BP Data Modelling as a Service (DMaaS) presented at DAMA USA (now Enterprise Data World)San Diego

1. This document is the confidential property of BP plc.
All rights are reserved. Copyright © 2006.
Data Modelling as a Service (DMaaS) at Bp
DAMA International, San Diego, March 2008
Christopher Bradley & Ken Dunn

2. Contents
1. Information Architecture challenges at Bp
2. Our solution
a) Self service user administration & provisioning for BP
users
b) Automated Model publishing
c) Detailed reporting of ER/Studio and Repository usage for
user
tracking (and chargeback)
d) Judicious automation
e) Community of Interest
3. Next steps

3. 1. Information Architecture challenges at Bp

4. This document is the confidential property of BP plc. All rights are reserved. Copyright © 2007. 4
BP is an oil, gas, petrochemicals & renewables company
We employ nearly 100,000 people
…operations on 6 continents and in over 100 countries
…market capitalisation of $250 billion
…revenues of $270 billion in 2006
…over 25,000 service stations worldwide
BP Overview

5. This document is the confidential property of BP plc. All rights are reserved. Copyright © 2007. 5
Areas of BP Business
Exploration &
Production
Gas
Refining
Alternative
Energy
Chemicals
LubricantsFuels Marketing
& Retail

6. This document is the confidential property of BP plc. All rights are reserved. Copyright © 2007. 6
DCT (IT) Landscape
Indicative Digital and Communication Technology statistics:
• 250 Data Centers, moving to 3 Mega Data Centers
• 80,000 Desktops – mainly Microsoft XP
• 6,000 Servers – Windows & Unix, some Linux
• 7,000 Applications - target to reduce significantly
• 33 instances of SAP (strategic ERP solution)
• 30 petabytes of spinning disk
• 26 major “data warehouses” (18 SAP BW, 3 Kalido)
• 150 applications independently maintaining Customer data

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7
Characteristics:
• Well-integrated, enterprise-wide global data where appropriate
• A single view of customer and product master data Key Attributes
• Real-time straight-through processing in areas of need
• Overt focus on Data Quality
• Business insights through greater data visibility
• Business ownership with Single Point of Accountability for data
• DCT role in providing leadership, coordination and verification
VISION
Data and Information are effectively and efficiently managed as a shared corporate
asset that is easily accessible.
Information Architecture vision

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8
Data
Types
Master Data MI/BI Data
Transaction
Data
Structured
Technical
Data
Digital
Document
Structure
Models / Taxonomy Catalog / Meta data
Information Architecture Framework
Integration
and Access
Quality
Lifecycle
Management
Process
Governance Planning People
Goals
Principles Purpose
ER/Studio

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9
Challenges: Delivery environment
1. Decentralized management
− IM tools optional including ER/Studio (need to be persuasive)
− no single subject area model (linking entities important)
− few standards but many “guidelines” (modeling guidelines)
2. Project focused
− documentation gets lost in the project repository (drive to put models in
ER/Studio corporate repository)
− continuity of resources is difficult (strong community of interest)
− much project work out-sourced (looking to an accreditation program for
partners)

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10
Challenges: Application environment
3. SAP
− SAP teams may believe that they only need to configure the application, thus
overlooking the importance of modeling
− gaining value from the modeling that is done (cultural change to get SAP team
to actually use models)
3. SOA
− demand for XML model management (ER/Studio used)
− looking for a quick way to turn data into services (using Composite EII
software)
3. Plus 5,000 other applications
− many different overlapping physical models (important to map to logical)
− much integration and ETL work (looking to establish canonical models)

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11
Challenges: Modeling environment
6. Process modeling (ARIS)
− integration with data models important (have completed the logical
mapping of entities, still looking at best way to do integration)
− ensuring that “process modelers” don’t also develop the data models
(quality has shown to be variable!)
6. Architectural modeling (System Architect)
− need to integrate with data and process models (work in early stages)
− confusing for modelers as to which tool to use
− confusing for project teams as to where to find information

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12
Challenges: Publishing
8. PDF
− critical for inclusion in project documentation
− still major communication format
8. SharePoint
− official repository for most projects and architectural documentation
− have automated publication of all models so that they are available to all
project team members
− need good way to publish ER/Studio including zooming in and out!!
8. Wiki environment
− starting to be popular especially for gathering definitions
− need an easy way to keep definitions synchronized with models

13. 13
Prior to 2006
2006 position:2006 position:
• Data modelling undertaken to different degrees in different Segments & Functions.
• Very wide variety of tools & techniques used to define DATA models
− ARIS, ERWin, System Architect, KMDM, Enterprise Architect, Power Designer,
Q_Designer, Rational, PowerPoint, Visio, …… others?
• Most commonly used tool in BP for Data Modelling is PowerPoint / Visio
• Projects encounter common cross Business data concepts, but still create their own
models & definitions.
• No repository of Data Models, nor Governance.
2006 position:2006 position:
• Data modelling undertaken to different degrees in different Segments & Functions.
• Very wide variety of tools & techniques used to define DATA models
− ARIS, ERWin, System Architect, KMDM, Enterprise Architect, Power Designer,
Q_Designer, Rational, PowerPoint, Visio, …… others?
• Most commonly used tool in BP for Data Modelling is PowerPoint / Visio
• Projects encounter common cross Business data concepts, but still create their own
models & definitions.
• No repository of Data Models, nor Governance.
Q4
2005
• Cross BP Data Modelling study – representation from all Segments + Functions.
• Developed agreed requirements statement for data modelling @ BP
• Comprehensive evaluation study
• Established x-BP licence agreements, MSLA & PSA.
• Cross BP Data Modelling study – representation from all Segments + Functions.
• Developed agreed requirements statement for data modelling @ BP
• Comprehensive evaluation study
• Established x-BP licence agreements, MSLA & PSA.
Data quality problems
Inconsistent Data definitions
Duplicated Data
Difficulty in reconciling MI
Models & knowledge lost after each project

14. 2. Our solution

15. DMaaS portal

16. A Service not simply tools!
235 models
50,529 entities
Standards & Guidelines
“How to” guides
Web based
Step by step guides
BP Courses
Online & classroom
Several Video guides
Active FAQ & discussion board
Productivity, quality &
standards macros
Macros wish list
Active COI. Highly
attended & rated

17. a) Self service user administration & provisioning of users

18. • Self service user administration & provisioning
for BP users to:
− register for ER/Studio
− gain repository permissions
− repository password change
− licence server access
− view registered users / managers (&
members) of teams can see who’s
registered
a) Self Service

19. − View registered users / managers (&
members) of teams can see who’s
registered
Self Service

20. • Lets managers know who has registered (or who has not) on their team
• Lets users verify they are registered correctly
• Lets users see other members of the data modelling community at BP
Self Service – Example: View users

21. − Register new user
Self Service

22. Register

23. Self Service – Example: Register New User
1. New user request
submitted from
SharePoint
2. Request received
and validated
against BP Active
Directory
3. User created in
database
4. User created in
Repository
5. User given default
permissions
6. Welcome email sent
= Embarcadero
components

25. b) Automated publishing of models to SharePoint

26. b) Model Publishing
• Publishing of models from repository to BP Data Modelling Environment
SharePoint
− Completely automatic generation of models in HTML (no need to produce
ER/Studio report settings files)
− Usual approach is to utilise report wizard
− Approach would be unworkable for BP’s large # of models
− Automatically generate report settings files
− Customise generated reports
− Layouts, title etc
− Automatic uploading to SharePoint
− Uploading of 1000’s of files to SharePoint is very problematic
− Restart built into our upload jobs
− Report home page in SharePoint mimics repository structure
− Highlights when repository models and SharePoint reports not synchronised
− Publishing meta data to inform users of status

27. Model Publishing - Example
1. Query for updated
models
2. Generate settings
file
3. Generate HTML
version of model
4. Upload HTML to
SharePoint
5. Generate and
update repository
page

28. Model publishing

29. Model publishing

30. Model publishing
Zoom inside the browser!

31. c) User & usage reporting

32. c) User & Usage Reporting
• Detailed reporting of ER/Studio and Repository usage for user
tracking (and chargeback)
• Custom solution
• Database of users
− User department & contact details
− MAC address
− Repository id
• Licence server usage
− Peak number of concurrent users (are we approaching licence limit?)
− Number of unique users registered and using DME (monitor take-up)

33. User & Usage Reporting
Concurrent License Usage
0
5
10
15
20
25
30
35
08May
22May
05Jun
19Jun
03Jul
17Jul
31Jul
14Aug
28Aug
11Sep
25Sep
09Oct
23Oct
06Nov
20Nov
04Dec
18Dec
01Jan
Max Usage
Unique Users
• Log files are copied from the server and parsed
• Usage graph shows peak concurrent license usage and number of unique users for a
given day
• Allows license purchasing decisions to be based on actual usage
• Allows Data Modelling Environment take-up to be monitored

34. d) Judicious automation

35. Generic Import/Export
Makes changes
to the model, e.g.
add entites and
attributes

36. Search Repository
Double-click to
get diagram then
view entity

37. Copy Entity For Re-Use
Select entities
and run the
Copy Entity
macro

38. Copy Entity For Re-Use
Run the Paste
Entity macro in
a new diagram

39. Copy Entity For Re-Use
Run the Entity Re-use
Report macro to see the
list of re-used entities and
their differences

40. Entity Mapping
Define a mapping concept
then check diagram into
Repository – this allows
entities to be mapped to

41. Entity Mapping
Define a mapping concept
then check diagram into
Repository – this allows
entities to be mapped to

42. Entity Mapping
Reference the mapping
concept from the Manage
Mapping Concepts macro
– this creates list
attachments to represent
the mapping

43. Entity Mapping
Generate a Mapping
Report, lists entities (or
submodels) and where
they are linked to

44. Render Stylesheet
Generate a Mapping
Report, lists entities (or
submodels) and where
they are linked to

45. Render Stylesheet
Apply the simple
stylesheet –
everything
becomes white

46. Render Stylesheet
Change
stylesheet, all
entities with an
‘EDM Business
Domain’
attachment
become red

47. Render Stylesheet
Change stylesheet
again, fill colour is
based on
attachment value;
Customers
become blue,
Commodities
become green

49. Validate Data Model
− Data modelling standards and guidelines have been developed.
− Large number of users are utilising ER/Studio (>300).
− No formal process or organisational function to check quality of data
models.
− An automated process (macro) provides a first level assessment of
model quality (i.e. conformance to standards & good practices).
− This does NOT provide any assessment of contentcontent quality – this can only
be accomplished by data model
domain expert review of model.
− Automated populates
the “Validation State” within the
model status block.
− Option to run “statistics only”
report on models in specific
project folders.
BP Model Status
Status: Approved
Type: Project
Validation State: Validated 25/12/2007 73%
Reviewed by: Chris Bradley (BRADC6)
Approved by: Ken Dunn (DUNNKB)

50. e) Community of interest

51. 51
Community of Interest (COI)
• Purpose:
− This CoI is to share business cases, issues, best practices, guidance, project experiences, and propose domain
directives for Data Modelling at BP.
• Why:
− Data Modelling is undertaken at different levels across BP (Enterprise, Conceptual, Logical, Physical, Message).
− ER/Studio is an accepted & supported tool that BP has adopted across the Enterprise
− Several projects are using ER/Studio at BP today and even more in the future
− Avoid project islands, re-inventing the wheel, gather project synergies
• Share “best practices”
• Charter:
https://wss2.bp.com/DCT/EA/teams/EAPublic/GIA/DME/Admin/Community%20of%20Interest/Data%20Modelling%20CO
• Membership:
− The Data Modelling COI is open to all interested BP staff
− Third parties such as consultants and offshore providers may also participate by invitation. Any consultants /
contractors or other 3rd parties participating will have a current NDA with BP.
− Primarily driven by technical demands
• Involvement of Embarcadero:
− Input from Embarcadero
− COI can influence Embarcadero product development though our involvement in PAC
PAC 4th
– 7th
Feb. Key product requests to Christopher.Bradley2@uk.bp.com
• Meeting Frequency and length:
− Monthly – last Tuesday of the month; 90 minutes / online & “real” meeting
• Agenda items:
− Product & DME news, “how to” sessions, user experiences, hot-topic issues.

52. 52
StonglyAgree
Agree
Disagree
StronglyDisagree
79%
77%
70%
55%
60%
4%
0%
10%
20%
30%
40%
50%
60%
70%
80%
User Survey:
What benefits are you gaining from the Data service?
We are not
obtaining any
benefits
We are obtaining
benefit through use of
a common modellingcommon modelling
tooltool
We are obtaining benefit
through utilisation of a
common repositorycommon repository
We are obtaining
benefit through use of
common standards,common standards,
guidelines &guidelines &
processesprocesses
We are obtaining
benefit through re-usere-use
of models &of models &
artefactsartefacts We are obtaining benefit
through provision of
central support & helpcentral support & help

53. 2006 & 2007 - evangelise
53
Governance & management
Best
practices
DM
Tools
Notation
DM
Repository
Common (core)
set of data definitions
e.g. Master DataImplementation
guidelines 200+ users; 8000+ viewers
BP Enterprise model
Conceptual models
Logical models
Physical models
Industry standard models
Template models
235 models
50,529 entities
Top 10 BP reasons for developing data model
1. Capturing Business Requirements
2. Promotes Reuse, Consistency, Quality
3. Bridge Between Business and Technology
Personnel
4. Assessing Fit of Package Solutions
5. Identify and Manage Redundant Data
6. Sets Context for Project within the Enterprise
7. Interaction Analysis: Compliments Process Model
8. Pictures Communicate Better than Words
9. Avoid Late Discovery of Missed Requirements
10. Critical in Managing Integration Between Systems
GET STARTED
Register for ER/Studio license
Training
List of users
Sign up to newsletter
Change repository permissions
Community of Interest
Productivity Macros
Web publication of models

54. 3. Next steps

55. Challenges
• SAP Architects
− “We don’t need to do Data Modelling”
• Selling / promoting purpose of Data Modelling
− It’s NOT just for bespoke database developments!
• Expanding online community of interest
• Certification of internal AND supplier staff
− An “approved” supplier doesn’t necessarily mean they know Data
Modelling!
• Interactive training
• Web portal to interrogate repository
− Develop & promote Business Data Dictionary
• Drive re-use
− Linking model artefacts to drive re-use (e.g. Entities from Master Data
Models)

56. 56
Next steps: 2008 & onwards
SOA:
Important in an SoA World.
Definition of data & consequently calls to / results from
services is vital.
Straight through processing can exacerbate the issue
what does the data mean?
which definition of X (e.g. “cost of goods”)?
need to utilise the logical model and ERP models
definitions
Data Lineage:
Repository based Data migration design - Consistency
Source to target mapping
Reverse engineer & generate Informatica ETL
Impact analysis
ERP:
Model Data requirements – aid configuration / fit for
purpose evaluation
Data Integration
Legacy Data take on
Master Data integration
BI / DW:
Model Data requirements in Dimensional Model
Reverse engineer BW Info Cubes, BO Universes,
etc
Generate Star / Snowflake / Starflake schemas
Message modelling:
Hierarchic view of data model
Canonicals
Utilise “Sub-models” for each XML
message
Generate XSD
Import WSDL
Customise XSD via ER/Studio macros
Very powerful XML features in new V7.5
Approved status of models by ….
Enterprise, Segment, Function
Model validation service
Promotion of “approved” e.g. master data models
Promotion of Industry standard models (e.g. PODS)
Drive quality model culture
Cross domain Governance
Modelling (Data lineage) befits for SOX compliance
Reward re-use
Demonstrate benefits of reuse
Make re-use the default behaviour
Share BP benefits success stories (e.g. GOIL)

57. 57
Questions?
Contact details
Chris Bradley
Head Of Information Management Practice
Chris.Bradley@ipl.com
+44 1225 475000
Ken Dunn
Head of Information Architecture
Ken,Dunn@bp.com
+1 630 836 7805