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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)

Data Management

Data management is the practice of securely, efficiently, and cost-effectively.

Collecting

Keeping

Using data

The goal of data management is

To help people, organizations, and connected things

Optimize the use of data

Within the bounds of policy and regulation

So that they can make decisions and take actions that maximize the benefit to the organization

Data Management

The work of data management has a wide scope, covering factors such as how to:

Create, access, and update data across a diverse data tier

Store data across multiple clouds and on premises

Provide high availability and disaster recovery

Use data in a growing variety of apps, analytics, and algorithms

Ensure data privacy and security

Archive and destroy data in accordance with retention schedules and compliance requirements

Data Management

A formal data management strategy addresses

The activity of users and administrators

The capabilities of data management technologies

The demands of regulatory requirements

The needs of the organization to obtain value from its data

Data Management

Today data is a kind of capital.

It is an economic factor of production in digital goods and services

Just as an automaker can’t manufacture a new model if it lacks the necessary financial capital

It can’t make its cars autonomous if it lacks the data to feed the onboard algorithms

This new role for data has implications for competitive strategy as well as for the future of computing

Strong management practices and a robust management system are essential for every organization

Regardless of size or type

Data Management

What is Data?

The quantities, characters, or symbols on which operations are performed by a computer

Which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media

What is Big Data?

Big Data is a collection of data that is huge in volume

Yet growing exponentially with time

It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently

Data Management

Big Data In 5 Minutes | What Is Big Data?| Introduction To Big Data |Big Data Explained

https://www.youtube.com/watch?v=bAyrObl7TYE

Data Management

Bytes(8 Bits)

0.1 bytes: A binary decision

1 byte: A single character

10 bytes: A single word

100 bytes: A telegram OR A punched card

Data Management

Kilobyte (1000 Bytes)

1 Kilobyte: A very short story

2 Kilobytes: A Typewritten page

10 Kilobytes: An encyclopedic page OR A deck of punched cards

50 Kilobytes: A compressed document image page

100 Kilobytes: A low-resolution photograph

200 Kilobytes: A box of punched cards

500 Kilobytes: A very heavy box of punched cards

Data Management

Megabyte (1 000 000 Bytes)

1 Megabyte: A small novel OR A 3.5 inch floppy disk

2 Megabytes: A high resolution photograph

5 Megabytes: The complete works of Shakespeare OR 30 seconds of TV-quality video

10 Megabytes: A minute of high-fidelity sound OR A digital chest X-ray

20 Megabytes: A box of floppy disks

50 Megabytes: A digital mammogram

100 Megabytes: 1 meter of shelved books OR A two-volume encyclopedic book

200 Megabytes: A reel of 9-track tape OR An IBM 3480 cartridge tape

500 Megabytes: A CD-ROM OR The hard disk of a PC

Data Management

Gigabyte (1 000 000 000 Bytes)

1 Gigabyte: A pickup truck filled with paper OR A symphony in high-fidelity sound OR A movie at TV quality

2 Gigabytes: 20 meters of shelved books OR A stack of 9-track tapes

5 Gigabytes: An 8mm Exabyte tape

10 Gigabytes:

20 Gigabytes: A good collection of the works of Beethoven OR 5 Exabyte tapes OR A VHS tape used for digital data

50 Gigabytes: A floor of books OR Hundreds of 9-track tapes

100 Gigabytes: A floor of academic journals OR A large ID-1 digital tape

200 Gigabytes: 50 Exabyte tapes

Data Management

Terabyte (1 000 000 000 000 Bytes)

1 Terabyte: An automated tape robot OR All the X-ray films in a large technological hospital OR 50000 trees made into paper and printed OR Daily rate of EOS data (1998)

2 Terabytes: An academic research library OR A cabinet full of Exabyte tapes

10 Terabytes: The printed collection of the US Library of Congress

50 Terabytes: The contents of a large Mass Storage System

Data Management

Petabyte (1 000 000 000 000 000 Bytes)

1 Petabyte: 5 years of EOS data (at 46 mbps)

2 Petabytes: All US academic research libraries

20 Petabytes: Production of hard-disk drives in 1995

200 Petabytes: All printed material OR Production of digital magnetic tape in 1995

Data Management

Exabyte (1 000 000 000 000 000 000 Bytes)

5 Exabytes: All words ever spoken by human beings

Zettabyte (1 000 000 000 000 000 000 000 Bytes)

Yottabyte (1 000 000 000 000 000 000 000 000 Bytes)

Xenottabyte (1 000 000 000 000 000 000 000 000 000 Bytes)

Shilentnobyte (1 000 000 000 000 000 000 000 000 000 000 Bytes)

Domegemegrottebyte (1 000 000 000 000 000 000 000 000 000 000 000 Bytes)

Data Management

Examples Of Big Data

Following are some of the Big Data examples-

The New York Stock Exchange generates about one terabyte of new trade data per day.

Data Management

Social Media

The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day

This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.

Data Management

A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, generation of data reaches up to many Petabytes.

Data Management

Data Growth over the years

Data Management

There are three types of data

Structured

Any data that can be stored, accessed and processed in the form of fixed format is termed as a 'structured' data

Computer science has achieved greater success in developing techniques for working with such kind of data and also deriving value out of it

However, There are problems when the size of such data grows to a huge extent

Typical sizes are being in the rage of multiple zettabytes

Data stored in a relational database management system is one example of a 'structured' data

Data Management

Unstructured

Any data with unknown form or the structure is classified as unstructured data

In addition to the size being huge

Un-structured data poses multiple challenges in terms of its processing for deriving value out of it

A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc.

Organizations have wealth of data available

But they don't know how to derive value out of it

This data is in its raw form or unstructured format

Examples Of Un-structured Data

Data Management

Semi-structured

Semi-structured data can contain both the forms of data. We can see semi-structured data as a structured in form but it is actually not defined with e.g. a table definition in relational DBMS

Example of semi-structured data is a data represented in an XML file.

Examples Of Semi-structured Data

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

Database Technologies: Databases

Data in databases are volatile because they can be updated millions of times every second

Especially if they are transaction processing systems (TPS)

The data is constantly changing over time

Transactions, updates, deletions, and database maintenance all contribute to data volatility

Database Technologies: Databases

Lessons Basic Data Models of Database

https://www.youtube.com/watch?v=diXxet0Bt3Q

Database Technologies: Data Warehouses

Integrate data from multiple databases and data silos,

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 Technologies: BI

What is Business Intelligence

https://www.youtube.com/watch?v=hDJdkcdG1iA

Database Technologies: BI

Business Intelligence (BI)

Business intelligence is the process by which enterprises use strategies and technologies for analyzing current and historical data

With the objective of improving strategic decision-making and providing a competitive advantage

Database Technologies: BI

Business Intelligence (BI)

Business intelligence systems combine

Data gathering

Data storage

Knowledge management

Data analysis

To evaluate and transform complex data into meaningful, actionable information

Which can be used to support more effective strategic, tactical, and operational insights and decision-making

Database Technologies: BI

Business Intelligence (BI)

Business intelligence environments consist of a variety of

Technologies

Applications

Processes

Strategies

Products

Technical architectures

To enable the collection, analysis, presentation, and dissemination of internal and external business information

Database Technologies: BI

Business intelligence technologies use

Advanced statistics and predictive analytics

To help businesses draw conclusions from data analysis

Discover patterns

Forecast future events in business operations

Business intelligence reporting is not a linear practice

It is a continuous, multifaceted cycle of data access, exploration, and information sharing

Common business intelligence functions include:

Database Technologies: BI

Data mining:

Sorting through large datasets using databases, statistics, and machine learning to identify trends and establish relationships

Querying:

A request for specific data or information from a database

Data preparation:

The process of combining and structuring data in order to prepare it for analysis

Reporting:

Sharing operating and financial data analysis with decision-makers so they can draw conclusions and make decisions

Database Technologies: BI

Benchmarking:

Comparing current business processes and performance metrics to historical data to track performance against industry bests

Descriptive analytics:

The interpretation of historical data to draw comparisons and better understand changes that have occurred in a business

Statistical analysis:

Collecting the results from descriptive analytics and applying statistics in order to identify trends

Data visualization:

Provides visual representations such as charts and graphs for easy data analysis

Database Management Systems (DBMS)

Lessons Basic Data Models of Database

https://www.youtube.com/watch?v=diXxet0Bt3Q

Database Management Systems (DBMS)

Integrate with data collection systems such as TPS and business applications

Transaction Processing Systems (TPS) process the company's business transactions and thus support the operations of an enterprise

A TPS records a non-inquiry transaction itself, as well as all of its effects, in the database and produces documents relating to the transaction

Organized way to store, access, and manage data

Database Management Systems (DBMS)

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

Standard database model adopted by most enterprises

DBMS Functions provide an accurate and consistent view of data throughout the enterprise

It enables the organization to make informed, actionable decisions that support the business strategy

Database Management Systems (DBMS)

Functions performed by a DBMS to help create such a view are:

Data filtering and profiling:

Process and store data efficiently

Inspect the data for errors, inconsistencies, redundancies, and incomplete information

Data integrity and maintenance:

Correct, standardize, and verify the consistency and integrity of the data.

Data synchronization:

Integrate, match, or link data from disparate sources

Database Management Systems (DBMS)

Functions performed by a DBMS to help create such a view are:

Data security:

Check and control data integrity over time.

Data access:

Provide authorized access to data in both planned and ad hoc ways within acceptable time

Today’s computing hardware is capable of crunching through huge datasets that were impossible to manage a few years back

Making them available on-demand via wired or wireless networks

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

Relational Database

A relational database is a type of database that stores and provides access to data points that are related to one another

Relational databases are based on the relational model

An intuitive, straightforward way of representing data in tables

In a relational database, each row in the table is a record with a unique ID called the key

The columns of the table hold attributes of the data

Each record usually has a value for each attribute

Making it easy to establish the relationships among data points

Relational Database

Relational Database Concepts

https://www.youtube.com/watch?v=NvrpuBAMddw

Relational Database

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 tables

Strike a balance between transaction-processing efficiency and query efficiency

Cannot be optimized for data mining

OLTP and OLAP Systems

When most business transactions occur

For example when an item is sold or returned

An order is sent or cancelled

A payment or deposit is made

Changes are made immediately to the database

These online changes are additions, updates, or deletions

The database management systems (DBMSs) record and process such transactions in the database

Support queries and reporting

OLTP and OLAP Systems

DBMSs are referred to as online transaction-processing (OLTP) systems

OLTP is a database design that breaks down complex information into simpler data tables

It strikes a balance between transaction-processing efficiency and query efficiency

OLTP databases process millions of transactions per second

OLTP and OLAP Systems

Online Transaction Processing and Online Analytics Processing

Online Analytics Processing (OLAP)

A means of organizing large business databases

Divided into one or more cubes that fit the way business is conducted

Databases cannot be optimized for

Data mining

Complex online analytics-processing (OLAP) systems

Decision support

These limitations led to the introduction of data warehouse technology

OLTP and OLAP Systems

Data warehouses and data marts are optimized for

OLAP

Data mining

Decision support

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

Databases are optimized for extremely fast transaction processing and query processing

Data warehouses are optimized for analysis

OLTP and OLAP Systems

SQL Tutorial: OLTP and OLAP

https://www.youtube.com/watch?v=-v3PhEtOuxw&t=18s

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

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

Centralized and Distributed Database Architecture

A centralized database stores all data in a single central computer such as a mainframe or server

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

For decades the main database platform consisted of centralized database files on massive mainframe computers

Database Management Systems (DBMS)

Centralized and Distributed Database Architecture

Benefits of centralized database configurations include:

Better control of data quality

Data consistency is easier when data are kept in one physical location

Data additions, updates, and deletions can be made in a supervised and orderly fashion

Better IT security

Data are accessed via the centralized host computer

They can be protected more easily from unauthorized access or modification

Centralized and Distributed Database Architecture

Disadvantage of centralized databases

Like all centralized systems

Transmission delay occurs when users are geographically dispersed

More powerful hardware and networks can compensate for this disadvantage

Centralized and Distributed Database Architecture

Distributed databases use client/server architecture to process information requests

The databases are stored on servers that reside

In the company’s data centers

A private cloud

A public cloud

Database Management Systems (DBMS)

Advantages of a distributed database include

Reliability

if one site crashes, the system will keep running –

Speed

It’s faster to search a part of a database than the whole

Disadvantages of a distributed database include

If there’s a problem with the network that the distributed database is using

It can cause availability issues

Appropriate hardware and software can be expensive to purchase

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

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

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

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

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

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

The consequences of not cleaning ‘dirty data’

Each dirty data point, or record, has a financial impact if not resolved

The costs of poor-quality data can spread throughout a company

It can affect systems from shipping and receiving to accounting and customer service

Data errors typically arise from the functions or departments that generate or create the data–and not within the IT department

When all costs are considered

The value of finding and fixing the causes of data errors becomes clear

The consequences of not cleaning ‘dirty data’

In a time of decreased budgets, some organizations may not have the resources for such projects

They may not even be aware of the problem

Others may be spending most of their time fixing problems

Leaving them with no time to work on preventing them

The benefits of acting preventatively against dirty data are considerable

While the short run cost of cleaning and preventing data is unrealistic for some companies

The long term conclusion is far more expensive

The consequences of not cleaning ‘dirty data’

Bad data are costing U.S. businesses hundreds of billions of dollars a year

It is has an impact on a corporation’s ability to ride out tough economic climates

Incorrect and outdated values

Missing data

Inconsistent data formats

Can all cause

Lost customers

Lost sales

Reduced revenue

Misallocation of resources

Flawed pricing strategies

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

Master Data Management (MDM)

There are a number of definitions of exactly what MDM means

There are however some constant themes

MDM is focused on Master or Reference Data

Certain dimensions of data quality are critical to enabling effective MDM

Timeliness, accuracy, completeness, meaning

Continuous data improvement and a well-managed data quality strategy are essential

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

Master Data Management (MDM)

Every organization typically has data on

Customers

Products

Employees

Physical assets

These data items are seldom held in one location

They are often physically scattered around the business

In various applications

Spreadsheets

Physical media such as paper and reports

Suggested Answers:

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

Master Data Management (MDM)

Different parts of the business will have different concepts and definitions for the same business entity and relationship

By way of example

An employee may be recorded in a payroll, HR, training and expense management system of an employer

But back in the real world they are the same person.

Typical examples of master data include

Customers, Employees, Vendors, Suppliers, Parts, Products, Locations, Contact Mechanisms, Profiles, Accounting Items, Contracts, Policies.

Suggested Answers:

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

Master Data Management (MDM)

External data is a typical form of reference data

Whereas standard business objects such as customer, employee, parts and so on are classed as master data

When building MDM strategies external data becomes incredibly important for creating a surrogate source of "truth“

Suggested Answers:

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

Master Data Management (MDM)

Master data management (MDM) processes

Integrate data from various sources or enterprise applications

To create a more complete (unified) view of a customer, product, or other entity

MDM consolidates data from various data sources into a master reference file

This feeds data back to the applications

Creating accurate and consistent data across the enterprise

Suggested Answers:

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

Master Data Management (MDM)

Some vendors claim their MDM solution creates

“a single version of the truth,”

Sadly this is probably not true

In reality, MDM cannot create a single unified version of the data

Constructing a completely unified view of all master data is simply not possible

Suggested Answers:

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

Master Data Management (MDM)

An MDM includes tools for cleaning and auditing the master data elements

Plus tools for integrating and synchronizing data to make them more accessible

MDM offers a solution for managers who are frustrated with how fragmented and dispersed their data sources are

Suggested Answers:

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

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

Learning Objectives (3 of 5)

Databases & Data Warehouses

Databases typically store data to support

Different functions

Information systems

Reporting

Analysis

Each information system in an organization may have multiple databases that are accumulating data

They have specific uses and requirements

Databases & Data Warehouses

A data warehouse is a decision support system which stores historical data from across the organization

Processes it

Enabling the data to be used for critical business analysis, reports and dashboards

A data warehouse system stores data from numerous sources

These are typically structured

Online Transaction Processing (OLTP) data such as invoices and financial transactions

Enterprise Resource Planning (ERP) data

Customer Relationship Management (CRM) data

The data warehouse focuses on data relevant for business analysis, organizes and optimizes it to enable efficient analysis

Databases & Data Warehouses

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

Are called enterprise data warehouses (EDW)

Data warehouses store data from various source systems and databases across an enterprise

To run analytical queries against huge datasets collected over long time periods