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Chapter 6- Data: Business Intelligence

Value of information
The ability to understand, digest, analyze, and filter information is key to growth and success for any professional in any industry
Information granularity
the extent of detail within the information (fine and detailed or coarse and abstract)
Levels, Formats, and Granularities of Organizational Information
Levels, Formats, and Granularities of Organizational Information
The Four Primary Traits of the Value of Information
The Four Primary Traits of the Value of Information
two primary types of information
transactional and analytical
Transactional information
encompasses all of the information contained within a single business process or unit of work, and its primary purpose is to support daily operational tasks

(Organizations need to capture and store transactional information to perform operational tasks and repetitive decisions such as analyzing daily sales reports and production schedules to determine how much inventory to carry)

Analytical information
encompasses all organizational information, and its primary purpose is to support the performance of managerial analysis tasks

(Analytical information is useful when making important decisions such as whether the organization should build a new manufacturing plant or hire additional sales personnel. Analytical information makes it possible to do many things that previously were difficult to accomplish, such as spot business trends, prevent diseases, and fight crime; identify many unusual trends)

Transactional versus Analytical Information
Transactional versus Analytical Information
Real-time information
immediate, up-to-date information
Real-time systems
provide real-time information in response to requests. Many organizations use real-time systems to uncover key corporate transactional information
The growing demand for real-time information
stems from organizations’ need to make faster and more effective decisions, keep smaller inventories, operate more efficiently, and track performance more carefully
biggest pitfalls associated with real-time information
continual change
Information inconsistency
occurs when the same data element has different values
Information integrity issues
occur when a system produces incorrect, inconsistent, or duplicate data

(can cause managers to consider the system reports invalid and will make decisions based on other sources)

Five Common Characteristics of High-Quality Information
Five Common Characteristics of High-Quality Information
Example of Low-Quality Information
Example of Low-Quality Information
Completeness. The customer’s first name is missing.
Another issue with completeness. The street address contains only a number and not a street name.
Consistency. There may be a duplication of information since there is a slight difference between the two customers in the spelling of the last name. Similar street addresses and phone numbers make this likely.
Accuracy. This may be inaccurate information because the customer’s phone and fax numbers are the same. Some customers might have the same number for phone and fax, but the fact that the customer also has this number in the email address field is suspicious.
Another issue with accuracy. There is inaccurate information because a phone number is located in the email address field.
Another issue with completeness. The information is incomplete because there is not a valid area code for the phone and fax numbers.
The four primary reasons for low-quality information
1) Online customers intentionally enter inaccurate information to protect their privacy.
2) Different systems have different information entry standards and formats.
3) Data-entry personnel enter abbreviated information to save time or erroneous information by accident.
4) Third-party and external information contains inconsistencies, inaccuracies, and errors.
Costs of Using Low-Quality Information
1) Inability to track customers accurately.
2) Difficulty identifying the organization’s most valuable customers.
3) Inability to identify selling opportunities.
4) Lost revenue opportunities from marketing to nonexistent customers.
5) The cost of sending undeliverable mail.
6) Difficulty tracking revenue because of inaccurate invoices.
7) Inability to build strong relationships with customers.
Data governance
refers to the overall management of the availability, usability, integrity, and security of company data
Master data management (MDM)
practice of gathering data and ensuring that it is uniform, accurate, consistent, and complete, including such entities as customers, suppliers, products, sales, employees, and other critical entities that are commonly integrated across organizational systems
maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses) (store information)

(core component of any system, regardless of size, is a database and a database management system)

database management system (DBMS)
creates, reads, updates, and deletes data in a database while controlling access and security. Managers send requests to the DBMS, and the DBMS performs the actual manipulation of the data in the database
query-by-example (QBE)
tool that helps users graphically design the answer to a question against a database
Two primary tools are available for retrieving information from a DBMS
query-by-example (QBE) tool and a structured query language (SQL)
structured query language (SQL)
that asks users to write lines of code to answer questions against a database
Relationship of Database, DBMS, and User
Relationship of Database, DBMS, and User
data element (or data field)
the smallest or basic unit of information (can include a customer’s name, address, email, discount rate, preferred shipping method, product name, quantity ordered, and so on)
Data models
logical data structures that detail the relationships among data elements by using graphics or pictures
provides details about data.

F(an image could include its size, resolution, and date created. Metadata about a text document could contain document length, data created, author’s name, and summary)

data dictionary
compiles all of the metadata about the data elements in the data model
DBMS use three primary data models for organizing information
hierarchical, network, and the relational database, the most prevalent
relational database model
stores information in the form of logically related two-dimensional tables
A relational database management system
allows users to create, read, update, and delete data in a relational database. Although the hierarchical and network models are important, this text focuses only on the relational database model
primary concepts of the relational database model
primary concepts of the relational database model
entities, attributes, keys, and relationships
entity (also referred to as a table)
stores information about a person, place, thing, transaction, or event (ex. TRACKS, RECORDINGS, MUSICIANS, and CATEGORIES) -columns, attributes, fields-> (supplier, inventory, materials, distribution)
Attributes (also called columns or fields)
the data elements associated with an entity (the entity TRACKS are TrackNumber, TrackTitle, TrackLength, and RecordingID. Attributes for the entity MUSICIANS are MusicianID, MusicianName, MusicianPhoto, and MusicianNotes)
a collection of related data elements (in the MUSICIANS table, these include “3, Lady Gaga, gag.tiff, Do not bring young kids to live shows”)
primary key
a field (or group of fields) that uniquely identifies a given record in a table.

In the table RECORDINGS, the primary key is the field RecordingID that uniquely identifies each record in the table.

Primary keys are a critical piece of a relational database because they provide a way of distinguishing each record in a table; for instance, imagine you need to find information on a customer named Steve Smith. Simply searching the customer name would not be an ideal way to find the information because there might be 20 customers with the name Steve Smith

foreign key
a primary key of one table that appears as an attribute in another table and acts to provide a logical relationship between the two tables
Potential Relational Database for company
Potential Relational Database for company
Business Advantages of a Relational Database
Business Advantages of a Relational Database
Business Advantages of a Relational Database
1) Increased Flexibility
distinction between logical and physical views is important in understanding flexible database user views
physical view of information
deals with the physical storage of information on a storage device
logical view of information
focuses on how individual users logically access information to meet their own particular business needs
Business Advantages of a Relational Database
2) Increased Scalability and Performance
scalable to handle the massive volumes of information, the large numbers of users expected for the launch of the website, and need to perform quickly under heavy use
Information redundancy

Business Advantages of a Relational Database
3) Reduced Information Redundancy

the duplication of data, or the storage of the same data in multiple places

(can cause storage issues along with data integrity issues, making it difficult to determine which values are the most current or most accurate. Employees become confused and frustrated when faced with incorrect information causing disruptions to business processes and procedures. One primary goal of a database is to eliminate information redundancy by recording each piece of information in only one place in the database)

Business Advantages of a Relational Database
4) Increased Information Integrity (Quality)
database design needs to consider integrity constraints
Information integrity
measure of the quality of information
Integrity constraints
rules that help ensure the quality of information
two types of integrity constraints
1) relational
2) business critical
Relational integrity constraints
rules that enforce basic and fundamental information-based constraints.

For example, a relational integrity constraint would not allow someone to create an order for a nonexistent customer, provide a markup percentage that was negative, or order zero pounds of raw materials from a supplier

business rule
defines how a company performs certain aspects of its business and typically results in either a yes/no or true/false answer

Stating that merchandise returns are allowed within 10 days of purchase is an example of a business rule

Business-critical integrity
constraints enforce business rules vital to an organization’s success and often require more insight and knowledge than relational integrity constraints

no product returns are accepted after 15 days past delivery (makes sense because of spoilage of produce)

Business Advantages of a Relational Database
5) Increased Information Security
Managers must protect information, like any asset, from unauthorized users or misuse

Security risks are increasing as more and more databases and DBMS systems are moving to data centers run in the cloud

content creator
the person responsible for creating the original website content
content editor
the person responsible for updating and maintaining website content
Static information
includes fixed data incapable of change in the event of a user action
Dynamic information
includes data that change based on user actions. For example, static websites supply only information that will not change until the content editor changes the information. Dynamic information changes when a user requests information. A dynamic website changes information based on user requests such as movie ticket availability, airline prices, or restaurant reservations
dynamic catalog
an area of a website that stores information about products in a database (dynamic website information)
data-driven website
an interactive website kept constantly updated and relevant to the needs of its customers using a database

(especially useful when a firm needs to offer large amounts of information, products, or services. Can help limit the amount of information displayed to customers based on unique search requirements)

advantages to using the web to access company databases
1) web browsers are much easier to use than directly accessing the database by using a custom-query tool
2) the web interface requires few or no changes to the database model
3) it costs less to add a web interface in front of a DBMS than to redesign and rebuild the system to support changes.

Additional data-driven website advantages include:

-Easy to manage content: Website owners can make changes without relying on MIS professionals; users can update a data-driven website with little or no training.

-Easy to store large amounts of data: Data-driven websites can keep large volumes of information organized. Website owners can use templates to implement changes for layouts, navigation, or website structure. This improves website reliability, scalability, and performance.

-Easy to eliminate human errors: Data-driven websites trap data-entry errors, eliminating inconsistencies while ensuring that all information is entered correctly.

Zappos.com—A Data-Driven Website
Zappos.com—A Data-Driven Website
BI in a Data-Driven Website
BI in a Data-Driven Website
business intelligence examples
Airlines: Analyze popular vacation locations with current flight listings.
Banking: Understand customer credit card usage and nonpayment rates.
Health care: Compare the demographics of patients with critical illnesses.
Insurance: Predict claim amounts and medical coverage costs.
Law enforcement: Track crime patterns, locations, and criminal behavior.
Marketing: Analyze customer demographics.
Retail: Predict sales, inventory levels, and distribution.
Technology: Predict hardware failures.
How BI Can Answer Tough Customer Questions
How BI Can Answer Tough Customer Questions
How BI Can Answer Tough Customer Questions 2
Where has the business been? Historical perspective offers important variables for determining trends and patterns.
Where is the business now? Looking at the current business situation allows managers to take effective action to solve issues before they grow out of control.
Where is the business going? Setting strategic direction is critical for planning and creating solid business strategies
Reasons Business Analysis Is Difficult from Operational Databases
Reasons Business Analysis Is Difficult from Operational Databases
data warehouse
a logical collection of information, gathered from many operational databases, that supports business analysis activities and decision-making tasks

primary purpose is to combine information, more specifically, strategic information, throughout an organization into a single repository in such a way that the people who need that information can make decisions and undertake business analysis (collect information from multiple systems in a common location that uses a universal querying tool)

data warehouse enables business users, typically managers, to be more effective in many ways, including:
Developing customer profiles.
Identifying new-product opportunities.
Improving business operations.
Identifying financial issues.
Analyzing trends.
Understanding competitors.
Understanding product performance
Three Core Concepts of Data Warehousing
Three Core Concepts of Data Warehousing
Extraction, transformation, and loading (ETL)
a process that extracts information from internal and external databases, transforms it using a common set of enterprise definitions, and loads it into a data warehouse. The data warehouse then sends portions (or subsets) of the information to data marts
data mart
(1 of 3 core concepts of data warehousing)
data mart contains a subset of data warehouse information.

To distinguish between data warehouses and data marts, think of data warehouses as having a more organizational focus and data marts as having a functional focus

Data Warehouse Model
Data Warehouse Model
information cube
the common term for the representation of multidimensional information
A Cube of Information for Performing a Multidimensional Analysis on Three Stores for Five Products and Four Promotions
A Cube of Information for Performing a Multidimensional Analysis on Three Stores for Five Products and Four Promotions
Dirty data
erroneous or flawed data (complete removal of dirty data from a source is impractical or virtually impossible)

dirty data is a business problem, not an MIS problem

Dirty Data Problems
Dirty Data Problems
Information cleansing or scrubbing
(2 of 3 core concepts of data warehousing)
a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
Specialized software tools
exist that use sophisticated procedures to analyze, standardize, correct, match, and consolidate data warehouse information
Contact Information in Operational Systems
Contact Information in Operational Systems
Standardizing a Customer Name in Operational Systems
Standardizing a Customer Name in Operational Systems
Information Cleansing Activities
Information Cleansing Activities
complete but inaccurate information
2/31/10 is an example of complete but inaccurate information (February 31 does not exist)
data quality audits
determine the accuracy and completeness of its data.

Most organizations determine a percentage of accuracy and completeness high enough to make good decisions at a reasonable cost, such as 85 percent accurate and 65 percent complete.

The Cost of Accurate and Complete Information
The Cost of Accurate and Complete Information
three methods organizations are using to dissect, analyze, and understand organizational data
three methods organizations are using to dissect, analyze, and understand organizational data
Data mining
the process of analyzing data to extract information not offered by the raw data alone

(can also begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down) or the reverse (drilling up))

Data-mining tools
Data-mining tools use a variety of techniques to find patterns and relationships in large volumes of information that predict future behavior and guide decision making.

help users uncover business intelligence in their data

Data Mining Analysis Methods
Data Mining Analysis Methods
-Analyzing customer buying patterns to predict future marketing and promotion campaigns.

-Building budgets and other financial information.

-Detecting fraud by identifying deceptive spending patterns.

-Finding the best customers who spend the most money.

-Keeping customers from leaving or migrating to competitors.

-Promoting and hiring employees to ensure success for both the company and the individual.

Data-Mining Techniques
Data-Mining Techniques
Structured data
has a defined length, type, and format and includes numbers, dates, or strings such as Customer Address.

(typically stored in a traditional system such as a relational database or spreadsheet and accounts for about 20 percent of the data that surrounds us)

The sources of structured data include:
Machine-generated data & Human-generated data (structured)
Machine-generated data
created by a machine without human intervention

Machine-generated structured data includes sensor data, point-of-sale data, and web log (blog) data

Human-generated data
Human-generated data is data that humans, in interaction with computers, generate

Human-generated structured data includes input data, click-stream data, or gaming data

Unstructured data
is not defined, does not follow a specified format, and is typically free-form text such as emails, Twitter tweets, and text messages

(Unstructured data accounts for about 80 percent of the data that surrounds us)

The sources of unstructured data include:
Machine-generated unstructured data & Human-generated unstructured data
Machine-generated unstructured data
satellite images, scientific atmosphere data, and radar data
Human-generated unstructured data
text messages, social media data, and emails
Big data
a collection of large, complex data sets, including structured and unstructured data, which cannot be analyzed using traditional database methods and tools
The four common characteristics of big data
The four common characteristics of big data
Big data requires sophisticated tools to analyze all the unstructured information from millions of customers, devices, and machine interactions. Big data are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science
Distributed computing
processes and manages algorithms across many machines in a computing environment
Advanced analytics
focuses on forecasting future trends and producing insights using sophisticated quantitative methods, including statistics, descriptive and predictive data mining, simulation, and optimization

(uses data patterns to make forward-looking predictions to explain to the organization where it is headed)

data scientist
extracts knowledge from data by performing statistical analysis, data mining, and advanced analytics on big data to identify trends, market changes, and other relevant information
Big Data Advanced Analytical Techniques
Big Data Advanced Analytical Techniques
present the results of data analysis, displaying the patterns, relationships, and trends in a graphical format

(exciting and quickly convey a story users can understand without having to analyze numbers, tables, and boring charts)

Analysis paralysis
occurs when the user goes into an emotional state of over-analysis (or over-thinking) a situation so that a decision or action is never taken, in effect paralyzing the outcome

In the time of big data, analysis paralysis is a growing problem. One solution is to use data visualizations to help people make decisions faster

Data visualization
technologies that allow users to see or visualize data to transform information into a business perspective

Data visualization is a powerful way to simplify complex data sets by placing data in a format that is easily grasped and understood far quicker than the raw data alone

Data visualization tools
move beyond Excel graphs and charts into sophisticated analysis techniques such as controls, instruments, maps, time-series graphs, and more

Data visualization tools can help uncover correlations and trends in data that would otherwise go unrecognized

Business intelligence dashboards
track corporate metrics such as critical success factors and key performance indicators and include advanced capabilities such as interactive controls, allowing users to manipulate data for analysis.

The majority of business intelligence software vendors offer a number of data visualization tools and business intelligence dashboards

data artist
a business analytics specialist who uses visual tools to help people understand complex data
Big data is one of the most promising technology trends occurring today. Of course, notable companies such as Facebook, Google, and Netflix are gaining the most business insights from big data currently, but many smaller markets are entering the scene, including retail, insurance, and health care.
Info 2
Over the next decade, as big data starts to improve your everyday life by providing insights into your social relationships, habits, and careers, you can expect to see the need for data scientists and data artists dramatically increase.
Time-series information
timestamped information collected at a particular frequency
association detection
reveals the relationship between variables along with the nature and frequency of the relationships
transactional vs. analytical information
transactional vs. analytical information
market basket analysis
analyzes such items as websites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services

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