The document describes the cycle of continuous business change and improvement, which includes deciding strategy, modeling the business, changing the business, operating the business, and analyzing the business. It provides details on business modeling activities like identifying business terms, processes, data models, risks, and metrics. It emphasizes the importance of knowledge in managing change effectively through techniques like capturing business stories and scenarios.

1. Architect
Business Change
Diane Alexander
Knowledge Enablement Partner Resources, LLC

2. Cycle of Business Change
Continuous business change and improvement works in a
business life-cycle of the high-level functions here:
DECIDE
STRATEGY
MODEL
BUSINESS
CHANGE
BUSINESS
OPERATE
BUSINESS
ANALYZE
BUSINESS

3. High-Level Business Functions
• Decide Strategy – Decide product strategy and what to change
• Model Business – Define current and target business
• Change Business – Move from current business to target business operation
• Operate Business – Make it happen
• Analyze Business – Assess revenue, cost, performance, risk, change impact

4. Business Modeling
• A business model is a key risk mitigating factor in both running
a business and managing changes to it.
• A good business model is a reflection of the reality of how the
business is run, and how it should be run.
• Current State
• Next Target
• Ideal State
• Decision making that is not aware of these realities is
inherently flawed at some level.
• Efforts to change or improve a business without this
knowledge are inherently risky and expensive.

5. Some Problems…
• Computer systems are a key enabler of how your business is run, and
should support the intended business model. Some problems below:
• Computer systems that do not align with the business model (how
you want to run your business)
• Increasing complexity of your technology makes it harder to manage
• Increasing complexity of your technology makes it more expensive to
build and maintain
• Increasing complexity of your technology makes it take longer to react
to changes in:
• Customer Needs
• Competitive Pressures
• Regulatory Changes
• New Technology

6. Business Modeling Activities (a starter set)
• Business Stories / Scenarios
• Identify and Define Business Terms
• Business Rules
• Identify and Define Processes
• Process Flow
• Process Details
• Data Models / Data Dictionary
• Risk Analysis
• Business Management Metrics
• Business Operating Procedures
• Software Requirements

7. To Manage Change Effectively – Need Knowledge
• A powerful way to know your business is to tell the business story
• Stories are very effective in sharing knowledge throughout history
• Business stories can be used to communicate:
– Problems to Overcome
– Envision New Opportunities
– Must Haves to Operate
• Stories can be provided from all levels and functions in the business:
– Executive Perspective
– Middle Management
– Operations Line Functions / Customer-facing / Knowledge Workers

8. • A detailed description of events that really happen in the business.
• Business stories point out the specific situation, parties involved, and
risks, cost and benefits of the value in solving a business problem,
realizing an opportunity, or failure to meet current requirements.
• Format of business stories can vary – should be in the writer’s own
words in plain natural language
• Business Value Stories are rich in knowledge, example template:
When this happens, a <person in role, algorithm> in this <organization,
location> performs this <service, procedure> using this < information,
resource> producing that <information, product>; and this happens having
these <risks, defects, inefficiencies, benefits, opportunities> valued at this
<cost, time, risk value, customer satisfaction, revenue opportunity>.
Business Scenarios – Tell the Story

9. • When scheduling truck transportation for a shipment to a Just In Time
manufacturing plant in Mount Pleasant, Pennsylvania, about 6% of the time,
transportation is scheduled to the wrong plant which is 400 miles away from
the other manufacturing plant in a different Mount Pleasant, Pennsylvania.
• If the shipment was received, then this results in the need to schedule a different truck
to pick-up the shipment from one plant and then deliver to the other at a cost that
averages $600.
• If the shipment is rejected, then need to request that the driver divert and deliver the
shipment to the other plant, resulting in an additional charge of $400.
• There is also a 40% risk that our percent of business will be reduced from 30% to 15%
from this major customer.
An Example Business Story
This scenario was happening with a major customer and did not have sales or executive
awareness of the problem.
Had the problem continued, a major revenue source would be lost and management
would now be faced with a crises.
Truck dispatch did not have all the information to know how serious the problem was.

10. Business Value Stories --> Extract Knowledge
When this happens, a <person in role, algorithm> in this
<organization, location> performs this <service, procedure> using
this <information, resource> producing that <information, product>
and this happens having these <risks, defects, inefficiencies,
benefits, opportunities> valued at this <cost, time, risk value
customer satisfaction, revenue opportunity>.
Business
Metrics
Information
Needs
Role in
Process
Event to
Start Process
Organization
and Location
Process Business
Case
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11. Business Terms and Definitions
• Business terms and words used in the business are identified and defined.
• Acronyms of the terms are provided.
• Provide business terms where different business units call a common concept
(same definition) by a different name (synonyms).
• Provide different definitions where different business units refer to the same
business term that have a different meaning for the same word (same
spelling) within a business unit context (homonyms).
• Business Terms can refer to:
• Functions / Processes / Roles / Organizations
• Resources / Facilities / Material / Equipment
• Objects / Concepts used by the Business
• Terms that are Summaries of other Business Terms
• Terms that are determined by calculations using other Business Terms

12. Business Terms Taxonomy
• Business terms and definition
• Can be used to quickly highlight scope for each business modeling effort
• Reference to check completeness of business modeling scope
• Root level of the business terms taxonomy:
Knowledge in Semantic Business Stories - the Who, What, When, Where, Why and How
Product (what) Rule (why, when) Party (who)
Resource (what) Process (how) Location (where)
Business Terms -- Framework Building Blocks

13. Business Terms Taxonomy Example

14. Sample business information concepts
Product (what)
• Product Name
• Product Package Deal
• Goods, Services
• Product Materials
• Product Ingredients
• Product Inventory
• Product Model, Design
• Industry Classification
• Financial Services
• Product Price
Rule (why, when)
• Contract, Agreement
• Laws, Regulations
• Policy, Business Rules
• Decision, Projects
• Standard, Guideline
• Industry Best Practices
• Customer Preference
• Account Terms
• License
• Unit of Measure, Time
Party (who)
• Person, Role
• Market Segment
• Organization, Committee
• Department
• Company
• Industry Association
• Syndicate
• Representative
• Regulator
• Computer Agent
Resource (what)
• Materials, Supplies
• Data, Computers
• Equipment
• Vehicle, Furniture
• Facility, Building
• Real Property
• Funds
• Container, Rail Car
• Pipe, Cable
• Worker Apparel
Process (how)
• Process Name, Description
• Procedure
• Function
• Algorithm Logic
• Methodology
• Project Process
• Reaction Plan
• Control, Monitor, Assess
• Metric, Threshold
• Residual Risk
Location (where)
• Address
• City
• County, State, Provence
• Postal Code Region
• Latitude, Longitude
• Store Aisle, Shelf
• Building Floor, Suite
• Room Number
• Country, Continent
• Metro Statistical Area
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15. Resource
Rule
Product
PartyLocation
Process
Relationships between all concepts exist

16. Business Rules
• Rules can come from:
– Laws and Regulations
• The same rule logic can be issued by multiple agencies with different laws and
regulations
• Laws and Regulations change over time (no longer in force, superseded by another
rule)
– Industry Standards and Best Practices
– Customer Expectations (and expectations from other types of Stakeholders)
– Company Policy
• Possible that a company policy is issued with a rule more stringent than a Law or
Regulation to meet customer expectations
• Rule logic can define:
– How the business processes should be defined to comply with the rules
– What additional control processes are needed to monitor and test for
compliance to the rule
– Need for other processes to react to operational data trending in an
undesirable direction that require management attention

17. Business Processes
• Business Processes are an ordered set of activities and decisions triggered by an
event that produce a specific product/service to customers/other parties to serve a
business goal.
• Business Process Definitions
– A description of the high-level business scenario that depicts the nature of the
process and the high-level process flow.
• Functions are higher-level groupings of Business Processes and provide context
• Business Processes can have sub-processes
• The lowest level sub-process is the true Business Process
– Process details are defined at the lowest level Business Process

18. Business Process Naming Standards
• Business Process names begin with a Verb
• Business Process names do not reference software application, report, or
names of forms that are filled out
• Report can be used as a verb to reference the process of preparing a
required report to a named regulatory, government, or tax agency,
otherwise, “Report” is not generally used as a Business Process Verb

19. Business Process Taxonomy
• Hierarchical organization of business functions and related processes
• Can be used to quickly highlight scope of processes for each business modeling effort
• Can be used to triage priorities for processes to be worked on first
• Reference to communicate and check completeness of business modeling scope
• Root level of the process taxonomy is the same as the business life-cycle:
• Plan Business Strategy
• Model Business
• Change Business
• Operate Business
• Analyze Business

20. Business Process Taxonomy Example

21. Business Process Framework
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• Coverage of business stories can be checked against a process framework
• A Process Framework is built by applying process types to each concept
Concept Framework Building Blocks
Product (what) Rule (why, when) Party (who)
Resource (what) Process (how) Location (where)
Multiply by Process Types
Asset Life Cycle Forecast, Plan, Acquire, Transform, Maintain, Retire
Operational Transact, Assemble, Monitor, Inspect, Alert, Assess, React
Business Life Cycle Incubate, Launch, Grow, Optimize, Sustain, Exit
Business Change Cycle Decide Strategy, Model Business, Change, Operate, Analyze
AI-Enabled Capabilities Recommend, Listen, Speak, Read, Write, Decide, Find, See
EQUALS = Business Process Framework  check business story scope coverage
21

22. Sample business processes defined within framework
• Design Product
• Forecast Product Sales
• Close Sales Agreement
• Quote Product Price
• Book Logistics Arrangement
• Create Project Plan
• Acquire Office Furniture
• Design Factory Layout
• Assemble Product
• Inspect Pressure Valve
• Alert for Inventory Level
• Order Materials
• Create Advertisement
• Assess Regulation Impact
• Discard Equipment
• Sponsor Charity Event
• Approve Company Policy
• Determine Insurance Need
• Package Product to Ship
• Monitor Brokerage Trades
• Determine Defect Cause
• Answer Customer Call
• Send Privacy Notice
• Define Process
• Prepare Tax Filing
• Record Data Source
• Monitor for Security Breach
• Retire Product Line
• Install Software Upgrade
• Pay Insurance Claim
• Detect Credit Card Fraud
• Resolve Fraud Alert
• Audit Accounting Procedure
• Maintain Vehicle
• Purchase Supplies
• Recommend Claim Approval
• Recognize Facial Features
• Analyze Customer Complaints
• Register Insurance Broker
• Find Warehouse Item
• Determine Staffing Level Need
• Hire Employee
• Test Software
• Assess Operational Risk
• Negotiate Contract
• Find Medical Research
• Research Patents
• Determine Facility Location
• Determine Training Needs
• Train Employees
• Optimize Truck Route
• Decide Credit Approval
• Model Information Meaning
• Build Website
• Collect Payments
• Analyze Market Risk
• Deploy Software Change
• Create Press Release
• Join Industry Association
• Pay Invoices
• Prioritize Proposed Projects
• Decide Business Change
• Open New Store
• Analyze Market Potential
• Review Architecture
• Close Project
• Propose Budget
• Measure Performance
• Sell Business Facility
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23. Business Process Flows

24. Process Details
• Other information that can be captured for each process step:
• Frequency, Cycle Time, Triggers, Number of People in Role
• Supporting Law/Regulation/Policy, Questions, Issues, Tools Used
• Sequence of steps, decision points, and other processes

25. Business Data Concepts and Data Modeling
• Business Term Definitions that are related to information needs can be
modeled as:
– Entities / Classes – objects in the business environment
– Attributes / Properties – descriptive information about those objects
– Relationships between the concepts
– Derivations / Summaries – calculation of one or more data elements,
the calculation should be expressed in business language
– Valid Values – the set of data values allowed for a business term,
usually used in drop down selects for data entry and reporting

26. Data Dictionary
• A data dictionary contains information about data. The data
dictionary can include information such as:
• Business name of data concept
• Business definition of data concept (semantic meaning)
• Physical Name of Data Store, Database, Table, Field
• Field format (data type and length); Field allowed values,
default value
• Is field mandatory when the record is first created or at key
process steps
• Origin of the data, data flows to other data stores
• Business rule or calculation logic for derived /summarized data
• Identification of the technical environment(s) storing the data
• Classification of the data sensitivity (Personally Identifiable,
Privacy, Medical)

27. Risk Analysis
• Risk Analysis examines the probabilities for what can go wrong (or better than
expected) for each step in a process. Analyze and capture information for:
• What can go wrong or produce defects
• Sales opportunity that exceeds current process capacity and materials
• Effect on the business if the risk is realized into an issue or opportunity
• Risk type is equal to root causes when an issue is realized (a problem
occurs)
• Current controls to prevent something going wrong
• Potential frequency of problems
• Ability to detect that a problem occurred
• Value of risk should be determined
• As a result of a risk analysis, decisions can be made to:
• Improve the process to reduce risk
• Create a reaction process and procedure should the risk materialize into
an issue
• Plan future process improvements to reduce risk

28. Risk Types
• A reference for types of risk and the meaning of those risks is a helpful tool to
recognize risks in your business  potential events. Categories of risk include:
• Strategic
• Operational
• Financial
• Reputation
• External Events
• Example risk type taxonomy:

29. Value of Risk
• Risks can be analyzed to estimate a dollar value for each risk.
• Business value of risks can help prioritize order that risks are addressed.
• Value of risk can be used in a business case for making a change in the business to
remediate the risk to a lower level of risk (residual risk).

30. Business Management Metrics
• Business metrics are the definition of what data to collect, how to
measure and analyze the data.
• Business metrics include what are acceptable upper and lower
threshold levels of process performance, and what reaction processes
to trigger if thresholds are exceeded.
• Metrics are what to measure, measurements are the operational data.
• Business monitoring is a process performed as part of regular business
operations to measure, collect and analyze business measurements.
• Best practice if the collection of data is built into your technology
systems and is transparent to the flow of operational activities.
• This provides better accuracy of the data, and less possibility of
distortion from the manual reporting of numbers.
• Types of business metrics includes product defects, volume, issue
detection (from your risk analysis), performance, compliance and
financial.

31. Business Monitoring
• Business monitoring is an on-going set of business processes to
measure and analyze business operations.
• Analysis can involve many types of statistical analysis, inference and
reasoning software, reports and charts.
• Performance measures that approach or go beyond the thresholds are
areas for management to investigate.
• Unusual or rare events may cause a spike in the measures and no changes
are planned for the business.
• Measures beyond the thresholds may be due to procedures not followed
correctly, and no process change is needed. Additional training, staffing
changes, or material/supplies/equipment adjustments may be needed.
• Deviations beyond acceptable performance may be due to changes in the
business environment, and a project to manage business change is
needed.
• Product defects exceeding an acceptable level may indicate that a process
or equipment improvement is needed.

32. Business Operating Procedures
• Follows business process model as an outline
• Consistent with process details
• Communicates detailed use of technology tools and process
• Provides examples on how to use technology tools within the
procedures
• Provides step-by-step procedure descriptions– Can be used for
training new employees, employees filling in for others on leave,
refresher training for current employees.
• Provides basis for Audit reviews

33. Software Requirements
• Create software requirements statements driven from the business
models and scenarios.
• Review software developer’s Statement of Work against requirements.
• Review packaged commercial off the shelf capabilities against business
requirements
• Review software developer’s design against requirements. Business to
decide features to include/exclude based on cost/benefit.
• Build/review Test Scripts against business scenarios / requirements for
the business to validate that the requirements were met.

34. Business Model Information  Knowledge
• Business model knowledge can be used to construct
improved business processes and communicate
needs for the technology team
• Business model information can be structured in an
information format called an Ontology
• Ontologies can be used by inference and reasoning
software (Artificial Intelligence Semantic
Technologies) to answer questions not possible with
traditional databases

35. Business Knowledge  Massive amount
• As the Business Value Stories accumulate for more
business areas, the amount of business knowledge to
manage becomes very large
• Business model information can be structured in an
information format called an Ontology
• Ontologies can be used by inference and reasoning
software (Artificial Intelligence Semantic
Technologies) to answer questions not possible with
traditional databases

36. Artificial Intelligence Semantic Technologies background
• AI technology was initially developed in a 1990 – 2003 contract at Stanford
University for US Department of Defense military intelligence applications
• Serves as the underlying technology for semantic web search (e.i. Google);
capabilities for social media (i.e., Facebook, LinkedIn)
• Standards are governed by W3C – the international body that develops
guidelines and standards for the World Wide Web (www.w3.org)
• Other current applied uses include:
• US and UK government financial data
• Genome project, Brain project, Biomedical knowledge
• USDA and United Nations agricultural data, Oil/Gas Exploration
• Business models for DoD software development
• Financial Industry Business Ontology (FIBO)

37. Artificial Intelligence uses data structures known as an Ontology
• The database structures are in the form of Triples structured as:
Subject verb Object
• An ontology data structure is designed to hold:
• Thing related to any other Thing (the subject/object nouns)
• Type of Thing
• Class – noun, concept, entity, table
• Property – attribute, descriptor, data element
• Value – instance of a thing or property, the data
• Type of Relationship between Things (the verb)

38. Ontology data can be at these levels:
• Semantic – what terminology is the same as (synonyms), a sub-category
(taxonomy), the meaning of relationships of business concepts
• Business Model – the relationships between process, procedures, data,
product, rules, and facilities to represent business knowledge
• Metadata – where the subject and object information is stored
• Can be related to any structured data in spreadsheets or data tables
• Can be related to other triples located in other Ontologies
• Transactional – stores the data used and produced by business activities
• Artificial Intelligence technologies can traverse knowledge across these
ontology data layers using inference and reasoning capabilities to provide
meaningful answers to questions

39. Ontology technology is capable of
traversing data across ontology
databases, spreadsheets, XML,
and standard relational databases.
ID Type Date Party ID Item Quantity
1 Ind 12/1/2011 10011 101 3
2 Bus 12/5/2011 120234 199 2
3 Bus 12/3/2011 129989 101 6
4 Ind 12/15/2011 178266 102 5
5 Ind 12/4/2011 84322 199 7
6 Bus 12/15/2011 3944876 102 8
7 Bus 12/3/2011 2099 111 2
Semantic Data = meaning of the data
Metadata = where to find the data
Operational Data
Spreadsheet
Relational Database
Ontology Database
Business Model Data = how the business runs

40. Ontology technology with natural language parsers can:
 Uncover semantic data in text and unstructured data
 Automatically tag text documents with metadata  can be located with a question
Semantic Data = meaning of the data
Metadata = where to find the document / image
Business Model Data = how the business runs
Transform text
into data
Tag documents with
metadata
Documents:
• Business Stories
• Customer/Employee Comments
• Employee Comments
• Contracts
• Policies / Procedures
• Patent Information
• Product Specifications
• Social Media Comments
• Research Articles
• Images – manual tagging

41. Artificial Intelligence (AI) and Machine Learning (ML) Technologies
Artificial Intelligence is the theory and development of computer
systems able to perform tasks that normally require human
intelligence, such as visual perception, speech recognition,
decision-making, and translation between languages.
Machine learning is a type of artificial intelligence (AI) that
provides computers with the ability to learn without being
explicitly programmed. Machine learning focuses on the
development of computer programs that can change when
exposed to new data.
• Exciting value propositions are now real for business
• Computers do not truly “think” or “learn”, but advances in compute power, availability of
large amounts of data, and increasing sophistication of statistical algorithms do provide
significant new capabilities
• Lots of hype out there, some providers exaggerating uses and applying to problems
where statistics not needed to solve for
• Trusted partnerships with experienced providers are key to success
• After all, computer intelligence is “artificial” – but should be used intelligently

42. The Promise of Artificial Intelligence (AI) and
Machine Learning (ML) Technologies
Artificial Intelligence now being used in main-stream
businesses can be a differentiator in how business provides
value to customers. Enabling capabilities include:
• Mine large volumes of text within and external to the
business to create insights
• Provide recommendations to decision makers
• Identify the key drivers of customer value in creating
product innovation
• Automate quality checking and risk/compliance control
processes
• Help customers discover your product value and
purchase recommendations
• Capture expected business behavior for performance
alerts, business change impact
• “See” images and objects to navigate, identify,
manufacture, inspect equipment
• Process natural language to “listen” and “talk” to
interact with your data

43. Artificial Intelligence – Many types of technology
• Machine Learning (30+ statistical algorithms to apply repeatedly and in
combinations)
• Supervised Learning
• Regression (Forecasting, Predictions)
• Classification (Diagnostics, Fraud Detection)
• Unsupervised Learning
• Dimensionality Reduction (Structure Discovery, Data Visualization)
• Clustering (Recommendation, Target Marketing)
• Reinforcement Learning (Real-time Decisions, Robot Navigation)
• Semantic Data Inference and Reasoning
• Natural Language Processing using Semantic Data
Many algorithms and software available open source and as commercial offerings. Experienced
practitioners needed to refine and combine the correct algorithms to create effective solutions.

44. Smart Data Harbor – Many terms and concepts to navigate
Machine Learning Deep Learning Artificial Intelligence
Recommendation Process Automation Predictive Analytics
Inference Engine Reasoning Engine Prescriptive Analytics
Text Mining Natural Language Processing
Social Media Data IoT Data Operational Data
News Feed Data
Streaming Data
Web Sites
SPARQL OWL Ontologies Web Data
Semantic Data
RDF
Open Linked Data
Big Data Metadata links to Physical Data Locations
Purchased Data
Spreadsheets
Data Warehouse
Operational Data
Comments
Complaints
Procedures
Policies Reviews
Claims
Videos
Digital Transformation
Documents
Images
Data Lake

45. Machine Learning – Delivering Results Today –
An example health care business case
Consider this story to avoid costs associated with re-admission to the hospital after heart
bypass surgery.
Physicians seeking recommendation engine to assist in determination of date to discharge a
patient from the hospital.
When a patient is discharged from the hospital after treatment that
required a hospital stay, the patient may experience complications
that require the patient to be re-admitted to the hospital.
This happens X percent of the time. Each re-admission costs an
average of Y dollars over per day costs for additional hospital days
for the original treatment, plus patient distress and reputation risk,
and risk of other serious side-effects.

46. Machine Learning discovered the 40 most impactful Re-Admission
data factors out of a possible 600
Machine Learning Software Creates Pattern Data for Likelihood of Re-Admission
MACHINE
LEARNING
DETERMINES
SIGNIFICANT
DATA PATTERNS
RECOMMENDATION TO
DISCHARGE PATIENT.
RECOMMENDATIONS FOR POST
– DISCHARGE PROTOCOLS.

47. Resulting Benefit Delivered
CABG coronary artery bypass
graft, also known as coronary
bypass surgery

48. Financial Services – Machine Learning Efforts Underway
• Experian -- Increasing speed for the process of applying for a mortgage. Lots of
data coming from transactional records, marketing databases and public
information such as court records. Looking for machine learning algorithms
determine what data / documents are most important.
• MetLife Insurance – Speech recognition has improved the tracking of incidents
and outcomes with more efficient claims processing as claims models are
enriched with text data such as doctor’s reports, and they are working toward
automating underwriting functions.
• AIG – Developing more accurate pricing of risk. Improving efficiency of claims
processing, fraud detection, and enhancing advise for loss prevention to
customers.
• Zurich Insurance Company – Improving pricing, claims, fraud detection, and also
enabling new advisory products for customers to shift from only getting payouts
when something happens, to helping customers predict when and how something
might happen to mitigate their own risk.

49. • Businesses that can anticipate and plan for change are most likely to
surpass their competition.
• Businesses that can quickly adjust to changes in customer needs are most
likely to delight their customers, increase revenue, and thrive over time.
• Knowledge-based business models can be used to:
• Assess impact for moving into new markets
• Find areas for cost / efficiency improvements
• Reuse knowledge determined in previous change projects -- can be
40-60% of the entire cost of a project !
• Support valuation of companies for investment or sale
• Enable quick assimilation of companies for Mergers / Acquisitions
• Assess impact of change from external / internal events
• Provide alerts of business operational performance trending in a
non-desirable direction so you can avoid a crises
Knowledge Enables Impactful Change