1 Data Management: Warehousing, Analyzing, Mining, and Visualization

Goals
Recognize the importance of data, their issues, and their life

cycle.
Describe the sources of data, their collection, and quality issues.
Describe document management systems.
Explain the operation of data warehousing and its role in decision support.
Describe information and knowledge discovery and business intelligence.
Understand the power and benefits of data mining.
Describe data presentation methods and geoinfosystems and virtual reality as decision support tools.
Discuss the role of marketing databases
Recognize the role of the Web in data management

are dispersed throughout different organizations.
Data are collected by many individuals

using several methods.
External data needs to be considered in making organizational decisions.
Data security, quality, and integrity are critical factors
of data management procedures.

IT applications cannot be done without using some kind of data
Which are at the core of daily management and marketing operations. However, managing data is difficult for various reasons.

Data become an asset, when it converted to information and knowledge, and give the firm an competitive advantage.

sources.
It is temporarily stored in a database then preprocessed to

fit the format of the organizations data warehouse or data marts
Users then access the warehouse or data mart and take a copy of the needed data for analysis.
Analysis (looking for patterns) is done with
Data analysis tools
Data mining tools

Businesses run on data that have been processed to information and knowledge, which managers apply to businesses problems and opportunities. This transformation of data into knowledge and solutions is accomplished in several ways.

The result of all these activities is the generating of decision support and new knowledge

Continued
The result of data processing is to

generate a solution

corporate database and are about people, products, services, and processes.
Personal

Data is documentation on the expertise of corporate employees usually maintained by the employee. It can take the form of:
estimates of sales
opinions about competitors
business rules
Procedures
Etc.
External Data Sources range from commercial databases to Government reports.
Internet Databases and Commercial Database Services are accessible through the Internet.

The data life cycle begins with the acquisition of data from data sources. These sources can be classified as internal, personal, and external.

field
from individuals
via manually methods
time studies
Surveys
Observations
contributions from experts
using instruments and

sensors
Transaction processing systems (TPS)
via electronic transfer
from a web site

The task of data collection is fairly complex. Which can create data-quality problem requiring validation and cleansing of data.

consists of
a decision support system
a central data request processor
a

data integrity component
links to external data suppliers
the processes used by the external data suppliers.

One way to improve data collection from multiple external sources is to use a data flow manager (DFM), which takes information from external sources and puts it where it is needed, when it is needed, in a usable form.

DQ: Accessibility and access security.
Contextual DQ: Relevancy, value added, timeliness,

completeness, amount of data.
Representation DQ: Interpretability, ease of understanding, representation

Data quality (DQ) is an extremely important factor since quality determines the data’s usefulness as well as the quality of the decisions based on the data analysis. Data integrity means that data must be accurate, accessible, and up-to-date.

Data quality is the cornerstone of effective business intelligence.

version
An update schedule should be determined
Security be provided for the

document
The documents be distributed to the appropriate individuals in a timely manner

Document management is the automated control of electronic documents, page images, spreadsheets, word processing documents, and other complex documents through their entire life cycle within an organization, from initial creation to final deleting or archiving.

systems at operational level (TPS) that provide the organization with

the capability to perform business transactions and produce transaction reports. The data are organized mainly in a structured manner and are centrally processed. This is done primarily for fast and efficient processing of routine, repetitive data flows.

A supplementary activity to transaction processing is called analytical processing, which involves the analysis of accumulated data. Analytical processing, sometimes referred to as business intelligence, includes data mining, decision support systems (DSS), querying, and other analysis activities. These analyses place strategic information in the hands of decision makers to enhance productivity and make better decisions, leading to greater competitive advantage.

ability to reach data quickly, since they are located in

one place
The ability to reach data easily and frequently by end users with Web browsers.
Characteristics of data warehousing are:
Organization. Data are organized by subject
Consistency. In the warehouse data will be coded in a consistent manner.

A data warehouse is a repository of subject-oriented historical data that is organized to be accessible in a form readily acceptable for analytical processing activities (such as data mining, decision support, querying, and other applications).

data are kept for many years so they can be

used for trends, forecasting, and comparisons over time.
Relational. Typically the data warehouse uses a relational structure.
Client/server. The data warehouse uses the client/server architecture mainly to provide the end user an easy access to its data.
Web-based. Data warehouses are designed to provide an efficient computing environment for Web-based applications

marts:
Replicated (dependent) data marts are small subsets of the data

warehouse. In such cases one replicates some subset of the data warehouse into smaller data marts, each of which is dedicated to a certain functional area.
Stand-alone data marts. A company can have one or more independent data marts without having a data warehouse. Typical data marts are for marketing, finance, and engineering applications.

A data mart is a small scaled-down version of a data warehouse
designed for a strategic business unit (SBU) or a department. Since they contain less information than the data warehouse they provide more rapid response and are more easily navigated than enterprise-wide data warehouses.

a product sold by specific retail locations during certain time

periods.
Another matrix might be Sales volume by department, by day, by month, by year for a specific region
Cubes provide faster the following opportunities for analysis :
Queries
Slices and Dices of the information
Rollups
Drill Downs

Multidimensional databases (sometimes called OLAP) are specialized data stores that organize facts by dimensions, such as geographical region, product line, salesperson, time. The data in these databases are usually preprocessed and stored in data cubes.

that require time sensitive data analysis
It logically falls between the

operational data in legacy systems and the data warehouse.
It provides detail as opposed to summary data.
It is optimized for frequent access
It provides faster response times.

Operational data store is a database for transaction processing systems that uses data warehouse concepts to provide clean data to the TPS. It brings the concepts and benefits of a data warehouse to the operational portions of the business.

budgeting
resource allocation
coupons and sales promotions
Seasonality trends
Benchmarking (business performance)
competitive intelligence.
Business

intelligence (BI) is a broad category of applications and techniques for gathering, storing, analyzing and providing access to data. It help’s enterprise users make better business and strategic decisions. Major applications include the activities of query and reporting, online analytical processing (OLAP), DSS, data mining, forecasting and statistical analysis.

Business Intelligence tools starts with Knowledge Discovery

multiprocessor computing
data mining and other algorithms processing.
KDD primarily employs

three tools for information discovery:
Traditional query languages (SQL, …)
OLAP
Data mining

Before information can be processed into BI it must be discovered or extracted from the data stores. The major objective of this procedure of knowledge discovery in databases (KDD) is to identify valid, novel, potentially useful, and understandable patterns in data.

Discovering useful patterns

results are subsets of the relationship :
Sales by department by

customer type for specific period
Weather conditions for specific date
Sales by day of week
…

Queries allow users to request information from the computer that is not available in periodic reports. Query systems are often based on menus or if the data is stored in a database via a structured query language (SQL) or using a query-by-example (QBE) method.

on top of an existing relational database. The multidimensional view

is created each time for the user.
MOLAP (Multidimensional OLAP) is a specialized multidimensional data store such as a Data Cube. The multidimensional view is physically stored in specialize data files.

Online analytical processing (OLAP) is a set of tools that analyze and aggregate data to reflect business needs of the company. These business structures (multidimensional views of data) allow users to quickly answer business questions. OLAP is performed on Data Warehouses and Marts.

providing:
Automated prediction of trends and behaviors.
Automated discovery of previously

unknown or hidden patterns.
Data mining tools can be combined with:
Spreadsheets
Other end-user software development tools
Data mining creates a data cube then extracts data

Data mining is a tool for analyzing large amounts of data. It derives its name from the similarities between searching for valuable business information in a large database, and mining a mountain for a valuable ore.

computing is a machine learning approach which examines historical data

for patterns.
Intelligent agents retrieving information from the Internet or from intranet-based databases .
Association analysis uses a specialized set of algorithms that sort through large data sets and express statistical rules among items.
Decision trees
Genetic algorithms
Nearest-neighbor method

group.
Clustering. Identifies groups of items that share a particular characteristic.

Clustering differs from classification in that no predefining characteristic is given.
Association. Identifies relationships between events that occur at one time.
Sequencing. Identifies relationships that exist over a period of time.
Forecasting. Estimates future values based on patterns within large sets of data.
Regression. Maps a data item to a prediction variable.
Time Series analysis examines a value as it varies over time.

may have several dimensions.
Dimensions:
Products
Salespeople
Market segments
Business units
Geographical locations
Distribution channels
Countries
Industries
Data visualization

refers to presentation of data by technologies such as digital images, geographical information systems, graphical user interfaces, multidimensional tables and graphs, virtual reality, three-dimensional presentations, videos and animation.

Multidimensionality Visualization:

computer-based system for capturing, storing, checking, integrating, manipulating, and displaying

data using digitized maps. Every record or digital object has an identified geographical location. It employs spatially oriented databases.
Visual interactive modeling (VIM) uses computer graphic displays to represent the impact of different management or operational decisions on objectives such as profit or market share.
Virtual reality (VR) is interactive, computer-generated, three-dimensional graphics delivered to the user. These artificial sensory cues cause the user to “believe” that what they are doing is real.

the current databases and marketing data sources into a new

database that allows marketers to engage in real-time personalization and target every interaction with customers
Interactive capability
an interactive transaction occurs with the customer exchanging information and updating the database in real time, as opposed to the periodic (weekly, monthly, or quarterly) updates of classical warehouses and marts.

Data warehouses and data marts serve end users in all functional areas. Most current databases are static: They simply gather and store information. Today’s business environment also requires specialized databases.

integrate query, reporting, OLAP, and other tools
Intelligent Data Warehouse Web-based

Systems employ a search engine for specific applications which can improve the operation of a data warehouse
Clickstream Data Warehouse occur inside the Web environment, when customers visit a Web site.

Data management and business intelligence activities—from data acquisition to mining—are often performed with Web tools, or are interrelated with Web technologies and e-business. This is done through intranets, and for outsiders via extranets.