Presentation is loading. Please wait.

Presentation is loading. Please wait.

What you need to know to get the most out of your Enterprise Data Warehouse Dr. Bjarne Berg Comerit.

Similar presentations


Presentation on theme: "What you need to know to get the most out of your Enterprise Data Warehouse Dr. Bjarne Berg Comerit."— Presentation transcript:

1 What you need to know to get the most out of your Enterprise Data Warehouse
Dr. Bjarne Berg Comerit

2 EDW architectural options
What We’ll Cover … Introduction EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse Data Integration challenges Masterdata Transaction data conversion Data cleansing The IT "Jail" Creating The Support Organization The top 10 EDW pitfalls Wrap-up

3 In this session. We will take a look at the pros and cons of EDW architectural options, including federated, centralized, and distributed EDW models, and explore when each approach is appropriate. See options for consolidating 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. 3

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. KEY CONCEPT: BI applications are not required to run on top of data warehouses, but the majority does 4

5 Before you start: Know your DW Governance model
Many EDW efforts fail, due to the IT governance changes needed to be successful. EDWs rarely succeeds in businesses modeled as federal, feudal, IT duopoly or anarchy. Know your organization before attempting a centralized EDW effort - do you have enough sponsorship to make real changes? 5

6 EDW architectural options
What We’ll Cover … Introduction EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse Data Integration challenges Masterdata Transaction data conversion Data cleansing The IT "Jail" Creating The Support Organization The top 10 EDW pitfalls Wrap-up 6

7 A Logical Enterprise DW Architecture
Metadata Operational Data Store Data Warehouse Source Data Extract Transform BI Applications Functional Area Custom Developed Applications Invoicing Systems Purchasing Data Extraction Integration and Cleansing Processes Purchasing Systems Marketing and Sales Data Mining Translate Corporate Information Segmented Data Subsets General Ledger Attribute Statistical Programs Summation Calculate Other Internal Systems Product Line Derive Summarized Data Query Access Tools External Data Sources Summarize Knowledge Management Architecture Source data Beginning at the left, we have the systems which provide source data. This diagram shows examples of source data systems. Source data can come from legacy systems which have been around years and are typically mainframe based. Source data can come from transactional processing systems which are primarily client/server systems (as opposed to mainframe) and were probably developed within the past 5-10 years. Many of these transactional processing systems are now running packaged solutions such as SAP, PeopleSoft, and Oracle Financials. Source data can also come from external sources, generally purchased databases. Source data can literally come from anywhere, even tape libraries with off line storage. Data extract and transformation system As we build the data warehouse, we decide what data we want to pull out of which sources and literally extract that data into the warehouse. This involves data extraction, transformation, and transfer and replication. Since the data from the different sources was not coded and look the same, these processes provide the means to reconcile those differences  to extract, cleanse, transform, and move the data into a single, integrated database in the warehouse. There are products, or tools, which aid in these processes. Data storage structures Now we need to store the extracted data. There common data storage structures are the operational data store (ODS), the data warehouse, and the data mart. Other names may be used for these structures, and you do not have to have all three. They can be built in different orders, but we recommend having a plan for the ultimate “big picture” before you begin. We will discuss the differences between these data storage structures in detail later, Location Synchronize Data Resource Management and Quality Assurance Source: Bjarne Berg, “Introduction to Data Warehousing” 7

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

9 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

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

11 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

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

13 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

14 Recommendations CDW, FDW and DDW Architectures
14 14

15 EDW architectural options
What We’ll Cover … Introduction EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse Data Integration challenges Masterdata Transaction data conversion Data cleansing The IT "Jail" Creating The Support Organization The top 10 EDW pitfalls Wrap-up 15

16 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. Information management is six distinct efforts. Therefore, several tools exists with different capabilities 16 16

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

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 an ETL Tool like BOBJ Data Services you can consolidate data from many source systems, cleanse and integrate them before you send it to the EDW. This avoids complex logic. Source systems - Oracle - JDE - Peoplesoft - Baan - Siebel - Custom - Hyperion - Other. 20 20

21 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. 21

22 Data Cleansing Capabilities
The Audit The Auditing selection allows you to take complex actions when the data quality is poor. You can: Send an 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 22

23 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

24 customer data is by Households instead of single records.
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.

25 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.

26 EDW architectural options
What We’ll Cover … Introduction EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse Data Integration challenges Masterdata Transaction data conversion Data cleansing The IT "Jail" Creating The Support Organization The top 10 EDW pitfalls Wrap-up 26

27 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 Analysis in the hands of IT instead of the business will create inflexible systems that are unlikely to succeed. 27

28 EDW architectural options
What We’ll Cover … Introduction EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse Data Integration challenges Masterdata Transaction data conversion Data cleansing The IT "Jail" Creating The Support Organization The top 10 EDW pitfalls Wrap-up 28

29 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 29

30 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. 30

31 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 address for Level 2 support that is not related to an individual. s to this address should not be deleted. 31

32 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). BWB BWQ BWP Project Stack Training BWD BWT BWS 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 32

33 EDW architectural options
What We’ll Cover … Introduction EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse Data Integration challenges Masterdata Transaction data conversion Data cleansing The IT "Jail" Creating The Support Organization The top 10 EDW pitfalls Wrap-up 33

34 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 34

35 Pitfall #2: Jack-of-all-trades  Master of none….
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 Technology. 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. 35

36 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. You don’t need a monolithic castle, but storage & performance 36

37 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, then continue with ad-hoc querying, and finalize with dashboards. All other tools are great, but not a starting point. Remember you first crawled and walked before you ran. 37

38 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. 38

39 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 EDW and Technology on-line communities. 39

40 EDW architectural options
What We’ll Cover … Introduction EDW architectural options Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse Data Integration challenges Masterdata Transaction data conversion Data cleansing The IT "Jail" Creating The Support Organization The top 10 EDW pitfalls Wrap-up 40

41 Resources Fundamentals of Data Warehouses
by Matthias Jarke, Maurizio Lenzerini, Yannis Vassiliou, and Panos Vassiliadis Implementing Enterprise Data Warehousing: A Guide for Executives by Alan Schlukbier 41

42 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 Create a BI technology advisory board and have periodic meetings 42

43 How to contact me: Dr. Bjarne Berg BergB@LRU.edu
Your Turn! How to contact me: Dr. Bjarne Berg 43

44 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.


Download ppt "What you need to know to get the most out of your Enterprise Data Warehouse Dr. Bjarne Berg Comerit."

Similar presentations


Ads by Google