G. Philip Rogers, PMP

1
Building the Business Case for Metadata in the
Enterprise Looking At Models, Architectures, and
Business Processes As Building Blocks for Cost
Benefit Analysis and ROI

• G. Philip Rogers, PMP
• Senior Business Analyst, School of Public Health,
  Instructional and Information Systems, UNC Chapel
  Hill
• g.philip.rogers_at_unc.edu
• Doctoral Student, Information Science, UNC
• http//www.ils.unc.edu/gerogers/

2
About Me

• Professional experience. Over the past 20 years,
  worked in Business Analyst, Project/Program
  Management, Technical Communications, and
  Management roles (before joining UNC, worked for
  Cisco Systems, Web startup, Intel Corporation,
  USAF).
• Academic interests. Doctoral student in UNCs
  School of Information and Library Science
  academic interests include metadata
  interoperability, Semantic Web, Business
  Intelligence, and the role of IT as an enabler
  for addressing research challenges.

3
Corporate Circle Goals

• The goals of the Global Corporate Circle are to
• Promote the use of the Dublin Core standard by
  enterprise organizations/corporations for both
  internal and external information.
• Coordinate with developers and information
  providers to ensure interoperability with
  enterprise-wide applications.
• Develop a body of work which provides best
  practices, case studies and examples of how
  Dublin Core is implemented and its' value to the
  organization. Examples can include what elements
  are used, how they are interpreted for the
  organization, values/controlled vocabularies
  developed and the return on investment (ROI) of
  metadata, specifically Dublin Core, for a
  company.

4
DCAM and Its Applicability to Business Process
and EA Modeling

• Pulis Neville propose a UML-compliant model of
  the DCAM as a means of moving toward the
  development of a UML meta-model so that UML can
  be used to develop DC-conformant Application
  Profiles.
• Perhaps additional modeling languages could be
  considered as a basis for additional
  DC-conformant Application Profiles, as a means of
  enhancing interoperability.

5
Do You Know Where Your Data Is?

• And what would you do with it if and when you
  find it?
• If someone were to ask you, in a work context,
  Hows it going? what would your answer be?
• The key to answering this question is of course
  defining it.
• Financial results (profitability, market share)
• Customer-related metrics (satisfaction, loyalty)
• Quality measures (defects, discrepancies)
• Performance measures (employee productivity)

6
Working Definitions of Metadata

• Semantic layer between IT systems and business
  users (McComb)
• Structured information that describes, explains,
  locates, or otherwise makes it easier to
  retrieve, use, or manage an information resource
  (NISO)
• Metadata is the information and documentation
  which makes data understandable and shareable for
  users over time. Data remain useable, shareable,
  and understandable as long as the metadata remain
  accessible. (ISO/IEC 11179-1)

7
Enterprise Metadata Management

• Enterprise metadata management should provide
  insight into
• What data exists
• Where data is being used
• How data is labeled and referenced
• How data is related to other information assets
• Who uses the data
• Why the data is needed
• When the data was last accessed or updated

8
The Changing Role of Metadata

• The role of metadata has been transformed it
  has gone from being an afterthought to being an
  architectural principle (McComb)
• Metadata plays a critical role in investments in
  data warehousing, data mining, business
  intelligence, customer relationship management,
  enterprise application integration, and knowledge
  management (to name some of the big-ticket items
  in which enterprises have invested over the past
  5 to 10 years).

9
Framing the Question

• Because the term metadata is abstract and not
  widely understood in a corporate environment,
  asking someone how they use metadata in their
  job is a question many people will struggle to
  answer.
• Asking someone to describe their job, such as the
  systems and tools that they use, and to what
  extent and how they might use the data entered in
  those systems and tools, should make it possible
  to make deductions about the role metadata plays
  in their job and in their organization.

10
Solutions Intended to Address Gaps In Systems and
Processes

• ETL. Extract, Transform, Load (reading data from
  one database, performing transformations on the
  data so that that it can be read in a different
  database, and writing the transformed data to the
  target database)
• Data warehouses, data marts. Provide a central
  repository and enable data mining.
• Middleware. Hides inconsistencies in underlying
  architectures (recent examples include web
  services integration, enterprise service buses) .

11
Problems with Scoping and Justifying Metadata
Projects

• Projects where metadata is the central component
  often are not successful, because they are not
• Driven by a distinct and evident business need
• Clearly defined
• Based on achievable or measurable goals
• Properly resourced, both during and after the
  project is completeMetadata repository projects
  are prone to failure because their contents are
  not sufficiently integrated across the enterprise
  for example, they are not fully compatible with
  ETL or data integration applications. Focusing
  too much attention on the metadata itself, as
  opposed to accomplishing clear goals with
  metadata, can be a costly mistake.

12
Challenges Associated with Metadata Repositories

• The vast majority of metadata repositories
  are unidirectional. Modeling tools that extract,
  transform, and load information into the
  repositories are responsible for capturing both
  business (business metadata) and IT (technical
  metadata) meta information flow in only a single
  direction Many enterprise tool vendors are
  trying to solve this particular problem, but an
  organization would need to fully embrace the
  metadata repository approach for it to work and
  could not adopt it in bits and pieces. The
  repository will be required to store the latest
  version of the metadata source in which it will
  propagate changes. Concurrency issues will arise
  in this situation and integration interfaces
  will have to be constructed to map and move
  metadata repository information back and forth to
  the metadata source.
• - McGovern

13
Has Something Like This Ever Happened Where You
Work?

• A large amount of money is allocated to building
  a data warehouse. Despite management support and
  ample funding, the initiative fails mainly due to
  inflexible business processes and lack of access
  to or understanding of critical business data.

14
The Need For Metadata Management Tools AND
Frameworks

• A metadata-driven framework is MANDATORY to
  enable companies to understand the different
  forms, types, and definitions that common data
  elements share with each other. It is important
  to maintain the distinction between managing
  metadata through a generalized metadata tool
  versus having a metadata-driven framework
  designed for a specific purpose, such as
  supporting customer data integration and
  master/reference data management. In my
  experience, the most successful companies combine
  'best practices' from both approaches.
• - Anurag Wadehra, VP of Marketing,
  Siperian

15
Metadata Management Maturity Curve
16
Observations About Enterprise Behavior

• Profit-driven enterprises are often heavily
  influenced by short-term, one-quarter-at-a-time,
  tactical thinking.
• Business justification for individual projects
  tends to be driven by short-term needs.
• Developing business cases and calculating ROI for
  long-term investments is very difficult in such
  an environment.

17
The Executive Sponsors Dilemma

• A certain project is expected to produce
  benefits, but will require a capital investment
• That same capital could be invested elsewhere,
  potentially producing a different set of benefits
• How does the sponsor decide which project or
  projects to invest in?

18
Building the Business Case

• Building a business case typically includes steps
  such as the following
• Estimate future expected costs
• Estimate future expected benefits
• Determine implied return
• Compare implied return to alternatives

19
The Cruel World of Estimation

• Projections of expected costs and benefits are
  often educated guesses, at best, particularly for
  software projects.
• The greater the degree of uncertainty (risk)
  about a potential project, the higher the
  expected rate of return to justify the project.
• A preliminary business case may be only a first
  step preceding additional analysis, understanding
  of requirements, and preparation of a more formal
  business case. The assumption is that investing
  additional time should reduce uncertainty (risk).

20
Estimating Costs and Benefits

• When estimating costs
• Level of cost is directly proportional to
  complexity of requirements
• Uncertainty about costs reduces as requirements
  are refined
• When estimating benefits
• An existing problem is solved or at least
  mitigated in order for the expected benefit to
  materialize
• The problem and the cost of living with the
  problem are well understood

21
Standard ROI Calculation

• ROI is often calculated as the average
  benefit over a specified time period divided by
  the cost.
• That is,
• Given the sum of the costs
• Given the sum of the benefits
• Given other parameters
• Then the ROI can be computed in a number of
  ways.
• However, the calculation of costs and benefits
  is not always based on realistic data, and under
  what is often tight schedule pressure,
  insufficient time is typically allocated for the
  preparation of business cases and similar
  deliverables.

22
Strategic Perspectives on Enterprise Data
Management

• Understanding enterprise business processes is an
  essential part of strategic thinking.
• In order to help the enterprise attain its goals,
  enterprise architecture must be aligned with
  enterprise business processes.

23
Business Process Definition

• Set of business events that enable the delivery
  of an organizations products or services to its
  customers. Categories for business processes
• Information processing of data within and
  movement of data among systems
• Operations individual contributors, equipment,
  operational policies and procedures
• Management managers, authority, organizational
  dynamics, management policies and procedures

24
Business Process Modeling

• A business process
• 1. Has a Goal
• 2. Has specific inputs
• 3. Has specific outputs
• 4. Uses resources
• 5. Has a number of activities that are performed
  in some order
• 6. May affect more than one organizational unit.
• 7. Creates value of some kind for the customer
  (internal or external).

25
Potential Business Process Focus Areas

• Generalized (broadly applicable)
• Business Intelligence/Knowledge Management
• Content Management
• Enterprise Resource Planning
• Portfolio Management
• Customer Relationship Management
• Specialized (industry-specific)
• Academia/government (grant-funded research)
• Financial services (investment banking)
• Health care (patient health records)
• Libraries/archives (digitization)
• Pharmaceuticals (clinical drug trials)
• Semiconductors (microprocessor design)

26
Enterprise Architecture (EA) Definition

• Principles, methods, and models that shape the
  organizational structure, business processes,
  information systems, and infrastructure of an
  enterprise.
• Enterprise architecture captures the
  essentials of the business, IT and its evolution.
  The idea is that the essentials are much more
  stable than the specific solutions that are found
  for the problems currently at hand. Architecture
  is therefore helpful in guarding the essentials
  of the business, while still allowing for maximal
  flexibility and adaptability. Without good
  architecture, it is difficult to achieve business
  success.
• - Lankhorst

27
EA Views

• Business architecture. Shows how business is done
  -- models the enterprise using business processes
  and the events that trigger them.
• Information (data) architecture. Enables the
  enterprise to develop a shared, distributed,
  consistent data resource -- consists of data
  models and databases that serve all participants
  in the enterprise business environment and the
  strategies, standards, policies required to
  develop and implement them.
• Application architecture. Supports business
  processes, provides automated solutions, manages
  information storage and retrieval, links the Data
  and Business architecture.
• Technology (infrastructure) architecture. Meets
  the infrastructure needs of business clients --
  interoperates with and supports the Application,
  Business, and Data Architectures to provide
  interoperable technology platforms.

28
EA View Components
29
Architecture Stack
30
Sample Business Architecture
User interaction
Order
Quote
Track
Pay
Support
Process integration
B2B
Business policies/rules
Pricing
ERP
SC
HRM
CRM
Transaction processing
Business Intelligence
CustData
Data
Data
Data
Data management
31
Metadata Requirements Stack
32
EA Governance Categories and Instruments
33
EA Governance Instruments

• Strategic Management (BSC). Emphasizes a balanced
  approach (traditional management focus is on
  finances) based on customer, financial, business
  process, and learning/growth perspectives.
• Strategy Execution (EFQM). Inspired by Malcolm
  Baldridge (USA) and Deming (Japan), provides
  management framework for performance excellence.
• Quality Management (ISO 9001). Focuses on
  integrated design, management, and documentation
  of business processes and supporting IT systems.
• IT Governance (COBIT). Provides control
  objectives and management guidelines for 34 IT
  processes. Also provides IT governance maturity
  model.
• IT Service Delivery and Support (ITIL). Provides
  set of best practices and training materials for
  IT service delivery.
• IT Implementation (CMM/CMMI). Model for
  evaluating maturity of software development
  processes.

34
Finding Data Where It LivesSemantic Elicitation
from Processes

• Long duration business transactions (LDBTs) are a
  valuable source for uncovering semantics in
  business processes (workflows).
• A good place to start for any enterprise is the
  predominant flow typically a flow that occurs
  frequently, has significant cost implications,
  and is central to the core mission of the
  enterprise.
• Looking at variations in the primary flow,
  whether the variations make business sense, and
  whether they merit a time investment can yield
  valuable information about how to manage critical
  business data (McComb).

35
On Data Governance

• Due in part to relatively recent business drivers
  related to compliance such as Basel II and
  Sarbanes-Oxley, data governance is an area that
  is seeing substantial enterprise investment.
• Data governance seeks to ensure that there is a
  management framework that can deliver
  availability, usability, integrity, and security
  of enterprise data. Such a framework should
  include a governing body, a defined set of
  procedures, and a plan to execute those
  procedures.

36
Why Metadata for Governance?
37
Business Imperatives Driving Data Governance

• Agility (ability to respond more quickly)
• Simplification (reduce unnecessary complexity,
  and ideally, costs)
• Rapid increase in the volume of information
• Rapid business growth
• Geographic dispersion (due to outsourcing and
  other factors)
• Compliance

38
Metadata Uses in Data Governance

• Strategic (data stewardship information reuse
  information management data integration
  strategy)
• Tactical (project flexibility and adaptability
  portfolio management)

39
Architecture (BPM, Metadata Repository, and EA)
Tools

• Tools that at least partially address the
  central EA challenge of representing enterprise
  information and technology portfolios
• BPM (top down) tools
• ARIS (IDS Scheer) Corporate Modeler (Casewise)
  MEGA International Software Suite ProVision
  (Proforma).
• Metadata repository tools
• Architecture Manager (Adaptive Enterprise).
  MOF-compliant repository that integrates with
  many modeling tools. http//www.adaptive.com/produ
  cts/eamanager.html
• Rochade (Allen Systems Group). Provides
  publication, visualization of models CWM
  support. http//www.rochade.com/index_flash.html
• EA Tools
• Architect (BiZZdesign) Enterprise Framework
  (Ptech) Metis (Computatis) System Architect
  (Popkin) Troux 4 (Troux Technologies).

40
Metadata Extraction Tool Example Saphir

• Saphir (Silwood Technology) is a tool that reads
  the data structures of Peoplesoft,, SAP (BW
  mySAP ), Siebel, and JD Edwards databases and
  extracts the definitions and relationships of the
  tables and columns, which can then be exported
  into tools such as ERwin, PowerDesigner, Popkin
  System Architect, or Visio.
• Data warehouse designers, reporting teams and
  data architects use this powerful application to
  analyse their data requirements from the key
  enterprise applications. Saphir helps you take
  control of your data management projects as you
  strive to understand exactly where vital business
  information is stored.
• http//www.silwoodtechnology.com/saphir.htm

41
Saphir (continued)
42
Conclusion
43
Facilitating Interoperability An EA/BPM
Perspective

• Metadata interoperability projects have generally
  been based on one of the following approaches
• Application profiling/schema customization
• Derivation (e.g., MODS and MARC Lite are derived
  from MARC21)
• Crosswalking/mapping
• Switching schema (e.g., OAI)
• Lingua franca (set of core attributes derived
  from multiple schemas)
• Metadata framework/container (e.g., RDF, METS)
• For EA/BPM, possible areas for further research
• Survey individuals working in areas such as EA,
  business process modeling (Architects, Business
  Analysts)
• Crosswalk of frameworks/models, leveraging
  GRAAL framework for conceptualizing and comparing
  IT architectures and Value-Based IT Alignment
  (VITAL) approaches
• Business process model (flow) registry
• Further application profiling (e.g,
  BPML-compliant model of DCAM)

44
Formulating a Business Case for Enterprise
Metadata Management

• What is driving investment in projects and
  initiatives -- Organizational needs? Business
  requirements? Technology demands?
• What are the main limitations in the use of
  information? Inappropriate organizational
  structures? Cumbersome business processes?
  Outdated technologies?

45
Simple Information Assessment

• Evernden Evernden information diagnostic
• There is a clear and distinct vision of
  information as a corporate resource
• There is an organization unit responsible for
  information and knowledge that is distinct from
  the information technology function
• There is a well-defined strategy and action plan
  for improving the effectiveness of information
  use across the organization
• Information that is vital and necessary to make
  key decisions is always readily and easily
  available
• All information is available in a consistent and
  integrated format
• Management believes that there is considerable
  value to be gained from the organizations use of
  information
• Information management is seen as the
  responsibility of business people as well as the
  information technology functions
• Information has a key role in all business
  processes
• Financial approval is readily available for
  investment in the information infrastructure of
  the organization (as opposed to technology
  investments)
• Information is used to support innovation and
  creativity in product and service development,
  business processes, and customer support
• Total Score

46
Surveying EA/BPM Practitioners

• Questions that might yield insight via a survey
  (or similar instrument), possibly using an
  approach such as the COBIT IT Maturity Model,
  could focus on areas such as
• Enterprise data warehouse or metadata repository
  initiatives attempted or planned
• Modeling frameworks or tools being used
• Extent to which business processes are understood
  and documented
• Extent to which EA aligns with business processes

47
EA Frameworks and Business Process Models Is
There a Role for Dublin Core?

• Pulis Neville have already proposed a
  UML-compliant model of the DCAM how might this
  model be leveraged as part of the larger OMG
  Model-Driven Architecture (MDA), which includes
  the Meta Object Facility (MOF) and the Common
  Warehouse Meta-model (CWM)?
• Other frameworks and models that appear to have
  traction in the U.S. are The Open Group
  Architecture Framework (TOGAF), the Business
  Process Modeling Initiative (BPMI), and the
  Federal Enterprise Architecture Data Reference
  Model (FEA DRM).

48
Selected References

• ANSI X3.285, Metamodel for Management of
  Shareable Data metadata-stds.org/Document-library
  /Draft-standards/X3-285-Mgmt-of-Sharable-Data/X3-2
  85.PDF
• Cook, M. (1996). Building Enterprise Information
  Architectures Reengineering Information Systems.
  Prentice Hall.
• Evernden Evernden (2003). Information First
  Integrating Knowledge and Information
  Architecture for Business Advantage. Elsevier.
• Finneran, T. (2003). Enterprise Architecture
  What and Why. http//www.tdan.com/i007ht03.htm
• ISO/IEC 11179-1. Specification and
  standardization of data elements - Part 1
  Framework. metadata-stds.org/metadata-stds/11179/
• IT Governance Institute (2006). COBIT 4.0.
• Lankhorst, M., et al. (2006). Enterprise
  Architecture at Work Modelling, Communication,
  and Analysis. Springer.
• McComb, D. (2004). Semantics in Business Systems
  The Savvy Managers Guide. Morgan Kaufmann.
• McGovern, J., et al. (2004). A Practical Guide to
  Enterprise Architecture. Prentice Hall.
• NISO. Understanding Metadata. http//www.niso.org/
  standards/resources/UnderstandingMetadata.pdf
• Silverston, L. (2001). The Data Model Resource
  Book, Revised Edition, Volume 2 A Library of
  Universal Data Models by Industry Types. Wiley.

49
Backup Slides
50
COBIT Maturity Model for IT Governance

• The Control Objectives for Information and
  related Technology (COBIT) for IT governance,
  first published in 1996 by ISACA, along with
  control objectives and management guidelines for
  34 IT processes, also includes an IT governance
  maturity model.
• The maturity model has five levels, from the
  lowest (Ad Hoc) level where there are no
  standardized processes, to the highest
  (Optimized) level, where processes have been
  refined to the level of external best practices.

51
COBIT Maturity Model for Internal Control
Maturity Level Meaning
0 (non-existent) Complete lack of recognizable processes organization does not acknowledge that there are issues to be addressed.
1 (Initial/ad hoc) Organization recognizes that issues exist no standardized processes - ad hoc approaches applied on a case by case basis overall management approach is disorganized.
2 (Repeatable but intuitive) Similar procedures are followed by different people undertaking same task no formal training or communication on standard procedures responsibility is left to individual high degree of reliance on the knowledge of individuals.
3 (Defined process) Standardized and documented procedures, communicated through training still left to individual to follow processes procedures are non-sophisticated based on formalization of existing practices.
4 (managed and measurable) Compliance with procedures can be measured/monitored and action taken where processes are not working effectively processes are under constant improvement automation and tools are used to a limited extent.
5 (Optimised) Processes refined to level of best practice, based on the results of continuous improvement and maturity modelling with other organisations IT employed in integrated way to automate workflow and provides tools to improve quality and effectiveness.
52
Business Process Analysis A Key To Gaining
Insight Into Organizational Data

• An operation is composed of processes designed to
  add value by transforming inputs into useful
  outputs. Inputs may be materials, labor, energy,
  and capital equipment. Outputs may be a physical
  product (possibly used as an input to another
  process) or a service. Processes can have a
  significant impact on the performance of a
  business, and process improvement can improve a
  firm's competitiveness.
• The first step to improving a process is to
  analyze it in order to understand the activities,
  their relationships, and the values of relevant
  metrics. Process analysis generally involves the
  following tasks
• Define the process boundaries that mark the entry
  points of the process inputs and the exit points
  of the process outputs.
• Construct a process flow diagram that illustrates
  the various process activities and their
  interrelationships.
• Determine the capacity of each step in the
  process. Calculate other measures of interest.
• Identify the bottleneck, that is, the step having
  the lowest capacity.
• Evaluate further limitations in order to quantify
  the impact of the bottleneck.
• Use the analysis to make operating decisions and
  to improve the process.

53
Framework for Comparative Analysis

• Based on an analysis of frameworks for systems
  engineering, industrial product engineering, and
  software engineering, Wierenga et al. developed
  the GRAAL conceptual framework for describing and
  comparing IT architectures.
• The four dimensions of the framework are system
  aspects, system aggregation, systems processes,
  and description levels.

54
GRAAL Conceptual Framework

• GRAAL program, http//graal.ewi.utwente.nl/
• A Conceptual Framework for Architecture Alignment
  Guidelines. Project GRAAL WP1 Whitepaper P. A. T.
  van Eck (editor), H. Blanken, M. Fokkinga, P. W.
  G. Grefen, R. J. Wieringa, October 17, 2002
  http//graal.ewi.utwente.nl/GRAAL_whitepaper_20021
  017.pdf
• Project GRAAL Towards Operational Architecture
  Alignment. Pascal van Eck, Henk Blanken, Roel
  Wieringa http//graal.ewi.utwente.nl/eck_blanken_w
  ieringa_ijcis04.pdf

55
Value-Based IT Alignment (VITAL)

• Value-based IT ALignment (VITAL),
  http//www.vital-project.org/
• Daneva, M., Wieringa, R. (2005). Requirements
  Engineering for Cross-Organizational ERP
  Implementation Undocumented Assumptions and
  Potential Mismatches. In Proc. Int. Conference
  on Requirements Engineering (RE'05), Paris,
  Aug/Sept 2005, IEEE Computer Society Press, Los
  Alamitos, CA. http//www.vital-project.org/papers/
  Daneva-Wieringa-Camera-Ready-RE-Paper.pdf
• Daneva, M., Eck, P. van (2006). What Enterprise
  Architecture and Enterprise Systems Usage Can and
  Cannot Tell About Each Other.CTIT Technical
  Report TR-CTIT-06-02, Centre for Telematics and
  Information Technology. University of Twente,
  Enschede, The Netherlands. http//www.cs.utwente.n
  l/patveck/redirect.php?pTR0602
• Santana Tapia, R. (2006). IT Process
  Architectjures for Enterprise Development A
  Survey from a Maturity Model Perspective.CTIT
  Technical Report TR-CTIT-06-04, Centre for
  Telematics and Information Technology. University
  of Twente, Enschede, The Netherlands.
  http//www.vital-project.org/papers/TR-CTIT-06-04.
  pdf

56
Traditional Industry Categories for Which Data
Models Exist

• Silverston provides an extensive library of
  universal data models for the following industry
  categories
• Manufacturing
• Telecommunications
• Health Care
• Insurance
• Financial Services
• Professional Services
• Travel
• E-Commerce

57
Traditional Data Modeling Entities and Attributes

• An entity represents a category of information
  that must be managed by the business.
• Entities are data that are captured, used in
  calculations, reported, and so on.
• Entities come in groups. For example, an entity
  called supplier implies that multiple suppliers
  exist.
• An attribute is a characteristic of an entity
  that reveals information about the entity that
  needs to be managed.
• For example, a supplier entity might have
  attributes such as supplier ID, supplier
  name, etc.

58
Data Structure Example Health Care Delivery

• For any given HEALTH CARE EPISODE, there can be
• One or more HEALTH CARE DELIVERYs (e.g.,
  EXIMINATION, DRUG ADMINISTRATION)
• Each HEALTH CARE DELIVERY must be associated with
  a HEALTH CARE OFFERING (that identifies possible
  HEALTH CARE SERVICES and HEALTH CARE GOODS)

59
Data Model Example Health Care Delivery
60
DC Element Set

• Title
• Creator
• Subject
• Description
• Publisher
• Contributer
• Date
• Type
• Format
• Identifier
• Source
• Language
• Relation
• Coverage
• Rights

61
Zachman Framework
62
Activities That Use Information

• Organizational use of information
• Analyze org structure, strategy skills
• Define goals and objectives, critical success
  factors and constraints
• Identify org structure and strategy changes
• Identify org impact of biz or technical
  requirements
• Business use of information
• Identify required functions
• Identify required data
• Identify business activities and critical
  business processes
• Identify required activities
• Map functions to data
• Map functions to activities
• Map activities to data
• Review biz impact of org or technical requirements

63
Activities That Use Information (continued)

• Plan or design how information will be used in a
  particular context
• Design workflows
• Design information structures
• Specify data storage and data access
• Specify application functionality
• Specify technical support
• Design organizational structures
• Review business requirements and designs
• Examine org, biz, and technical benefits and
  costs
• Prioritize solutions
• Plan implementations
• Use information effectively
• Analyze strategies, competitive environment,
  skills, and competencies, org design, management
  structures
• Analyze processes and workflows, functions, data
  and information use
• Analyze existing application, network, and system
  architecture
• Analyze existing databases, applications, and
  systems
• Review org impact, biz requirements, and
  technical architectures
• Prioritize redevelopment needs

64
On Metrics

• Keys to Measurement
• Measure the right things.
• Metrics must be specific, measurable, actionable,
  relevant, and timely (SMART)
• Understand who the customers (internal or
  external are)
• Understand process inputs and outputs