Business Information Technology- Assignment
AJ-773
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Information Systems: A Manager’s Guide to Harnessing Technology V 3.0
By John Gallaugher
Chapter 13
The Data Asset: Databases, Business Intelligence, analytics, Big Data, and Competitive Advantage
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.
Data and Decision Making
Big data: The collections, storage, and analysis of extremely large, complex, and often unstructured data sets that can be used by organizations to generate insights that would otherwise be impossible to make.
The 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.
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
Enterprises that Have Benefited from Data Mastery
Walmart: Moved to the top of the Fortune 500 list.
Caesars Entertainment: Grew to be twice as profitable as rivals and rich enough to acquire them.
Capital One: Found valuable customers that competitors were ignoring.
Its ten-year financial performance was ten times greater than the S&P 500 average.
Learning Objectives
Understand the difference between data and information.
Know the key terms and technologies associated with data organization and management.
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
Database administrator (DBA): Job 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
Key terms associated with database systems
TABLE OR FILE
List of data, arranged in columns or fields and rows or records.
COLUMN OR FIELD
Column in a database table.
Represents each category of data contained in a record.
ROW OR RECORD
Row in a database table.
Represents a single instance of the data in the table.
Key terms associated with database systems
KEY
Field or fields used to uniquely identify a record, and to relate separate tables in a database, like a social security number.
RELATIONAL DATABASE
Most common standard for expressing databases.
Tables or files are related based on common keys.
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.
Transaction Processing Systems
Transaction: Any 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.
Record a transaction or some form of business-related exchange, such as a cash register sale, ATM withdrawal, or product return
Enterprise Software
Firms set up systems to gather additional data beyond conventional purchase transactions or Website 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.
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.
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.
Data Aggregators
One has to be aware of the digital tracking of individuals.
Made possible by the availability of personal information online.
Firms that collect and resell data
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.
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 .
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.
Recognize and understand technologies behind “Big Data,” how they differ from conventional data management approaches, and how they are currently being used for organizational benefit.
Data Warehouses and Data Marts
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.
Set of databases designed to support decision making in an organization
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.
Information Systems Supporting Operations
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
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
E-discovery
Firm should account for it in its archiving and data storage plans.
Data can be used later and therefore should be stored in order.
Identifying and retrieving relevant electronic information to support litigation efforts
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.
Business Intelligence Toolkit
CANNED REPORTS
Provide regular summaries of information in a predetermined format.
AD HOC REPORTING TOOLS
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.
DASHBOARDS
Heads-up display of critical indicators that allows managers to get a graphical glance at key performance metrics.
ONLINE ANALYTICAL PROCESSING (OLAP)
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
Data mining
Models based on:
Customer segmentation and market basket analysis.
Marketing and promotion targeting.
Collaborative filtering and customer churn.
Fraud detection, financial modeling, hiring and promotion.
Prerequisites
Organization must have clean, consistent data.
Events in that data should reflect trends.
Using computers to identify hidden patterns in large data sets and to build models from this data
Problems in Data Mining
Using bad data can give wrong estimates, thus exposing the firm to risk.
When the market does not behave as it has in the past, computer-driven investment models are not effective.
Overengineering: Building a model with so many variables that the solution arrived at might only work on the subset of data used to create it.
Skills for data mining
INFORMATION TECHNOLOGY
STATISTICS
BUSINESS KNOWLEDGE
ARTIFICIAL INTELLIGENCE
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.
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.
Computer software that seeks to reproduce or mimic human thought, decision making, or brain functions
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.
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 in.
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.
Sharing data and keeping secrets
Walmart shares sales data only 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.
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 draw 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.
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 success.
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 the largest gaming company by revenue.
Collecting Data
Caesars’ collects customer data on all activities on 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.
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 aged 45 years and older.
Data-driven service
Identifies high-value customers and gives them special attention.
Customers can 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 because they feel that the company treats them well.
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 an every property-for-itself mentality to a collaborative, customer-focused enterprise.
Innovation and strategy
| INNOVATION | STRATEGY |
| Has new innovations that help it gather more data Push service quality and marketing program success Interactive billboards RFID-enabled poker chips and under-table RFID readers Incorporation of drink ordering to gaming machines | Data advantage creates intelligence for a high-quality and highly personal customer experience Data gives the firm a service differentiation edge Loyalty program represents a switching cost Firm’s technology is unique and holds many patents |
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.
