Data Management, Analytics, and Business Intelligence

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IT for Management: On-Demand Strategies for Performance, Growth, and Sustainability

Eleventh Edition

Turban, Pollard, Wood

Chapter 3

Data Management, Business Intelligence, and Data Analytics

Learning Objectives (1 of 5)

Database Technologies: 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.

Database Technologies: 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

Database Technologies: 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

Database Technologies: BI

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

Database Management Systems (DBMS)

Integrate with data collection systems such as TPS and business applications

Organized way to store, access, and manage data

Stores data in tables consisting of columns and rows, similar to the format of a spreadsheet

Standard database model adopted by most enterprises

Functions include:

Data filtering and profiling

Data integrity and maintenance

Data synchronization

Data security

Data access

Database Technologies: SQL

Relational Database Management Systems (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 declarative language:

SELECT column_name(s)

FROM table_name

WHERE condition

OLTP and OLAP 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 tarbles and 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

Database Technologies: NOSQL

Trend toward NoSQL Systems

Higher performance

Easy distribution of data on different nodes

Enables scalability and fault tolerance

Greater flexibility

Simpler administration

Popular DBMS

DBMSs (mid-2016)

Oracle’s 12C Database

Microsoft’s SQL Server

IBM’s DB2

SAP Sybase Ase

PostgreSQL

Data Management and Database Technologies

Describe a database and database management system (DBMS).

Explain what an online transaction-processing (OLAP) system does.

Why are data in databases volatile?

Describe the functions of a DBMS.

Describe the purpose and benefits of data management.

What is a relational database management system?

Suggested Responses:

1. A database is a collection of data sets or records stored in a systematic way. A database stores data generated by business apps, sensors, and transaction processing systems. Databases can provide access to all of the organization’s data collected for a particular function or enterprise-wide, alleviating many of the problems associated with data file environments. Central storage of data in a database reduces data redundancy, data isolation, and data inconsistency and allows for data to be shared among users of the data. In addition, security and data integrity are easier to control, and applications are independent of the data they process. There are two basic types of databases: centralized and distributed.

A database management system (DBMS) is software used to manage the additions, updates, and deletions of data as transactions occur; and support data queries and reporting. DBMSs integrate with data collection systems such as TPS and business applications; store the data in an organized way; and provide facilities for accessing and managing that data.

2. OLAP is a term used to describe the analysis of complex data from the data warehouse.

3. Data in databases are volatile because they can be updated millions of times every second, especially if they are transaction processing systems (TPS).

4. Data filtering and profiling: Inspecting the data for errors, inconsistencies, redundancies, and incomplete information.

Data integrity and maintenance: Correcting, standardizing, and verifying the consistency and integrity of the data.

Data synchronization: Integrating, matching, or linking data from disparate sources.

Data security: Checking and controlling data integrity over time.

Data access: Providing authorized access to data in both planned and ad hoc ways within acceptable time

5. The main benefits of data management include greater compliance, higher security, less legal liability, improved sales and marketing strategies, better product classification and improved data governance to reduce risk.

6. Relational databases store data in tables consisting of columns and rows, similar to the format of a spreadsheet.

Learning Objectives (2 of 5)

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

Dirty Data

Garbage In, Garbage Out

Dirty Data

Lacks integrity/validation and reduces user trust

Incomplete, out of context, outdated, inaccurate, inaccessible, or overwhelming

Characteristics of Poor Quality or Dirty Data

Characteristic	Description
Incomplete	Missing data
Outdated or Invalid	Too old to be valid or useful
Incorrect	Too many errors
Duplicated or in conflict	Too many copies or versions of the same data—and the versions are inconsistent or in conflict with each other
Non-standardized	Data are stored in incompatible formats—and cannot be compared or summarized
Unusable	Data are not in context to be understood or interpreted correctly at the time of access

Data Life Cycle and Data Principles (1 of 2)

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 near-real-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

Data Life Cycle and Data Principles (2 of 2)

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 in “actionable information”

Figure 3.11 Data life cycle

Figure 3.12 An enterprise has transactional, master, and analytical data.

Centralized and Distributed Database Architectures

Describe the data life cycle.

What is the function of master data management (MDM)?

What are the consequences of not cleaning “dirty data”?

Describe the differences between centralized and distributed databases.

Discuss how data ownership and organizational politics affect the quality of an organization’s data.

Suggested Answers:

1. The data life cycle is a model that illustrates the way data travel through an organization. The data life cycle begins with storage in a database, to being loaded into a data warehouse for analysis, then reported to knowledge workers or used in business apps.

2. Master data management (MDM) is a process whereby companies integrate data from various sources or enterprise applications to provide a more complete or unified view of an entity (customer, product, etc.) Although vendors may claim that their MDM solution creates “a single version of the truth,” this claim probably is not true. In reality, MDM cannot create a single unified version of the data because constructing a completely unified view of all master data simply is not possible. Realistically, MDM consolidates data from various data sources into a master reference file, which then feeds data back to the applications, thereby creating accurate and consistent data across the enterprise.

3. Bad data are costing U.S. businesses hundreds of billions of dollars a year and affecting their ability to ride out the tough economic climate. Incorrect and outdated values, missing data, and inconsistent data formats can cause lost customers, sales, and revenue; misallocation of resources; and flawed pricing strategies.

4. A centralized database stores all data in a single central compute such as a mainframe or server.

A distributed database stores portions of the database on multiple computers within a network.

5. Data ownership problems exist when there are no policies defining responsibility and accountability for managing data. Inconsistent data formats of

various departments create an additional set of problems as organizations try to combine individual applications into integrated enterprise systems.

The tendency to delegate data-quality

Learning Objectives (3 of 5)

Data Warehouses: Enterprise data warehouses (EDW)

Data warehouses that pull together data from disparate sources and databases across an entire enterprise

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

Data Preparation: Procedures to Prepare EDW Data for Analytics

Extract from designated databases

Transform by standardizing formats, cleaning the data, integration

Loading into a data warehouse

Figure 3.15 Database, data warehouses and marts, and BI architecture.

Data Warehouses: ADW

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, and associates

Data Warehouse Processing: 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 software

Map stage: breaks up huge data into subsets

Reduce stage: recombines partial results

Data Warehouses

What are the differences between databases and data warehouses?

What are the differences between data warehouses and data marts?

Explain ETL.

Explain CDC.

What is an advantage of an active data warehouse (ADW)?

Why might a company invest in a data mart?

How can manufacturers and health care benefit from data analytics?

Explain how Hadoop implements MapReduce in two stages.

Suggested Answers:

Human expertise and judgment are needed to interpret the output of analytics because it takes expertise to properly prepare the data for analysis.

2. Validating data and extracting insights that managers and workers can trust are key factors of successful analytics. Data quality is the key to meaningful analytics.

3. If the wrong analysis or datasets are used, the output would be nonsense, as in the example of the Super Bowl winners and stock market performance

4. Federal health reform efforts have pushed health-care organizations toward big data and analytics. These organizations are planning to use big data analytics to support revenue cycle management, resource utilization, fraud prevention, health management, and quality improvement.

5. Data mining is used to discover knowledge that you did not know existed in the databases.

Answers may vary. A data mining example: The mega-retailer Walmart wanted its online shoppers to find what they were looking for faster. Walmart analyzed clickstream data from its 45 million monthly online shoppers then combined that data with product and category related popularity scores which were generated by text mining the retailer’s social media streams. Lessons learned from the analysis were integrated into the Polaris search engine used by customers on the company’s website. Polaris has yielded a 10 to 15 percent increase in online shoppers completing a purchase, which equals roughly $1 billion in incremental online sales.

6. Up to 75 percent of an organization’s data are non-structured word processing documents, social media, text messages, audio, video, images and diagrams, fax and memos, call center or claims notes, and so on. Text mining is a broad category that involves interpreting words and concepts in context. Then the text is organized, explored, and analyzed to provide actionable insights for managers. With text analytics, information is extracted out of large quantities of various types of textual information. It can be combined with structured data within an automated process. Innovative companies know they could be more successful in meeting their customers’ needs if they just understood them better. Text analytics is proving to be an invaluable tool in doing this.

7. The basic steps involved in text mining/analytics include:

Exploration. First, documents are explored. This might be in the form of simple word counts in a document collection, or manually creating topic areas to categorize documents by reading a sample of them. For example, what are the major types of issues (brake or engine failure) that have been identified in recent automobile warranty claims? A challenge of the exploration effort is misspelled or abbreviated words, acronyms, or slang.

Preprocessing. Before analysis or the automated categorization of the content, the text may need to be preprocessed to standardize it to the extent possible. As in traditional analysis, up to 80 percent of the time can be spent preparing and standardizing the data. Misspelled words, abbreviations, and slang may need to be transformed into a consistent term. For instance, BTW would be standardized to “by the way” and “left voice message” could be tagged as “lvm.”

Categorizing and Modeling. Content is then ready to be categorized. Categorizing messages or documents from information contained within them can be achieved using statistical models and business rules. As with traditional model development, sample documents are examined to train the models. Additional documents are then processed to validate the accuracy and precision of the model, and finally new documents are evaluated using the final model (scored). Models then can be put into production for automated processing of new documents as they arrive.

Learning Objectives (5 of 5)

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

BI “unites data, technology, analytics, and human knowledge to optimize business decision and ultimately drive an enterprise’s success” (The Data Warehousing Institute)

Business Intelligence Challenges

Challenges

Data selection and 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

Figure 3.17: Business Intelligence Factors: Four factors contributing to increased use of BI

Business Intelligence Architecture

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

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

ERM Practices and Standards

Best Practices

Effective systems capture all business data

Input from online forms, bar codes, sensors, websites, social sites, copiers, emails, and more

Industry Standards

Association for Information and Image Management (AIIM; www.aim.org)

National Archives and Records Administration (NARA; www.archives.gov)

ARMA International (formerly the Association of Records Managers and Administrators; www.arma.org)

ERM Benefits: an ERM can help a business

Access and use the content contained in documents

Cut labor costs by automating business processes

Reduce time and effort to locate require information for decision making

Improve content security, thereby reducing intellectual property theft risks

Minimize content printing, storing, and searching costs

ERM: Disaster Recovery, Business Continuity, and Compliance

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?

Is the software easy to use and accessible from Web browsers, office applications, and email applications? If not, people will not use it.

Does the software have lightweight, modern Web and graphical user interfaces that effectively support remote users?

Before selecting a vendor, it is important to examine workflows and how data, documents, and communications flow throughout the company.

Suggested Answers:

Human expertise and judgment are needed to interpret the output of analytics because it takes expertise to properly prepare the data for analysis.

2. Validating data and extracting insights that managers and workers can trust are key factors of successful analytics. Data quality is the key to meaningful analytics.

3. If the wrong analysis or datasets are used, the output would be nonsense, as in the example of the Super Bowl winners and stock market performance

5. Data mining is used to discover knowledge that you did not know existed in the databases.

7. The basic steps involved in text mining/analytics include:

Business Intelligence and Electronic Records Management

What are the business benefits of BI?

What are two data-related challenges that must be resolved for BI to produce meaningful insight?

What are the steps in a BI governance program?

What does it mean to drill down into data, and why is it important?

What four factors are contributing to increased use of BI?

Why is ERM a strategic issue rather than simply an IT issue?

Why might a company have a legal duty to retain records? Give an example.

Why is creating backups an insufficient way to manage an organization’s documents?

Suggested Answers:

1. BI provides data at the moment of value to a decision maker—enabling it to extract crucial facts from enterprise data in real time or near real time. BI solutions help an organization to know what questions to ask and to find answers to those questions. BI tools integrate and consolidate data from various internal and external sources and then process them into information to make smart decisions. According to The Data Warehousing Institute (TDWI), BI “unites data, technology, analytics, and human knowledge to optimize business decisions and ultimately drive an enterprise’s success. BI programs… transform data into usable, actionable business information” (TDWI, 2012).

Managers use business analytics to make better-informed decisions and hopefully provide them with a competitive advantage. BI is used to analyze past performance and identify opportunities to improve future performance.

2. Data selection and data quality.

Information overload is a major problem for executives and for employees. Another common challenge is data quality, particularly with regard to online information, because the source and accuracy might not be verifiable.

3. The mission of a BI governance program is to achieve the following:

Clearly articulate business strategies.

Deconstruct the business strategies into a set of specific goals and objectives—the targets.

Identify the key performance indicators (KPIs) that will be used to measure progress toward each target.

Prioritize the list of KPIs.

Create a plan to achieve goals and objectives based on the priorities.

Estimate the costs needed to implement the BI plan.

Assess and update the priorities based on business results and changes in business strategy.

4. Drilling down into the data is going from highly consolidated or summarized figures into the detail numbers from which they were derived. Sometimes a summarized view of the data is all that is needed; however, drilling down into the data, from which the summary came, provides the ability to do more in-depth analyses.

5. Smart Devices Everywhere creating demand for effortless 24/7 access to insights.

Data is Big Business when they provide insight that supports decisions and action.

Advanced Bl and Analytics help to ask questions that were previously unknown and unanswerable.

Cloud Enabled Bl and Analytics are providing low-cost and flexible solutions.

6. Because senior management must ensure that their companies comply with legal and regulatory duties, managing electronic records (e-records) is a strategic issue for organizations in both the public and private sectors. The success of ERM depends greatly on a partnership of many key players, namely, senior management, users, records managers, archivists, administrators, and most importantly, IT personnel. Properly managed, records are strategic assets. Improperly managed or destroyed, they become liabilities.

7. Companies need to be prepared to respond to an audit, federal investigation, lawsuit, or any other legal action against them. Types of lawsuits against companies include patent violations, product safety negligence, theft of intellectual property, breach of contract, wrongful termination, harassment, discrimination, and many more.

8. Simply creating backups of records is not sufficient because the content would not be organized and indexed to retrieve them accurately and easily. The requirement to manage records—regardless of whether they are physical or digital—is not new. ERM systems consist of hardware and software that manage and archive electronic documents and image paper documents; then index and store them according to company policy. Properly managed, records are strategic assets. Improperly managed or destroyed, they become liabilities.

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