Business Transformation: US EPA Enterprise Data Management

1. Business Transformation: US EPA Enterprise Data Management Brand Niemann Senior Enterprise Architect July 7, 2008

2. Disclaimer • The author is assisting the Data Architect in the Office of Environmental Information as part of his PARS and while these thoughts have been discussed with the Data Architect they are his alone and do not reflect those of the Data Architect or the Agency. They are solely for the purpose of planning an Agency Community of Interest/Practice meeting.

3. Overview • Enterprise Architecture is evolving to Business Transformation to demonstrate value in actually changing the business processes of organizations. • Data Architecture is evolving to a more comprehensive activity to support business transformation in actually doing model-driven data applications for organizations.

4. Business Transformation Best Practices • DoD Business Operating Environment Supporting Business Transformation, Dennis Wisnosky, Business Mission Area Chief Architect and Chief Technology Officer, Department of Defense. • BrainStorm DC Training, June 24, 2008. • 5th SOA for E-Government Conference, April 30-May 1, 2008.

5. Business Transformation Best Practices • Framework: • Architecture - collection of views • View - presentation of reality as a model • Model - representation of concepts in language • Language - construction of vocabulary and grammar • Vocabulary - collection of terms (e.g., next slide for more details) • Grammar - rules for combination of terms Source: DoD Business Operating Environment Supporting Business Transformation, Dennis Wisnosky, Business Mission Area Chief Architect and Chief Technology Officer, Department of Defense. BrainStorm DC Training, June 25, 2008.

6. The Vocabulary & The Knowledgebase • A vocabulary is a machine readable, formal description of the meaning of “things” in a domain, to facilitate understanding and interoperability amongst systems, services, and participants. • Seriously engineered artifact! • No short-cuts! • Must enable query, not just search! • A knowledge base is a collection of facts that can be queried using any vocabulary. • The unique aspect of a knowledge base it that the facts are independent of any schema providing for extensibility and continual change. • Database = Facts + Schema • Knowledgebase = Facts queried thru any Vocabulary. Source: Source: Michael Lang and Dave McPherson, Vitria Technologies, Inc., and Brooke Stevenson, Spry Technologies, Inc. SOA for E-Gov Pilot: Using BPM software to govern a collaboration process used to define software services, April 30, 2008.

7. Data Architecture for Business Architects Best Practices • Ken Orr, Founder and Chief Scientist, The Ken Orr Institute: • Business Semantics, Enterprise Architecture and Data Architecture: Bringing Everything Together, DAMA International Symposium & Wilshire Meta-Data Conference, May 17, 2008. • Data Architecture for Business Architects, BrainStorm DC Training, June 26, 2008.

8. Data Architecture for Business Architects Best Practices • Bringing together the following: • Traditional data modeling (David Hay) • Ontologies (John Sowa and Arun Majumdar) • Semantic Web (Jim Hendler and Dean Allemang) • Using Web 2.0/3.0 Wikis for bookkeeping and collaboration on all of this that would have the added advantage of attracting younger people to this area. • Core Enterprise Services to the Tactical Edge (CESTE), Department of the Army using the Revelytx Knoodl Semantic Wiki (Brooke Stevenson)

9. Background • Data Architect in the Office of Environmental Information: • On the job for about 1 ½ years. • Supported by SRA, PPC, and Booz Allen with funding interruptions. • Two collaboration spaces setup and one meeting: • August 7, 2007, January 30, 2008, and July 17-18, 2007, respectively. • Two Web 2.0 Wikis created to advance the collaboration to support a “Metadata Summit” in the summer of 2008 (see next slides). • Challenge to maintain funding and interest of senior management in an election year.

10. EPA Metadata:Helping people use EPA data tocollaborate and built applications • Our initial objective is to see if this Web 2.0 Wiki can be useful in bringing about collaboration across Data Architecture (see separate Wiki). A longer range goal is to see if this Web 2.0 Wiki could be used as an Enterprise Metadata Management and Application Development Tool (e.g. data and metadata mashups).

11. EPA Web 2.0 Pilot Metadata Wiki http://epametadata.wik.is/

12. EPA's Enterprise Data Architecture (EDA) Program • EPA's Enterprise Data Architecture (EDA) program is designed to optimize the value of enterprise data assets by improving accessibility, reusability, reliability, and overall quality. The program employs a structured approach to incorporate insight from Agency practitioners and leverage other Federal best practices to provide the Agency the best possible data support.

13. EPA Web 2.0 Pilot Data Architecture Wiki http://epadatarchitecture.wik.is/

14. Recent Suggestions to theOEI Data Architect • Document everything you have learned about EDA for continuity and to assist others in helping you. • Use the existing Web 2.0 Wikis or just create it in Word and it can be transferred to the Wiki. • Focus the limited contract resources during the remainder of this fiscal year on bringing together data modeling, ontology, and Semantic Web using Web 2.0/3.0 to support the Gov 2.0 “democratizing data” initiative. • This also supports our CIOs National Dialogue to improved access to environmental information currently under way. • Take the enterprise view that all the Segment Architectures gives us the “ontology” (context) for doing the data modeling (which the Segment Data Architects should do for their segments and then share them with all the others), and then we can collaboration – semantic Web enable this using the Web 2.0/3.o Wikis and other tools (e.g. see http://www.semanticexchange.com): • See new Water Wiki Wikis where this is being done for water data across the multi-levels of government and scale to support indicators and assessments for decision making.

15. Recent Suggestions to the EA Team Lead • This work would also support the new “July EA Tasks” as follows: • Task 2. Business Transformation Frameworks: • Jose and Larry • Task 3. EA Solution Architecture Data Analysis: • Eric and Rachel • Task 4. Data Architecture for Enterprise Search: • Kevin and Jose • I wrote a paper on this last year in my online book on a New Enterprise Information Architecture and Data Management Strategy for EPA and the Government (simply put then just get a Google appliance and now even better use the new semantic middleware that has been developed - e.g. see http://www.semanticexchange.com that I am using in my other work on semantification of EPA content). Once we get it into a Web 2.0 Wiki then there is a clear path to doing this! See http://epa.wik.is.

16. Suggested Plan for Summer 2008 Metadata Summit • Call it Business Transformation: US EPA Enterprise Data Management Workshop • This is much bigger that metadata! • Organize it ourselves, but invite the contractors supporting us and others to participate by posting comments to our Web 2.0 Wiki. • Use the “semantic model basics” to organize and structure the agenda: • Concept, Definition, and Specific Example • Distribute the “semantic model basics” in the Web 2.0 Wiki beforehand to develop general consensus before the workshop.

17. Suggested Agenda • Welcome and Opening Remarks: • Kevin Kirby, OEI Data Architect • Brief Introduction to the Web 2.0 Wiki Supporting this Workshop • Brand Niemann, OEI Senior Enterprise Architect • Brief Introduction of Participants: • All present and remote • Selected Presentations for Semantic Model Basics Matrix: • See initial draft • Initial Discussions of a Series of Collaborative Pilot Projects for Semantic Model Basics Matrix • Lunch Discussions (catered if possible so do not have to break up the networking) • Building Consensus on a Series of Collaborative Pilot Projects for Semantic Model Basics Matrix: • Facilitator or someone from the participants that could do this well. • Closing Remarks and Adjourn: • Select someone who is good at summarizing using the Web 2.0 Wiki content documenting the meeting.

18. EPA EDA WorkshopSemantic Model Basics Matrix Note: We (contractor) can/should do short white papers on each of these.

19. Selected Notes from Ken Orr Training • What is Data Architecture?: • An Enterprise View of Data: • Data Architecture means looking at all of the information (data) in the enterprise in a structured fashion, even the “unstructured data”. • Data Architecture means working to make it easier to bring key information about the same real-world entities and transactions together. • Note: Does this sound like what we are doing in the Web 2.0 Wikis with Mashups?!

20. Selected Notes from Ken Orr Training • Enterprise Architecture Stack: • Business Architecture • Data/Information Architecture • Application Architecture • Technology Architecture • The Roles Enterprise Architects Play: • Master Planner • Building Inspector • County Agent (Mentor, Trainer, Consultant)

21. Selected Notes from Ken Orr Training • Data Architecture is analogous to Product Architecture: • How do we design our data so that we can produce more and more data products faster and cheaper. • Most people think Data Warehouses and Data Marts • Note: EPA Experience has shown that we should have built more data (web) services (former CIO Kim Nelson).

22. Selected Notes from Ken Orr Training • The Key Elements of Semantic-Based Business Architecture: • Business Context (which leads to Business Semantics and Information/data Architecture) • Business Value and Value Streams (which lead to Business Processes) • Business Process Identity Activities (which lead to Data Requirements)

23. Selected Notes from Ken Orr Training

24. Selected Notes from Ken Orr Training • High Level Semantic Models: • Answers to the six journalistic questions • Resource, Event, Activity Model • AMO Semantic Model: Actor, Message, and Object • Data Quality in data models: • Ultimately, data quality is a function of usage, not collection or processing…the more you use data the better it gets, the less you use data the worse it is…data that is never used cannot be correct. • The same is true with data models, if you don’t use them you can’t be sure they’re any good.

25. Selected Notes from Ken Orr Training • AMO Time Line: • Pre-1980: context diagrams, data base design strategy • Post 1980: “functional flow” business processes (follow the messages), and automatic db design • Post 1990: “actors”, “objects” and “messages” in DB, and semantic DW/DM design • Post 2000: “objects”, “subjects”. “events”, “roles”

26. Selected Notes from Ken Orr Training • Context Diagrams Contain Only 3 “Explicit” Components: • Context: • What is this system/application about? • Actors/Roles are.. • Individuals or people • Organizations (of people) • Systems • Roles are actors in a specific context • Messages are… • Communication sent from one actor to another • Things sent from one actor to another

27. Selected Notes from Ken Orr Training • Business Semantic Categories and Physical Data Representations: • Actors/Roles (“who”) • Master Files (Customer) • Tables with unique keys • Object or Subjects (“what”) • Master Files (Product) • Tables with unique keys • Messages (“how”) • Transaction Files (Order) • 2 level tables with foreign keys • XML • Events/Time (“when”) • Parameter Files • Locations and Context (“where”) • Goals/Motives (“why”)

28. Selected Notes from Ken Orr Training • Semantic-based Data Architecture: • Data Modeling • Master Data Management • Enterprise Data Architecture • Enterprise Data Flow Architecture • Putting Data in SOA: • Process Bus - Enterprise Process (Workflow) Meta-data • Application Bus - Enterprise Service/Business Rule Meta-data • Data Bus – Data Meta-data

29. Selected Notes from Ken Orr Training • Information (Data) Architecture Assertions: • The information architecture is the most important of all the various architectures from an IT asset standpoint. • The Information Architecture is the anchor for the Enterprise Architecture. • The non-structured components of Information Architecture are growing much faster than the structured data. These non-structured (or quasi-structured) components include: • Email • Documents • Attachments • Voice and video • Web content • Note: This is why I am doing a Data Architecture for the Enterprise Architecture in a Web 2.0 Wiki!

30. Selected Notes from Ken Orr Training • Enterprise Data (Flow) Architecture: • Created to provide IT architects with a framework for understanding what Data Warehousing/Data Marts were all about. • One of the ongoing debates centers around the definitions of each and their roles in the Data Architecture: • Data Warehouse – a subject-oriented, integrate, time-variant and historical collection of summary and detailed data (nonvolatile and relevant to a point in time) used to support the strategic decision-making process. • Data Mart – contains data structured for analysis and historical reporting for a specific purpose and is sourced via the ELTP hub with data from one data warehouse or an SOR database. • ELTP • SOR – System of Record provides a “certified”, “standard”, “enterprise”, “shared”, set of data. • Getting data out of SORs is a whole lot harder than getting it in.

31. Selected Notes from Ken Orr Training • Where Do Data Models Come From?: • The key elements in all good data models ought to come from the business modeling. • The better the business model/requirements, the easier it is to create good data models. • Simple data models take a lot of work, complex ones are much easier to produce. • What is Master Data Management?: • An new name for an old problem: • One view of a customer, product. etc. • “Virtual” data Warehousing • Major vendors (IBM, Oracle, SAP, etc.) are selling “Hub” products.

32. Selected Notes from Ken Orr Training • Future Trends: • Collaboration • Business Semantics and State-of-the-Art (CAD/CAM) Tools (ARTech, Ltd.): • Automatic DB Design and Application Generation • Workflow • Data Warehousing and Business Intelligence • Semantic Category Patterns • Note: This is where doing all of this in a Web 2.0/30 Wikis makes great sense!