Zarządzanie cyklem życia danych

© 2012 IBM Corporation

Zarządzanie cyklem Ŝycia danychPeter Harrison, Smarter Planet Solutions Lead Architect CEE


The Information Challenge


On average, data repositories for large applications grow by 50% annually


How are Organizations Responding to Data Growth?

Hardware Capacity

Performance

Dat

abas

e S

ize

• Use database partitioning

• Use database vendor compression

• Buy more Storage & CPU hardware


�Performance degradation � Key business processes - payroll,

shipping, financial period close, information reporting - not being completed on time

� SLAs missed

� Applications not available when customers want to do business with you

� Customer satisfaction impacted

�Regulatory compliance �Data retention (Sox, Euro-Sox, J-Sox)

�Budget Pressures�Increased hardware storage costs due to

growing data

Challenges associated with Data Growth

�Storage cost spiral

� Multiplier effect

�Deteriorating service levels� Customer & employee complaints

� Increased fire fighting

�Increased maintenance burdens� Time spent on tuning and partitioning

� Longer backup windows

� Backup and recovery time increases

�Batch window creep


Storage optimization can

reduce storage requirements

by over 90%!


Benefits of Data Growth Management

• Reduce/Control Costs:– Storage costs - production level data is typically one of the

most expensive storage platforms

– Reduce CPU & disk upgrades and related downtime

– Enable tiered storage strategies

– Administrative costs - software license fees, hardware costs

& the people to manage data growth

(DBA, system & storage admin)

• Improve Application Performance:– Improved availability – no downtime caused by batch

process overruns;

– meet SLAs

– Improved/faster backup and recovery


The results can have significant financial impact

For every 20% that is spent on storage, 80% cost is spent on theoperational elements of managing that stored informati on


… with a rapid payback and high ROI

3 Yr Accumulative benefit

3,8M EUR

3 Yr ROI

151%

Payback period

2,5 months

€ 0,0

€ 2,0

€ 4,0

€ 6,0

€ 8,0

€ 10,0

€ 12,0

€ 14,0

€ 16,0

Day 0 Year 1 Year 2 Year 3 Year 4 Year 5

Exp

endi

ture

(M)

base case Optim DG

Sample assumptions: 10 TB in production, growth 20% per annum, 4 copies, 45 EUR/GB total annual storage cost


Requirements to manage data across its lifecycle

Validate test resultsDefine policiesReport & retrieve

archived data

Enable compliance with retention &

e-discovery

Move only the needed information

Integrate into single data source

Create & refresh test data Manage data growthClassify & define data

and relationships

Develop database structures & code

Enhance performance

Discover where data resides

Develop &Develop &TestTest

Understand &Understand &DefineDefine

Optimize, Archive Optimize, Archive & Access& Access

Consolidate &Consolidate &RetireRetire

Information Governance Core DisciplinesLifecycle Management

Application Owners

& Data Stewards“I need to understand what data exists and how its related in order to define the policies and standards to manage that

information as it ages. “

“I need to store data according to enterprise standards.” [Leverage Data Architect]

Test & QA Teams “I need a repeatable process to configure and refresh my test databases quickly to meet business demands.”

“I need to identify bottlenecks in system performance.”

Operations Team

DBAs

“I need a method to tune application performance and archive aged data.”

“I need to archive legacy data that we do not want to move to the new application but provide access to it on-demand.”

Developers “I need to consolidate & integrate data across multiple systems.”

Slide 10

A1 Remove AccessAuthor; 2010-11-23


13.Govern Lifecycle of Information13.1 Baseline database sizes and storage architecture13.2 Discover business objects13.3 Classify data and define service levels13.4 Archive data and unstructured content13.5 Establish policies for management of test data13.6 Define policies for legal discovery of electronic documents13.7 Analyze content

Information Lifecycle Governance


UNIVERSAL

ACCESS

UNIVERSAL

ACCESS

IBM Optim Solution Overview

Optim Designer

Eclipse-Based

Optim ServerOptim Server

Web-Based Management

Console

DRADRA

InfoSphereData Arch.InfoSphereData Arch.

3rd party model data3rd party

model data

InfoSphereDiscoveryInfoSphereDiscovery

TARGETS

Archive FilesArchive Files

Archive DatabaseArchive Database

XMLXML

OtherOther

WORMWORM

Tape or CDTape or CD

Custom / Legacy / P

ackaged

Application Data

Custom / Legacy / P

ackaged

Application Data

Non-Production EnvironmentsNon-Production Environments

Subset/MaskSubset/Mask

Additional Options

ODBC / JDBC

XML

SQL

Excel

Access

ArchiveArchive

ERP/CRM Applications

Custom/Other

IBM Mashups


All projects require that you “Discover and Define”

You can’t manage what you don’t understand

• Data distributed over multiple applications,

databases and platforms

– Where are the entities located?

– How are different DBs related?

• Complex, poorly documented data

relationships

• Inconsistent data models and designs

– Different vocabularies across the

organization

– Documentation poor or non-existant

– Not often kept up-to-date

– Applications manage the relationships and

business rules – not in the DB

• Where is the sensitive data?

?

??

??

??

?

???

?

?

?

?

?

?

?

??

?

??

?

??

?

?

?

?

Distributed Data Landscape


Understanding data relationships across the enterprise

Capture related business objects from across the enterprise

Define

CRM on

Oracle database

ERP / Financials

on DB2

Custom Inventory Mgmt

on DB2

• Represents application data record – payment, invoice, customer

– Referentially-intact subset of data across related tables and applications; includes metadata

• Provides “historical reference snapshot” of business activity

• Federated extract support across enterprise data stores


Employ effective test data management practicesDevelop &

Test

•Create targeted, right-sized test environments

•Substitute sensitive data with fictionalized yet contextually accurate data

•Easily refresh, reset and maintain test environments

•Compare data to pinpoint and resolve application defects faster

•Accelerate release schedules

Production or Production Clone

100 GB

25 GB

50 GB

25 GB

Development

Unit Test

TrainingIntegration

Test

Extract

Relational subset

Load / MaskInsert / UpdateCompare


Current

Production

Historical

Retrieve

Retrieved

Universal Access to Application Data

Application Application XML ODBC / JDBC

Step 13.4: Optim™ Data Growth Solution: Archiving

Archives

Reporting

Data

Historical

DataReference

Data

Archive

Optim

Mashup

Archiving is an intelligent process for moving inactive or infrequently accessed data that still has value, while providing the ability to search and retrieve the data


Leverage Cost-Effective Storage Alternatives

Non DBMSRetention Platform

ATA File ServerEMC CenteraIBM RS550HDS

Compressed

Archives

Offline Retention Platform

CDTapeOptical

Compressed

Archives

Production

Database

Archive

Definitions

Archive

Restore

Archive Reporting

Database

Compressed

Archives

Online

Archive

5-6 years

Offline

Archive

7+ years

Current

Data

1-2 years

Active

Historical

3-4 years


Proven with customers across the world…


Summary

• Databases are growing at an unprecedented rate

• Cost of storing data increases as the data grows

• The larger the database, the slower the performance

• Archive historical data to reclaim space, improve performance

– Prevent data growth from impairing business results

• Automate Business Object Discovery to gain new Data Insights,

Ensure Accuracy and Speed Implementation

• Control database size at desired level

– Minimize storage footprint, cut costs

– Streamline routine maintenance

• Significant cost savings and rapid ROI!!!


The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices

Sunil Soares : Director of Data Governance, IBM Software Group

IBM DATA GOVERNANCE UNIFIED PROCESS

IBM - Pioneering Information Governance

Date post:	20-Aug-2015
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