sitnl 2015 lean data management (frank gundlich)
TRANSCRIPT
# 3
Agenda
• Who is DataVard
• What is the value of your data
• Lean Data Management
• Key take a ways
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Who is DataVard?
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DataVard
− Helping customers improve their SAP Landscape since 1998− Fortune 1000 and DAX30 (e.g. Allianz, BASF, KPMG, Roche, Nestle)− SAP & DataVard, a partnership since 1999 − Development partner of SAP® Landscape Transformation Suite (LT) and
Information Lifecycle Management (ILM)
− Gartner 2015 positioned DataVard the furthest for completeness of vision in the Niche Players Quadrant for Structured Data Archiving
− Global reaching with locations in Germany (HQ), Italy, Slovakia, United Kingdom and the US
− BW/BI optimization, innovative and platform-dependent data management, − Data management across your SAP Landscape− Automated system housekeeping− Test Data Automation− HANA
Growth gives Credibility
Experience gives Safety
Focus gives Strength
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• System Analysis and data classification
• Data Volume Management (ADK, ILM NLS)
• Automated Housekeeping• Hadoop integration into
SAP• ILM• Data cleansing
• Data migration• Performance analysis and
tuning• System monitoring• Automated system
administration and housekeeping
• Selective System copies
DataVard Solution Suite
Manage your data in line with the data’s value
Intelligent system landscape management
Lean Data Management
System Monitoring & Management
OutBoard™ Suite CanaryCode & ReLine
• Test administration• Test data creation • Automated testing• Performance testing• Test monitoring
Automated SAP testing
Lean Test Automation & Selective System
Copy
Selective System Copy
• Mergers & Acquisitions• Data harmonization and
standardization• Company reorganization• Divestiture, carve-out and
spin-offs• System decommissioning• System Landscape
Optimization (SLO)• HANA Deployments
Re-organize your data to reflect business change
Business Transformation
ReLine Suite
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Data Value in the age of Big
Data
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vs.
Smart Data Management is paramount
How much VALUEdo your data
generate?
How much COSTdo your data
generate?
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5%
15%
15%
9%
11%
32%
5%5% 3%
Master data
Temporary data
Other data
PSA data
Changelog data
ODS data
Cube E data
Cube F data
Cube D data
Data distribution in SAP BW*
Comments:§ 13-17% of system size is
reporting data§ Quick check on
housekeeping potential (size of BALDAT, RS*DONE, ...)
§ HANA sizing report gives a 1st indication (OSS note 1736976)
“Only 12% of all data in BW is actually used.”Forrester research
* Source: DataVard BW Fitness Test
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How to shrink your Database – 5 common practices
Housekeeping of temporary data
Selective Copy of PROD to non-
PROD
Transactional & Analytical Data
Documents / Attachments
Selective Copy of PROD to non-PROD
Move non-active data
Delete unnecessary
or unused data
Move documents to separate store
§ Protocols, Logs, Statistics, other Temp. data
§ Standard & automated procedure
§ Selection e.g. based on time-slice
§ Maintaining integrity of data for testing
§ HANA: Active / non-active data concept
§ ERP: SARA Archiving
§ BW: NLS, SDA, DT
§ Using ArchiveLinkinterface
§ E.g. incoming invoices as PDF or email
§ Attached to Business process
Master, Transactional & Analytical Data
Avoid data creation
§ Based on changing user behavior data or apps may be obsolete
?% 2 5 % 3 5 % 6 5 % 1 1 %
Typi
calb
enei
ft
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Lean Data Management-Architecture
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Central Governance, Management Functions
Incorporation into an existing operations model, Automation
Requirements definition & goals per data class (temperature based)
Achieving - Lean Data Management
Define
Improve
Data Profiling, Growth & Cost analysis (e.g. BW Fitness Test™)
Data Classification based on usage (HeatMap) in Reporting & ETLAnalyze
Control
Measure
Data handling via simple rules derived from external/legal, internal rules and classification
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BW Fitness Test™ – Sample
Check Here
„The BW Fitness Test™ prepared us perfectly to make our SAP BW fit for the future. Now we manage our aged data with Nearline Storage and improved our Load Performance.“
Steffen Muesel, Randstad
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DataVard HeatMap• Cost / benefit analysis
• Cost is usually associated with volume and storage
• Benefit is measured by number of queries executed
• Other important KPIs are users, number of loads, duration of loads, etc.
How does it work?
1: Size map of the SAP system2: Determining KPIs3: Correlating KPIs4: Know hot and cold spots
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Turning usage statistics into Operational Intelligence
DataVard HeatMap
Leading chemical company: 8,1 TB of data which was queried less than 5 times over a 6 months period.
Use Case
8,1 TB non-active data moved to NLS
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IQ RDBMSVertica Hadoop File Recycle Bin
DATAVARD Multi-tier Storage Management
OutBoard™ - Architecture overview on SAP Data Management
Protocols, Logs, Statistics, other Temp.
data
CUBE, ODS, PSA, Ch.Log
Master-, Transactional data
Scan from paper, PDF, Email
Logi
cal v
iew
App
licat
ion
view
Stor
age
view
Analytical Data Transactional Data Temporary Data Documents
§ Identification / analysis
§ Mass archiving§ Automation§ Recycle Bin prep§ Cross System
§ HeatMap Analysis§ Selection of data§ Mass archiving§ Automation§ NLS Writer
§ HeatMap Analysis§ Compliant archiving§ Automation§ Browse & search via
NLS-like interface
§ OCR§ Categorization§ Automatic assignment
Data
Toolkit/Features
Interface NLS SDA DT ILM “NLS”ADK ADK Deletion ArchiveLink
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OutBoard™ - Storage Layer Concept
Manage the cost of storage inline with the value of information.
Data can be transferred to other layers, managing various aging thresholds using Aging Profiles.
Example:− Up to 2 years in SAP HANA− 2-7 years in IQ− 8-10 years in files− 11+ will be deleted
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OutBoard™ - Housekeeping
Scope of Housekeeping
n Unused customersn Unused vendorsn Phantom change documentsn Phantom textsn Application log n Batch logn IDoc tables (EDI40, EDIDS)n qRFC, tRFCn Job-Tables (TBTCO, TBTCP etc.) n Change & Transportsystemn Spool data (TST03)n Table Change Protocolsn Batch Input Foldersn Alert Management Data (SALRT*)n Old short dumpsn Batch input datan …
ERP and SAP NetWeaver®n PSAs & Change Logsn Request logs & tables (RSMON* and
RS*DONE)n Unused dimension entriesn Unused master data n Cube & Aggregate compressionn Temporary database objectsn NRIV bufferingn Table bufferingn BI-Statisticsn Process Chain Logn Errorlogsn Unused Queriesn Empty partitionsn BI Background processesn Bookmarksn Web templatesn …
Business Warehouse
§ Housekeeping addresses data which is not relevant for business
§ Housekeeping should be automated to avoid manual work
§ Housekeeping should be done centrally for the complete SAP landscape.
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Housekeeping Cockpit
“With the help of OutBoard™ and ERNA™ we were able toreduce the system size by 35% in the initial wave of archiving.”
Jens Graef, Kion Group IT
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The effect! A real case from automotive customer.
• Cost per GB per month range from USD 1.13 to USD 2.33 and approx. 40-45% for backups
• Data gets moved to NLS after 2 years
• Data growth is 35-40% p.a.
• One-time effect is 43%!• 20% of the data is
removed with Housekeeping “ERNA”
System growth in TB Assumptions
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ü The cost of storage needs to match the business value of your data.
ü Separate Data Management from Storage Technology. An open architecture secures your current and future investments.
ü Automation and central rules for ease of Data Management.
ü Iterate through the DMAIC cycle several times. Refine rules based on actual data usage statistics.
ü Start reducing data volumes from bottom (staging) to top (reporting).
5 Key Principles of Lean Data Management
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ThankYou
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