Transcript Sharing Enterprise Data

Sharing Enterprise Data
Data
administration
Data downloading
Data warehousing
Data administration
Organization-wide activity (the DBA of a
particular database is only a part of this)
 Challenges:

Many types of data exist
 Basic categories of data are not obvious
 The same data can have many names,
descriptions, and formats
 Data are changed – often concurrently
 Political and organizational issues complicate
operational issues

Marketing
Communicate existence of data
administration to organization
 Explain reason for existence of
standards, policies, and guidelines
 Describe in a positive light the services
provided
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Data standards and policies
Establish standard means for describing
data items; standards include name,
definition, description, processing
restrictions, etc.
 Establish data proponents
 Establish organization-wide data policy;
examples are security, data proponency,
and distribution

Forum for data conflict
resolution
Establish procedures for reporting
conflicts
 Provide means for hearing all
perspectives and views
 Have authority to make decision to
resolve conflict

Return on organization's data
investment
Focus attention on value of data
investment
 Investigate new methodologies and
technologies
 Take proactive attitude toward
information management

Downloading
data for local
processing
Data downloading
via file-sharing systems
Data downloading
via client-server systems
Downloading: potential problems

Coordination
Conform downloaded data to database constraints
 Coordinate local updates with downloads
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Consistency
Downloaded data should not be updated
 Applications need features to prevent updating
 Warn users of possible problems
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Access control
Data may be replicated on many computers
 More difficult data access control procedures
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Risk of computer crime
Disks and modem access are easy to conceal
 Illegal copying is difficult to prevent
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Data warehousing

What if every department wants to
download the organization’s data?

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The data management problem becomes
immense
Data warehouse: a centralized
repository to facilitate management
decision making and increase the
value of the enterprise data assets
Data warehouse architecture
Integrated From Various
Sources

Operational Data
appln A - m,f
appln. B - male, female
appln. C - x,y
appln.. D - 1,0
Data Warehouse
m, f
Data in Data Warehouse
National Sales by
Month 85-98
Regional Sales
by Week 83-98
Sales Detail 1998-99
Sales Detail 1992-98
Highly
Summarized
Lightly
Summarized
Current Detail
Older Detail
Data
Time Variant
Operational Data
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time horizon 60-90
days
key may / may not
have element of
time
can be updated
Data Warehouse
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time horizon 5-10
years
key contains
element of time
once snapshot is
made data cannot
be updated
Non - volatile
Change
Replace
Replace
Insert
Load
Operational Data

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Data is updated on a
record by record basis
To support the recordby-record on line
update, requires the
technology to have very
complex foundation
Access
Data Warehouse

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Data is not updated
The physical design
levels liberties can be
taken to optimize the
access of data
Data warehouse components
Data extraction tools
 Extracted data
 Metadata of warehouse contents
 Warehouse DBMS(s)
 Warehouse data management tools
 Data delivery programs
 End- user analysis tools
 User training courses and materials
 Warehouse consultants

Data warehouse requirements
Queries and reports with variable
structure
 OLAP: On-Line Analytical Processing

User- specified data aggregation
 User- specified drill down
 Graphical outputs
 Integration with domain- specific programs
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OLAP
--to gain insight into data through fast, consistent,

interactive access to wide variety of views
--functionality characterized by dynamic
multidimensional
analysis of consolidated enterprise data
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Data Extraction
--ability to capture, convert, & deliver data to various
sources
--provides fast disk-to-disk transfer capabilities and
automate data compression

Data Mining Tools
-- helps by focusing end user attention on a smaller
subset of data
-- subset is determined by data mining
“discovery”process, which is done in advance of indepth analysis
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Executive Information System
-- for senior executives with little computing experience
-- available on demand with whatever level of detail (
drill-down)
-- add value, improve strategic & financial control,
market & economical information, better competitive
analysis
Financial & Marketing Analysis
-- provides end user with highly value added report
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like
accounts receivable / payable, ledger mgmt., cost
control
cost budgeting & planning,
-- in marketing - product pricing, demand analysis,
estimation
-- use non-technical language, run queries in fast,
reliable manner..
 Report & Query Tools
-- most important & widely used
-- emphasize generating value added reports
-- user have flexibility to use either common English/
SQL
-- support graphical interface
Example
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FINGERHUT
150 catalog mailings in 1997
 based on statistically predicted consumer
response
 30 million customers, 14% annual growth
 database captures 1400 pieces of
information about a household
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demographics, purchasing histories
Data warehouse challenges
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Inconsistent data
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Tool integration
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E.g., spreadsheets versus databases…
Lack of warehouse data management
tools
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E.g., different timing, different domains...
In-house software development (expensive)
Ad-hoc requirements
Data warehousing
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Is it as good an idea as it seemed?
What about the Internet?
 Data mart: limit the scope of the
warehouse
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