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Dr. Bjarne Berg

What you need to know to get the most out of using SAP NetWeaver BW as your enterprise data warehouse. Dr. Bjarne Berg . What We’ll Cover …. Introduction The EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse

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Dr. Bjarne Berg

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  1. What you need to know to get the most out of using SAP NetWeaver BW as yourenterprise data warehouse Dr. Bjarne Berg

  2. What We’ll Cover … • Introduction • The EDW architectural options • Federated Data Warehouse • Centralized Data Warehouse • Distributed Data Warehouse • Data Integration challenges • Masterdata • Transaction data conversion • Data cleansing • Designing for Flexibility • The Support Organization • The top 10 EDW pitfalls • Wrap-up

  3. In this session. • We will take a detailed look at the pros and cons of your EDW architectural options, including federated, centralized, and distributed EDW models, and explore when each approach is appropriate. • Learn how to interface The Support Organization and how to consolidate different master and transactional data. • Weigh your options for building a centralized or a decentralized EDW support organization. • Examine the top 10 pitfalls companies face when implementing SAP NetWeaver BW as their EDW and how to overcome them. 4

  4. A Quick Definition: BI Vs. Data Warehousing Data warehousing is the act of extracting, transferring, transforming, storing and retrieval of data for reporting and analytical purposes. Business Intelligence (BI) is a terminology for applications that uses data stores for analytical purposes. BI applications are not required to run on top of data warehouses, but the majority does 5

  5. What We’ll Cover … • Introduction • The EDW architectural options • Federated Data Warehouse • Centralized Data Warehouse • Distributed Data Warehouse • Data Integration challenges • Masterdata • Transaction data conversion • Data cleansing • Designing for Flexibility • The Support Organization • The top 10 EDW pitfalls • Wrap-up 6

  6. A Logical Enterprise DW Architecture Functional Area Custom Developed Applications InvoicingSystems Purchasing Data ExtractionIntegration and Cleansing Processes Marketingand Sales PurchasingSystems Data Mining Translate CorporateInformation SegmentedData Subsets GeneralLedger Attribute Statistical Programs Summation Calculate Other InternalSystems Product Line Derive SummarizedData Query Access Tools Summarize External DataSources Location Synchronize Metadata OperationalData Store DataWarehouse Source Data Extract Transform BI Applications Data Resource Management and Quality Assurance Source: Bjarne Berg, “Introduction to Data Warehousing”,1997

  7. The Federated Data Warehouse (FDW) Architecture Security Training User Support Projects Ad-hoc Synchronization Metadata IT Driven Data Warehouses IT Developed Semantic Layer Business Driven BI Applications IT Support & Development Users Enterprise Portal SAP BW(s) SAP BOBJ OLAP Universes Ad-Hoc Webi Financial Report center SAP BW InfoCubes OLAP Pioneer Sales Report center Employees SAP DSOs SAP BOBJ SQL Universes Dashboards Xcelcius Manufacturing Report center Batch reports Crystal HR Report center Direct Connections SAP BWA BEx Explorer Customers Partner facing Report center SAP BOBJ Data Services Custom and 3rd party Data Warehouse(s) Customer facing Report center DW Star-schemas BPC Partners Ad-Hoc Report center DW ODSs External Applications Data Resource Management and Quality Assurance 8

  8. Federated Data Warehouse (FDW) Architecture • Federated Data Warehouses are best in very large organization where development is separated by geography, organizational boundaries, or where multiple data warehouses exists due to mergers & acquisitions. • To make FDWs successful, there needs to be a rapid convergence to standardized technologies. This include: • Same type of databases and support pack levels (costs and compatibility) • Same technical platforms Hardware, Backups and Archiving (costs) • Shared Portal and user interface strategy (reduced training and support) • Shared security design and centralized administration (risk management) If the data is federated you gain faster response time to business needs, can execute multiple projects in parallel, and work 24/7 across the globe. But without any standardization, it can also be very costly. 9

  9. The Centralized Data Warehouse (CDW) Architecture Security Training User Support Projects Ad-hoc Synchronization Metadata IT Driven Data Warehouses IT Developed Semantic Layer Business Driven BI Applications IT Support & Development Users Enterprise Portal OLTP sources Ad-Hoc Webi Financial Report center SAP ECC SAP BOBJ OLAP Universes Siebel, JDE OLAP Pioneer Sales Report center Oracle Employees Others Dashboards Xcelcius Manufacturing Report center SAP BOBJ SQL Universes Batch reports Crystal HR Report center SAP BW BEx Explorer Customers SAP BW InfoCubes Partner facing Report center Direct Connections Custom and 3rd party SAP DSOs Customer facing Report center BPC SAP BOBJ Data Services Partners Ad-Hoc Report center External Applications SAP BWA Data Resource Management and Quality Assurance 10

  10. Centralized Data Warehouse (CDW) Architecture • Centralized Data Warehouses are great for small and mid-size data warehouses (less than 15-40Tb). There are great benefits in terms of the ease to mange upgrades, support packs, enforcing development standards, transport control, master data management and the overall total cost of ownership • To make CDWs successful, there needs to be: • Adequate funding of hardware, application servers, database servers • Serious consideration should be made to move BI and reporting to BWA • Focus on using the database capacity on storage and data loads-- not queries • No direct reporting from DSOs (takes too much system resources) • Broadcasting , caching and performance tuning is a dedicated support effort • A plan for data partitioning and archiving needs to be in-place as soon as the system exceeds 5-8 TB. If the data is centralized it is faster to develop new solutions for the business and merging from different data sources are easier 11

  11. The De-centralized Data Warehouse (DDW) Architecture Security Training User Support Projects Ad-hoc Synchronization Metadata IT Driven Data Warehouses IT Developed Semantic Layer Business Driven BI Applications IT Support & Development Users Enterprise Portal SAP BW(s) Ad-Hoc Webi Financial Report center SAP BW InfoCubes SAP BOBJ OLAP Universes OLAP Pioneer Sales Report center Employees SAP DSOs Dashboards Xcelcius Manufacturing Report center SAP BOBJ SQL Universes Batch reports Crystal HR Report center SAP BWA BEx Explorer Customers Partner facing Report center Direct Connections Custom and 3rd party SAP BW(s) Customer facing Report center SAP BW InfoCubes BPC SAP BOBJ Data Services Partners Ad-Hoc Report center External Applications SAP DSOs Data Resource Management and Quality Assurance 12

  12. De-centralized Data Warehouse (DDW) Architecture • A Decentralized Data Warehouses makes sense if there are logical division between business units, geographies and little shared reporting I.e. in a conglomerate organization with diverse business units. • The benefits of DDWs include the flexibility of the FDW with the technology standardization and lower cost of ownership of the CDW. To make DDWs successful, there needs to be: • A formal Masterdata Management (MDM) strategy with clearly defined standards • A rule based data cleaning and data integration plan for centralized reporting • A shared hardware location to keep costs lower • Tight integration with upgrades, support packs and interface standards With DDWs there is a risk of creating stove-pipe data marts that cannot be integrated at the corporate level without very high costs. 13

  13. Recommendations CDW, FDW and DDW Architectures 14

  14. What We’ll Cover … • Introduction • The EDW architectural options • Federated Data Warehouse • Centralized Data Warehouse • Distributed Data Warehouse • Data Integration challenges • Masterdata • Transaction data conversion • Data cleansing • Designing for Flexibility • The Support Organization • The top 10 EDW pitfalls • Wrap-up 15

  15. The 3-Tiers of Information Management For all data warehouses 60-80% of the effort is to move, store, retrieve and integrate data from various source systems. From a SAP perspective, Information management is six distinct efforts. Therefore, several SAP BI tools exists with different capabilities 16 16

  16. The XI Data Services Architecture Data integration in an EDW can be done with SAP BOBJ Data Services. The tool architectural can be illustrated in terms of source data, process and target data. 17

  17. Pre-delivered connectors to systems and databases Extraction and data movement may take 30-50% of the time in a process chain. Therefore, do not plan to build an EDW with slow ‘non-native’ connectivity to the source systems.

  18. Reconciliation Between Systems The majority of time spent on maintaining a complex EDW is the time spent on reconciliation of the data You have to prove that the data in the warehouse is equal to the data you extracted, or your financial reporting systems will have no credibility. You are also legally required to have a reconciliation process that can be tracked, if you use the warehouse for financial reporting to external entities.

  19. Reconciliation Between Systems- Dashboards Many companies invest in developing manual control queries, while others use reconciliation products that are powered by SAP NetWeaver An example of a reconciliation Dashboard built on SAP BW. In this example: A reconciliation memo was written on Feb. 1st PCA reconciliation between BW and R/3 failed on Feb. 16th

  20. Interesting use for SAP NetWeaver BI Using BOBJ Data Services you can consolidate data from many source systems, cleanse and integrate them before you send it to SAP BI. This avoids multi-nested DSOs and complex load logic. Source systems - Oracle - JDE - Peoplesoft - Baan - Siebel - Custom - Hyperion - Other. 21 21

  21. Interesting use BOBJ Data Services Using BOBJ Data Services you integrate, cleanse and merge data from source systems during • ECC implementation projects, • Retirement of legacy systems, • Mergers and Acquisitions. Source systems - Oracle - JDE - Peoplesoft - Baan - Siebel - Custom - Hyperion - Other. 22 22

  22. Data Cleansing Capabilities The Data Profile Tab in BOBJ Data Services This tab in the “view data” screen contains data profile statistics on each column that can help you decide on the quality of the input data. The system automatically captures the following statistics in a profile grid. • Column Name • Number of distinct values in a column • Number of records with a NULL value in this column • Maximum & Minimum value of the column 23

  23. Data Cleansing Capabilities The Validation Validation allows you to create rules for cleaning data prior to loading it to the system. You can have a pass rule and an 'Action on Failure' that can provide complex logic. 24

  24. Data Cleansing Capabilities The Audit The Auditing selection allows you to take complex actions when the data quality is poor. You can: • Send an email to an administrator • Load the data to a table for later correction • Modify the data through scripts • Create custom functions for your own processing logic 25

  25. Universal Data Cleansing: Example of Enhanced Party Masterdata You can also add new items such as geocodes for visualization in SAP BI I.e. maps You can add new characteristics to the data such as: • Legal tax jurisdictions • Census track ID • Block group ID • Insurance rating territories • Tax authority name • Tax authority FIPS codes • Longitude & Latitude • City type • ... GREAT FEATURE: The Census track ID allows you to analyze your customers and partners using government census information Source: SAP AG, 2009

  26. Universal Data Cleansing: Customer Aggregating & Discovery A common way to look at customer data is by Households instead of single records. BOBJ DQ allows you to look at customer's addresses and create shared master records, customer mapping keys, aggregating data (i.e. aggregated sales data for the household), check "no-call" lists, examining churn (apparent customer turn-over). You can also integrating all master data from many records into a single "super record" that contains all the unique master data you have about a single customer or partner.

  27. Universal Data Cleansing: Data integration & BAS The Business Address Service (BAS) feature can: • Use Postal reference files from 190 countries to clean address, including suggestion lists • Data scans and searches in SAP for duplicate records using partial user input. SAP Data Quality Management has pre-delivered content for many solutions including CRM -> ECC integration, including: • Across platform search capabilities • Automated address correction • De-Duplication of records • Direct system connection (no file extraction) • Supported for all major releases: R/3 4.6c; ECC 5 and 6; CRM 4 and 5 "Data Quality Management for SAP provides a prepackaged native integration of data quality best practices within the SAP environment using the BOBJ Data Services platform" SAP AG, 2009

  28. What is New in BOBJ Data Services • Expanded matching capabilities to allow the business user to select other fields (beyond street name and zip code) within the generation of break keys. • An improved method to install the functionality of this product into your IC WebClient or CRM IC WebClient environment. To do so, you add a Component Usage to the Component to which you want to add Postal Validation. • If you have purchased the geocoding option for this product, geocoding allows you to return latitude, longitude, and relevant status information for a U.S. address record 29

  29. What We’ll Cover … • Introduction • The EDW architectural options • Federated Data Warehouse • Centralized Data Warehouse • Distributed Data Warehouse • Data Integration challenges • Masterdata • Transaction data conversion • Data cleansing • Designing for Flexibility • The Support Organization • The top 10 EDW pitfalls • Wrap-up 30

  30. SPO in SAP BW 7.2 can Partition Objects Automatically • In BW 7.2 a new feature called "Semantic partitioned object" (SPO) is introduced to help partition InfoCubes for query performance, and DSOs for load performance. • BW 7.2 provides Wizards to help you partition objects by year, business units or products. • BW also generate automatically all needed DTP such as transformation rules and filters to load the correct infoProvider. Source: SAP AG, 2010 • Maintenance is easier since any remodeling only need to change the reference structure. SPOs can be added to MultiProviders for easy query administration and to mask complexity 31

  31. BWA is becoming mainstream and enhanced in BW-7.2 With BW 7.2, you can have data in BWA, InfoCube are not required. Once you exceed a few hundred critical users and/or 3-4 Tb of data you should seriously consider BWA Some of SAP reference clients • BWA is no longer exotic. • Many large SAP-BI customers have already implemented BWA & projects are under way in Europe, Asia and the Americas. 32

  32. Separate the Data Warehouse from the BI solutions IT cannot hold BI ‘hostage’ with long delivery times and slow responses to changing user demands. The only way to be successful is to provide flexible data structures and cleansed, integrated data to the business and let the business groups take over the BI development. So what is needed is a stronger emphasis on scalable, fast IT solutions and a ramp up of BI capabilities of the business units. Keeping BI front-end solutions such as Webi, Visual Composer and Pioneer in the hands of IT instead of the business will create inflexible systems that are unlikely to succeed. 33

  33. What We’ll Cover … • Introduction • The EDW architectural options • Federated Data Warehouse • Centralized Data Warehouse • Distributed Data Warehouse • Data Integration challenges • Masterdata • Transaction data conversion • Data cleansing • Designing for Flexibility • The Support Organization • The top 10 EDW pitfalls • Wrap-up 34

  34. BI Support Organization — Big Picture You need to separate the operations of BI systems from the project work If there is no support organization, the BI system quickly becomes an orphan when the project ends Without a support org. there is a risk that future BI projects are delayed since the project team has to support previous projects

  35. The BI Help Desk — Level 1 Support The first level support should be done by Power Users in the organization You will have to train these resources, empower them to make changes, and leverage them as much as possible, even when it is easy to “jump to solutions” Query related support tickets from a central location/Web site should be routed to the power users in each department. The power user can escalate the ticket to Level- 2 support if he/she is unable to resolve it.

  36. The BI Help Desk — Level 2 Support The second level support is used for issues that are not related to queries, presentations, reports, and formatting This include data loads, performance, security, availability, training schedules, etc. This is addressed by the central support team Some support ticket types are always routed to Level 2 support. It is important to have a generic email address for Level 2 support that is not related to an individual. Emails to this address should not be deleted.

  37. Break-Fix - Splitting Projects & Support Environments Break fix and Production stack The Break-Fix and production stack as well as the training environment is owned by the support team. The project teams own the development and Sandbox environments (BWS and BWD). By Introducing a Break-Fix (BWB) environment, the support team can correct break-fixes and move code into the Testing environment (BWQ) and Production environment (BWP) without impacting the project team Transports can be captured in the buffer and moved to the Development environment (BWD) on a periodic basis BWP BWB BWQ Project Stack Training BWD BWT BWS

  38. What We’ll Cover … • Introduction • The EDW architectural options • Federated Data Warehouse • Centralized Data Warehouse • Distributed Data Warehouse • Data Integration challenges • Masterdata • Transaction data conversion • Data cleansing • Designing for Flexibility • The Support Organization • The top 10 EDW pitfalls • Wrap-up 39

  39. Pitfall #1: Lack of Reasonable SLA with EDW Support Team Some examples of reasonable performance include: • 90% of all queries run under 20 seconds • System is available 98% of the time • Data loads are available at 8am — 99% of the time • User support tickets are answered within 30 minutes (first response) • User support tickets are closed within 48 hours — 95% of the time. • System is never unavailable for more than 72 hrs — including upgrades, service packs, and disaster recovery • Delta backups are done each 24 cycle and system backups are done every weekend

  40. More EDW Pitfalls…. Pitfall #2: Jack-of-all-trades  Master of none…. • BI is complex with many different tools and technologies. Don’t rely on a single person with no specialized skills. • Make each person responsible for a focused technology/task. Pitfall #3: An army of ‘Architects’ who don’t understand SAP. • Have one ‘architect’ – quality is more important than quantity • Architecture is technical by nature. PowerPoints only gets you a small part of the way. • The BI architect should know the technology better than anyone in the room and be able to design solutions.

  41. More EDW Pitfalls…. Pitfall #4: Not separating the Support Team from the Project team • Keeping the ‘lights-on’ is a core focus area. • Many EDWs fail because of lack of training, production and user support, and by having nobody around to do continuous improvements. Pitfall #5: A Firm Belief in Monolithic Data Warehouses • Google runs on over 500,000 servers, why must your data warehouse run on one? • Divide and concur when the performance becomes a too-large problem. • Separate BI onto SAP BWA and use the data warehouse for data movement and data storage. • You don’t need a monolithic castle, but storage & performance

  42. More EDW Pitfalls…. Pitfall #6: Analysis Paralysis. • You will never have perfect EDW requirements – get over it…. • The business will change and so will the BI system. Change is a sign of success not failures (people who cares wants to make it better). • Not moving forward and keep analyzing is a costly decision… Pitfall #7: A Single User Interface will solve all my EDW problems.. • There are no magic bullets. Most companies need 2-3 end user tools. • Start with OLAP (Pioneer) web, then continue with ad-hoc querying (Webi), and finalize with dashboards (Xcelcius). All other tools are great, but not a starting point. • Remember you first crawled and walked before you ran.

  43. More EDW Pitfalls…. Pitfall #8: Enforce EDW Standards • Standards are not a word document buried in a file cabinet • If you allow ‘exceptions’ the standards quickly become meaningless. • It costs to keep your house clean, but data management and data integration will benefit greatly from it. Remember: “the road to hell is paved with good intentions” - unknown. Pitfall #9: Keep Your EDW Support Team motivated • The average application developer stays on the job for 47 months, the average support person is only there for 25 months! • It is very expensive to use the support team as a training ground for technical staff and it hurts performance. • Make the support team a ‘cool’ place to work with flexible hours and defined career paths.

  44. Final EDW Pitfall. Pitfall #10: Not Creating a ‘BI Technology Advisory Board’ for the EDW • Use ad-hoc best practice advise from external experts on an periodic basis. • If you are struggling with something, there are many others who have ‘cracked the nut’ already – leverage their experiences. • Attend BI conferences, take good notes and leverage the many experts at the booths, the speakers and the forums. • You are not alone, but your team needs to get ‘plugged into’ the many ASUG, BI Expert, SDN and SAP BI communities.

  45. What We’ll Cover … • Introduction • The EDW architectural options • Federated Data Warehouse • Centralized Data Warehouse • Distributed Data Warehouse • Data Integration challenges • Masterdata • Transaction data conversion • Data cleansing • Designing for Flexibility • The Support Organization • The top 10 EDW pitfalls • Wrap-up 46

  46. Resources Support Organizations - ppt download with more details http://www.comeritinc.com/Downloads.htm Implementing Enterprise Data Warehousing: A Guide for Executives by Alan Schlukbier Efficient SAP NetWeaver BI Implementation and Project Management by Gary Nolan 47

  47. 7 Key Points to Take Home There are more than one way to architect an EDW. However, you need to make sure your BI solution is designed, not evolutionary Consider FDW and DDWs when data volumes are extremely high or your company just underwent a merger or acquisition Make the front-end independent from the backend Formalize a data integration strategy with MDM and Reconsolidation as key focus areas Invest in people, not just technology –Great support staff is key to EDW success SAP BWA should be part of your EDW strategy unless you are a tiny company Create a BI technology advisory board and have periodic meetings 48

  48. Your Turn! How to contact me: Dr. Bjarne Berg bberg@ComeritInc.com 49

  49. Disclaimer SAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet™, PartnerEdge, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other product and service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP.

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