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Chapter 3
Data Management,
Big Data Analytics, and
Records Management
Prepared by Dr. Derek Sedlack, South University
Learning Objectives
Data
Warehouse
and Big Data
Analytics
Database
Management
Systems
Electronic
Records
Management
Data and
Text Mining
Business
Intelligence
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Databases
– Collections of data sets or records stored in a
systematic way.
– Stores data generated by business apps, sensors,
operations, and transaction-processing systems
(TPS).
– The data in databases are extremely volatile.
– Medium and large enterprises typically have many
databases of various types.
Volatile data changes frequently
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Data Warehouses
– Integrate data from multiple databases and data
silos, and organize them for complex analysis,
knowledge discovery, and to support decision
making.
– May require formatting processing and/or
standardization.
– Loaded at specific times making them non-volatile
and ready for analysis.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Data Marts
– Small-scale data warehouses that support a single
function or one department.
– Enterprises that cannot afford to invest in data
warehousing may start with one or more data marts.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Business intelligence (BI)
– Tools and techniques that process data and conduct
statistical analysis for insight and discovery.
– Used to discover meaningful relationships in the
data, keep informed of real time, detect trends, and
identify opportunities and risks.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Database Management System (DBMS)
– Integrate with data collection systems such as TPS
and business applications.
– Stores data in an organized way.
– Provides facilities for accessing and managing data.
– Standard database model adopted by most
enterprises.
– Store data in tables consisting of columns and rows,
similar to the format of a spreadsheet.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Relational Management System (DBMS)
– Provides access to data using a declarative
language.
• Declarative Language
– Simplifies data access by requiring that users only
specify what data they want to access without
defining how they will be achieved.
– Structured Query Language (SQL) is an example of a
declarative language:
SELECT column_name(s)
FROM table_name
WHERE condition
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• DBMS Functions
– Data filtering and profiling
– Data integrity and maintenance
– Data synchronization
– Data security
– Data access
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
Online Transaction Processing and Online Analytics
Processing
• Online Transaction Processing (OLTP)
– Designed to manage transaction data, which are
volatile & break down complex information into
simpler data tables to strike a balance between
transaction-processing efficiency and query
efficiency.
– Cannot be optimized for data mining
• Online Analytics Processing (OLAP)
– A means of organizing large business databases.
– Divided into one or more cubes that fit the way
business is conducted.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• DBMSs (mid-2014)
– Oracle’s MySQL
– Microsoft’s SQL Server
– PostgreSQL
– IBM’s DB2
– Teradata Database.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Trend Toward NoSQL Systems
– Higher performance
– Easy distribution of data on different nodes
• enables scalability and fault tolerance
– Greater flexibility
– Simpler administration
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
Centralized and Distributed Database Architecture
• Centralized Database Architecture
– Better control of data quality.
– Better IT security.
• Distributed Database Architecture
– Allow both local and remote access.
– Use client/server architecture to process requests.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
Garbage In, Garbage Out
• Dirty Data
– Lacks integrity/validation and reduces user trust.
– Incomplete, out of context, outdated, inaccurate,
inaccessible, or overwhelming.
Cost of Poor
Quality Data
Lost
Business
Cost to
Prevent
Errors
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Cost to
Correct
Errors
Chapter 3
Database Management Systems
• Principle of Diminishing Data Value
– The value of data diminishes as they age.
– Blind spots (lack of data availability) of 30 days or
longer inhibit peak performance.
– Global financial services institutions rely on nearreal-time data for peak performance.
• Principle of 90/90 Data Use
– As high as 90 percent, is seldom accessed after 90
days (except for auditing purposes).
– Roughly 90 percent of data lose most of their value
after 3 months.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
• Principle of data in context
– The capability to capture, process, format, and
distribute data in near real time or faster requires a
huge investment in data architecture.
– The investment can be justified on the principle that
data must be integrated, processed, analyzed, and
formatted into “actionable information.”
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
Data Life Cycle
Figure 3.11 Data life cycle.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
Figure 3.12 An enterprise has transactional, master, and analytical data.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Database Management Systems
1. Describe a database and a database management
system (DBMS).
2. Explain what an online transaction-processing (OLAP)
system does.
3. Why are data in databases volatile?
4. Explain what processes DBMSs are optimized to
perform.
5. What are the business costs or risks of poor data
quality?
6. Describe the data life cycle.
7. What is the function of master data management
(MDM)?
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Learning Objectives
Data
Warehouse
and Big Data
Analytics
Database
Management
Systems
Electronic
Records
Management
Data and
Text Mining
Business
Intelligence
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Market share
– Percentage of total sales in a market captured by a
brand, product, or company.
• Operating Margin
– A measure of the percent of a company’s revenue
left over after paying variable costs: wages, raw
materials, etc.
– Increased margins mean earning more per dollar of
sales.
– The higher the operating margin, the better.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
TORTURE DATA LONG ENOUGH AND IT WILL CONFESS . . .
BUT MAY NOT TELL THE TRUTH
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Human Expertise and Judgment Required
– Data are worthless if you cannot analyze, interpret,
understand, and apply the results in context.
– Data need to be prepared for analysis.
– Dirty data degrade the value of analytics.
– Data must be put into meaningful context.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Enterprise data warehouses (EDW)
– Data warehouses that pull together data from
disparate sources and databases across an entire.
– Warehouses are the primary source of cleansed
data for analysis, reporting, and Business
Intelligence (BI).
– Their high costs can be subsidized by using Data
marts.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Procedures to Prepare EDW Data for Analytics
– Extract from designated databases.
– Transform by standardizing formats, cleaning the
data, integration.
– Loading into a data warehouse.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Active Data Warehouse (ADW)
– Real-time data warehousing and analytics.
– Transform by standardizing formats, cleaning the
data, integration.
• They Provide
– Interaction with a customer to provide superior
customer service.
– Respond to business events in near real time.
– Share up-to-date status data among merchants,
vendors, customers, and associates.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Supporting Actions as well as Decisions
– Marketing and Sales
– Pricing and Contracts
– Forecasting
– Sales
– Financial
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Really Big Data
– Low-cost sensors collect data in real time in all
types of physical things (machine-generated sensor
data):
• Regulate temperature and climate
• Detect air particles for contamination
• Machinery conditions/failures
• Engine wear/maintenance
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
Figure 3.16 Machine generated data from physical objects are
becoming a much larger portion of big data and analytics..
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
• Hadoop and MapReduce
– Hadoop is an Apache processing platform that
places no conditions on the processed data
structure.
– MapReduce provides a reliable, fault-tolerant
software framework to write applications easily that
process vast amounts of data (multi-terabyte datasets) in-parallel on large clusters (thousands of
nodes) of commodity hardware.
• Map stage: breaks up huge data into subsets
• Reduce stage: recombines partial results
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data Warehouse and Big Data
Analytics
1. Why are human expertise and judgment important to data
analytics? Give an example.
2. What is the relationship between data quality and the value
of analytics?
3. Why do data need to be put into a meaningful context?
4. What are the differences between databases and data
warehouses?
5. Explain ETL and CDC.
6. What is an advantage of an active data warehouse (ADW)?
7. Why might a company invest in a data mart?
8. How can manufacturers and health care benefit from data
analytics?
9. Explain how Hadoop implements MapReduce in two stages.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Learning Objectives
Data
Warehouse
and Big Data
Analytics
Database
Management
Systems
Electronic
Records
Management
Data and
Text Mining
Business
Intelligence
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data and Text Mining
• Creating Business Value
– Business Analytics: the entire function of applying
technologies, algorithms, human expertise, and
judgment.
– Data Mining: software that enables users to analyze data
from various dimensions or angles, categorize them, and
find correlative patterns among fields in the data
warehouse.
– Text Mining: broad category involving interpreted words
and concepts in context.
– Sentimental Analysis: trying to understand consumer
intent.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data and Text Mining
• Text Analytics (Mining) Procedure
– Exploration
• Simple word counts
• Topics consolidation
– Preprocessing
• Standardization
• May be 80% of processing time
• Grammar and spell checking
– Categorizing and Modelling
• Create business rules and train models for
accuracy and precision
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data and Text Mining
• Text Analytics Procedure
– Exploration
• Simple word counts
• Topics consolidation
– Preprocessing
• Standardization
• May be 80% of processing time
• Grammar and spell checking
– Categorizing and Modelling
• Create business rules and train models for
accuracy and precision
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Data and Text Mining
1. Describe data mining.
2. How does data mining generate or provide value? Give
an example.
3. What is text mining?
4. Explain the text mining procedure.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Learning Objectives
Data
Warehouse
and Big Data
Analytics
Database
Management
Systems
Electronic
Records
Management
Data and
Text Mining
Business
Intelligence
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Business Intelligence
• Key to competitive advantage
– Across industries in all size enterprises
– Used in operational management, business
process, and decision making
– Provides moment of value to decision makers
– Unites data, technology, analytics, & human
knowledge to optimize decisions
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Business Intelligence
• Challenges
– Data Selection & Quality
– Alignment with Business Strategy and BI Strategy
• Alignment
– Clearly articulates business strategy
– Deconstructs business strategy into targets
– Identifies PKIs
– Prioritizes PKIs
– Creates a plan based on priorities
– Transform based on strategic results and changes
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Business Intelligence
Smart Devices
Everywhere
have created demand for
effortless 24/7 access to
insights.
Advanced BI and
Analytics
help to ask questions that
were previously unknown
and unanswerable.
Data is Big
Business
when they provide insight
that supports decisions and
action.
Cloud Enabled BI and
Analytics
are providing low-cost and
flexible solutions.
Figure 3.20 Four factors contributing to increased use of BI.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Business Intelligence
• BI Architecture and Analytics
– Advances in response to big data and end-user
performance demands.
– Hosted on public or private clouds.
– Limits IT staff and controls costs
– May slow response time, add security and backup
risks
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Business Intelligence
1. How has BI improved performance management at
Quicken Loans?
2. What are the business benefits of BI?
3. What are two data-related challenges that must be
resolved for BI to produce meaningful insight?
4. What are the steps in a BI governance program?
5. What is a business-driven development approach?
6. What does it mean to drill down, and why is it
important?
7. What four factors are contributing to increased use of
BI?
8. How did BI help CarMax achieve record-setting
revenue growth?
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Learning Objectives
Data
Warehouse
and Big Data
Analytics
Database
Management
Systems
Electronic
Records
Management
Data and
Text Mining
Business
Intelligence
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Electronic Records Management
• Business Records
– Documentation of a business event, action,
decision, or transaction.
• Electronic Records Management (EMR)
– Workflow software, authoring tools, scanners, and
databases that manage and archive electronic
documents and image paper documents.
– Index and store documents according to company
policy or legal compliance.
– Success depends on partnership of key players.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Electronic Records Management
• Best Practices
– Effective systems capture all business data.
– Input from online forms, bar codes, sensors,
websites, social sites, copiers, e-mails, and more.
• Industry Standards
– Association for Information and Image Management
(AIIM; www.aiim.org)
– National Archives and Records Administration
(NARA; www.archives.gov)
– ARMA International (formerly the Association of
Records Managers and Administrators;
www.arma.org)
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Electronic Records Management
• Primary Benefits
– Access and use the content contained in
documents.
– Cut labor costs by automating business processes.
– Reduce time and effort to locate required
information for decision making.
– Improve content security, thereby reducing
intellectual property theft risks.
– Minimizes content printing, storing, and searching
costs.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Electronic Records Management
• DISASTER RECOVERY, BUSINESS CONTINUITY, AND
COMPLIANCE
1. Does the software meet the organization’s needs? For
example, can the DMS be installed on the existing
network? Can it be purchased as a service?
2. Is the software easy to use and accessible from Web
browsers, office applications, and e-mail applications?
If not, people will not use it.
3. Does the software have lightweight, modern Web and
graphical user interfaces that effectively support
remote users?
4. Before selecting a vendor, it is important to examine
workflows and how data, documents, and
communications flow throughout the company.
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.
Chapter 3
Electronic Records Management
1. What are business records?
2. Why is ERM a strategic issue rather than simply an IT
issue?
3. Why might a company have a legal duty to retain
records? Give an example.
4. Why is creating backups an insufficient way to manage
an organization’s documents?
5. What are the benefits of ERM?
Copyright © 2015 John Wiley & Sons, Inc. All rights reserved.

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