its data-powered prowess to move from an also-ran chain of casinos to become the largest gaming company by revenue

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Collecting Data

  • Harrah’s collects customer data on everything you might do at their properties
  • The data is then used to track your preferences and to size up whether you’re the kind of customer that’s worth pursuing

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Collecting Data

  • The ace in Harrah’s data collection hole is its Total Rewards loyalty card system
  • The system is constantly being enhanced by an IT staff of seven hundred, with an annual budget in excess of one hundred million dollars
  • It is an opt-in loyalty program, but customers consider the incentives to be so good that the card is used by some 80 percent of Harrah’s patrons

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Who are the Most Valuable Customers?

  • With detailed historical data at hand Harrah’s can make fairly accurate projections of customer lifetime value (CLV)
  • Customer lifetime value (CLV): The present value of the likely future income stream generated by an individual purchaser
  • The firm tracks over ninety demographic segments, and each responds differently to different marketing approaches

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Who are the Most Valuable Customers?

  • Identifying segments and figuring out how to deal with each involves:
  • An iterative model of mining the data to identify patterns
  • Creating a hypothesis, then testing that hypothesis against a control group
  • Turning to analytics to statistically verify the outcome
  • From its data, Harrah’s realized that most of its profits came from:
  • Locals
  • Customers forty-five years and older

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Data Driven Service: Get Close (But Not Too Close) to Your Customers

  • Harrah’s identifies the high value customers and provides special attention to them
  • Customers could obtain reserved tables and special offers
  • It monitors even gamblers suffering unusual losses, and provide feel-good offers to them
  • The firm’s CRM effort monitors any customer behavior changes
  • Customers come back to Harrah’s because they feel that those casinos treat them better than the competition

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Data Driven Service: Get Close (But Not Too Close) to Your Customers

  • Harrah’s focus on service quality and customer satisfaction are embedded into its information systems and operational procedures
  • Employees are measured on metrics that include speed and friendliness and are compensated based on guest satisfaction ratings
  • The process effectively changed the corporate culture at Harrah’s from an every-property-for-itself mentality to a collaborative, customer-focused enterprise
  • Harrah’s is keenly sensitive to respecting consumer data
  • Some of its efforts to track customers have misfired

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Innovation

  • Harrah’s is constantly tinkering with new innovations that help it gather more data and help push service quality and marketing program success
  • Interactive bill boards, RFID-enabled poker chips and under-table RFID readers, incorporation of drink ordering to gaming machines, and touch-screen and sensor-equipped tabletop are examples of such innovations

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Strategy

  • The data is the major competitive advantage for Harrah’s
  • The data advantage creates intelligence for a high-quality and highly personal customer experience
  • The data gives the firm a service differentiation edge
  • The loyalty program represents a switching cost
  • The firm’s technology has been pretty tough for others to match and the firm holds many patents

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Challenges

  • Gaming is a discretionary spending item, and when the economy tanks, gambling is one of the first things consumers will cut
  • Harrah’s holds twenty-four billion dollars in debt from expansion projects and the buyout
  • The firm is now in a position many consider risky due to debt assumed as part of an overly optimistic buyout

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