Post on 06-Feb-2021
Bhuvan UNHELKAR, PhD, FACSProfessor of IT, Univ. Of South Florida,
(Sarasota-Manatee campus),
Florida, USA. bunhelkar@usf.edu;
mailto:bunhelkar@sar.usf.edu
Presenter Profile - Dr. Bhuvan Unhelkar (BE, MBA, PhD, FACS, CBAP®, PSM)
• Professor at College of Business; Univ. of South Florida (Sarasota-Manatee campus); Founder, MethodScience.com; PlatiFi.com;
• Courses: UML, Agile PM, Progressive Web Apps; Big Data; CapStone; Designed and Presenter Australian Computer Society’s Business Analysis (BAS) and Agile PM Online
• Hon. Professor Western Sydney Univ. (WSU) and Amity Univ.; Visiting Faculty at UTS (Agile SW Modeling);
• Author: 22 Books (Including Big Data Strategies for Agile Business)
• Supervisor: 8 PhD Completions;• Fellow of the Australian Computer Society; IEEE Sr. Member;
Life Member, Computer Society of India & BMA• Past President – Rotary Club of Sarasota Sunrise, USA (Paul
Harris Fellow+6; AG); TiE ex-Mentor Director;
https://southflorida.iiba.org/event/virtual-role-business-analysis-data-driven-organization-hunting-value
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• Business Analysis (BA) as a profession has its roots in capturing requirements for a software solution.
• BA has then evolved into a strategic role requiring skills in envisioning strategies, offering alternatives for solutions, modeling requirements, validating and verifying solutions, and continuous optimization.
• This talk examines these aforementioned skills in the context of today's digital world where decisions at business and technical levels are driven by data.
https://southflorida.iiba.org/event/virtual-role-business-analysis-data-driven-organization-hunting-value
Agenda• Business Analysis (BA) as a Profession
• Roots in capturing requirements for a software solution.• Evolved into a strategic role requiring skills in envisioning
strategies• Business ‘Data’ Analysis• BA skills evolution in the context of today's digital world
• Enabling data-driven decisions at business and technical levels
• Cybersecurity Analysis• Conclusions and Q&A
Sub-Module
Business AnalystNOT Just a Bridge between Users and Developers!!
© Bhuvan UNHELKAR (2020)
Business analysts are responsible for…(BABOK v3.0)
• Discovering, synthesizing, and analyzing information from a variety of sources within an enterprise, including tools, processes, documentation, and stakeholders.
• Eliciting the actual needs of stakeholders—which frequently involves investigating and clarifying their expressed desires—in order to determine underlying issues and causes.
• Aligning the designed and delivered solutions with the needs of stakeholders.
• The activities that business analysts perform include:• understanding enterprise problems and goals,• analyzing needs and solutions,• devising strategies,• driving change, and• facilitating stakeholder collaboration. 6
© Bhuvan UNHELKAR (2020)7
© Bhuvan UNHELKAR (2020)8
PURDUE
Sub-Module
A Data-Driven Organization?
The What & How
“Big
” is
a Re
lativ
e Te
rm
(Con
text
dep
ende
nt)
IBM
5M
B H
ard
Driv
e 19
56
https://www.bing.com/images/search?q=IBM+5MB+Hard+Drive+1956&id=5B041032609B4869596374B0FA939EA6BAA3A736&FORM=IDBQDM
© Bhuvan UNHELKAR (2020)
What is Big Data?• ‘Big Data’ is still ‘Data’
• But difficult to make SENSE of with the available tools
• Could be made up of much wider ‘types’ or ranges of data
• Handling such Data requires Latest:• Techniques, tools, and architecture• Frameworks to guide adoption and reduce Risks
• With an aim to:• Solve new challenges in Business• Improving the Value proposition
Dimensions of Big Data –started with 4 x Vs
© Bhuvan UNHELKAR (2020)
Figure 3.3 : Detailed Characteristics of Big Data’s 3+1+1 Vs and the Types and Categories of data. (The fifth V for Value is the focus of BDFAB)
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Volume Extremely Large Data sets (Peta and beyond)
Storage & Administration
VelocityHigh speed of Data creation
and Movement (IoT, Sensors)
Shorter Latency (Currency) and
relevance
VarietyStructured,
Semi- and Un-Structured variations
Video, Audio, Graphics, Text,
Mixed )
Each DATA characteristic impacts the way Big Data Strategies and corresponding Solutions are designed, developed and used
Vera
city
(Qua
lity;
Acc
urac
y; C
onte
xt)
Ow
ned
-Pu
rcha
sed
-Le
ased
-O
pen
Data
Part
ners
–Go
vern
men
t –3r
dPa
rtie
s
Audio Video
Graphics Sensor
MixedText/NumbersValue
Technologies Analytics
Sub-Module
Business “Data” Analyst
The What & How of the Role in a Data-Driven Organization
© Bhuvan UNHELKAR (2020)15
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© Bhuvan UNHELKAR (2020)17
© Bhuvan UNHELKAR (2020)
Identifying the Root causes of Problems; and Opportunity
Business Analysis as the Foundation of Big data strategies: Short- and Long-term decision making based on observations-data-information-knowledge-insights
Ch - 1 18
Transforming from one level to
another comprises a suite of sub-
processes
Business Intelligence:Inferences based on
extensive Correlation amongst widely
dispersed Knowledge
ExplicitObjective
[Analytical,Technical]
Shor
t Ter
m
Lon
g Te
rm(T
actic
al)
( St
rate
gic)
Business Analysis
Where to use Data? Which Data to use?
Impact on Knowledge creation
Business Analysis
© Bhuvan UNHELKAR (2020)
Think Data – Handset, Dataset, Toolset, Mindset
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•Systems, Processes, Communications
•Analytics as a Service•Data Handling
•People, Users•Decision-making•Agile Business
•Sources (Own, Buy, Lease) •Types (4V) + Alternate Data•Big Data
•IoT, Smartphones,•Sensors•Edge Computing•Communications
Handset Dataset
ToolsetMindset
VALUE
Cybersecurity
© Bhuvan UNHELKAR (2020)20
Core Concept Description (BABOK) Data Relationship (Analysis) Example (Data as Means)
Change The act of transformation in response to a need
Data is not the change; Data is meant to fulfil a Need. Think Data > What will change? When and how?
All University Courses move to Online during the pandemic; now likely to remain there for a long time.
Need A problem or opportunity to be addressed
Data is not the need; But Data can create Needs in the Business;
Ability to teach, learn and evaluate entirely online.
Solution A specific way of satisfying one of more needs in a context
Ask Data for Potential Solutions. Machine Learning (AI-based) exploration of solutions
Canvas, MS Teams, Blackboard, Polls and surveys
Stakeholder A group or individual with relationship to the change, the need, or the solution
Mass Personalization of End-users based on Data; Development as a Service
Students, Teachers, Researchers, Administrators, Staff, People, Security – each Personalized
Value The worth, importance, or usefulness of something to a stakeholder within a context
Explore the Dynamicity in Value (it changes rapidly in an Electronic world)
Achieving Learning Objectives in the safety of home; Rapid feedback cycle.
Context The circumstances that influence, are influenced by, and provide understanding of the change
Context is Subjective – depending on the moods and needs of the User
Covid-19; concerns, fears for safety;
© Bhuvan UNHELKAR (2020)
Chapter 3: Business Analysis Planning and Monitoring• Planning for Stakeholder Engagement, Business Analysis Governance
and Identifying Business Analysis Performance Improvements
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• Data -> Cloud Vendors
• Cybersecurity
Stakeholders
• Data -> Value-based KPIs
• Tools for Governance
BA Governance • Kaizen (Agile)
• Incremental
Performance Improvements
© Bhuvan UNHELKAR (2020)
Chapter 4: Elicitation and Collaboration
• Preparing for and Conducting Elicitation; Communicate Business Analysis Information and Managing Stakeholder Collaboration
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• Interviews++• Exploration
(Research)
Conduct Elicitation
• Iteratively • Shorter feedback
cycles
Communicate BA Information • In-side and outside
the Organization• Business, Project,
Operations
Stakeholder Collaboration
© Bhuvan UNHELKAR (2020)
Chapter 5: Requirements Life Cycle Management
• Maintain, Prioritize and Trace Requirements; Assess Requirements Changes and Approve Requirements
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• Not one-off• Not project-
based
Requirements
• Ongoing basis• Impact on
Data sources
Assess Changes • Dynamically
• Holistically
Approve Requirements
© Bhuvan UNHELKAR (2020)
Chapter 6: Strategy Analysis
• Analyze Current State, Define Future State, Assess Risks and Define Change Strategy
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• Current State rapidly changing
• Future state is context based
Assess States
• Known-Unknown• Use AI-based
Engines
Assess Risks• Current-Future state
is Iteratively defined• Mindset is most
challenging
Define Change Strategy
© Bhuvan UNHELKAR (2020)
Chapter 7: Requirements Analysis and Design Definition
• Specify and Model Requirements, Verify and Validate Requirements, Define Requirements Architecture, Define Design Options, Analyze Potential Value and Recommend Solution
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• Visual Models• What-if
Scenarios
Requirement Specs
• Tools-based• Data-Algorithm-
Visualization
Verify & Validate • Keeping Data &
Context in Mind• Cybersecurity in
Mind
Recommend Solutions
© Bhuvan UNHELKAR (2020)
Chapter 8: Solution Evaluation
• Measure Solution Performance, Analyze Performance Measures, Assess Solution Limitations, Assess Enterprise Limitations and Recommend Actions to Increase Solution Value
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• Tools-based• Security-based
Solution Performance
• Collaborative Enterprise?
• Governance-Risk-Compliance?
Enterprise Limitations • More, relevant
Data• Wider
Correlations
Increase Solution Value
© Bhuvan UNHELKAR (2020)
Chapter 9: Underlying Competencies
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• Creative Thinking; Problem Solving; Visual Thinking (Build Scenarios & Experiment)
Analytical Thinking and Problem Solving
• Adaptability; Ethics; Behavioural Characteristics
• Business specific-Data and Business ProcessesBusiness Knowledge
• Including Social MediaCommunication Skills
• Especially AgileInteraction Skills
• Data-driven Modeling toolsTools and Technology
© Bhuvan UNHELKAR (2020)
Cybersecurity Analysis
• Module 1: Introduction to Cybersecurity Analysis • Module 2: Enterprise Security Concepts • Module 3: Enterprise Risk • Module 4: Cybersecurity Risks and Controls • Module 5: Securing the Layers • Module 6: Data Security • Module 7: User Access Control • Module 8: Solution Delivery • Module 9: Operations
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© Bhuvan UNHELKAR (2020)
Data Security At Rest: Information Classification & Categorization
• Identify information types for input, stored, processed, and/or output data.
• Assign levels of impact to confidentiality, integrity and availability• Assign a system security category based on the aggregate of information
types. (NIST)• Technology Controls • Data Loss Prevention (DLP) on Endpoints and Network Devices such
as Firewalls. • Use Tools to establish and manage information and system sensitivity
requirements and controls. • Automation of Classification of Information
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© Bhuvan UNHELKAR (2020)
Data Security In Transit: Encryption and Keys
• Understand the difference between symmetric and asymmetric encryption and how keys are exchanged, stored, generated, and revoked.
• Understand the sensitivity of the data being transmitted and received to determine the correct type of encryption and key strength to ensure datasecurity.
• Understand how keys are used to generate and verify digital signatures. • Understand how public and private key pairs form the underpinning
ofvarious Internet security standards including TLS (TransportLayerSecurity) and S/MIME (Secure/Multipurpose Internet Mail Extensions).
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© Bhuvan UNHELKAR (2020)
Data Security In Transit: SSL/TLS
• Understand the types of data that will be transmitted between systems and if that data is classified as sensitive enough to require encryption.
• Understand any applicable legal requirements for in-transit data security.
• Understand how certificates are used for encryption of data in transit.
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© Bhuvan UNHELKAR (2020)
Data Security In Transit: Digital Signature and Identification
• Understand what data being transmitted willbe used for and if non-repudiation will be required.
• Understand the process for how data willbe signed before transmission and identified and validated upon receipt.
• Understand how the management and use of certificates for signing and identification willbe performed.
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© Bhuvan UNHELKAR (2020)
• The School of Information Systems and Management (SISM) of the Muma College of Business at the University of South Florida (USF) is searching for outstanding candidates to fill three (3) open rank tenure-track faculty positions in the Cybersecurity area. One position is located on the Tampa campus and two positions are located on the Sarasota-Manatee campus. Candidates are encouraged to apply on the USF Careers website at https://www.usf.edu/work-at-usf/careers/ . The job ID for the Tampa position is 25400. The job ID for the Sarasota-Manatee positions is 25388. A candidate may apply for one or both positions.
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https://www.usf.edu/work-at-usf/careers/
© Bhuvan UNHELKAR (2020)34
Sub-ModuleStrategic Business
AnalysisFOR DATA-DRIVEN ENTERPRISES:
Beyond Requirements and Designs
Opportunities (External):• Innovative Business Growth• Customer Satisfaction/ Experience• Innovative Products & Services • Mergers & Acquisitions;
Collaborations
Optimization (Internal):• Innovative Problem Solving• Internal Process Optimization &
Maturity (SCM)• Decentralizing Decision making (&
Re-structuring
Environment & Corporate Social Responsibility:• Reduce Carbon Footprint• Enhanced Measurement & Analytics
with Machine Sensors & Tools• Develop positive User Attitude
Risk & Compliance • Business Risks (with, without) Big
Data adoption• Security & Privacy Risks• Compliance & Documentation• Audits and Traceability
Strategic Business Analysis
The Strategy Cube for Business Analysis
Social-Mobile (SoMo) – Internet of Thing (IoT)
Analytics & Fine Granularity
Storage & Sharing (Local, Cloud)Hadoop/HDFS – NoSQL - ”R”
Perceive, Anticipate
Respond & Iterate
Collaborate (Partners)
Value (Customer, U
ser)
Opportunities (External):
• Innovative Business Growth – BA explore opportunities• Customer Satisfaction/ User Experience• Innovative Products & Services • Mergers & Acquisitions; Collaborations with External, Government, Third-party Organizations
Amazon; Uber; Airbnb
• Innovative Problem Solving• Internal Process Optimization & Maturity• Decentralizing Decision making (& Re-structuring
of the organization to a Process-oriented flat, non-hierarchical structure)
Optimization (Internal):
FedEx; XPO;
• Reduce Carbon Footprint• Enhanced Measurement & Analytics with
Machine Sensors & Tools to provide most current Data
• Incorporate Automated feedback loops• Develop positive User Attitude
Environment & Corporate Social Responsibility:
• Business Risks (with, without) Big Data adoption• Security & Privacy Risks• Compliance & Documentation• Audits and Traceability
Risk & Compliance
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Why Machine Learning is an Imperative?
• Data is so vast that “We don’t know what question to ask?” from this data. Take help from Machine LearningVolume
• IoT devices pumping high velocity data – human and ‘normal’ machine language cannot handle this speed; only Machine Learning can.
Velocity• How to Analyze Un-structured data? Let
Machine Learning help us figure that out. Variety
• Quality of Data cannot be ascertained without Machine Learning; as it’s not a matter of CodingVeracity
Let A
I/M
L he
lp u
s fig
ure
out h
ow
to d
eriv
e Va
lue
from
Big
Dat
a
© Bhuvan UNHELKAR (2020)
Data Patterns
Correlations
Application
Scope the data; (Approximations, Heuristics)
Process of Applying ML algorithms to Big Data: for asking Questions!
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Identify Patterns; WITHOUT Goals; Let the Data Speak!
Apply to “Sample” business situation; Create Themes.
Discover “Value” from Application; Identify further Correlations!
© Bhuvan UNHELKAR (2020)
Future Directions
• BA competencies required understanding of the Cloud and ML application for Big Data
• Predictive and Prescriptive Analytics• Continuous Feedback and Content Improvements
• BA and Data Science Intersection needs further exploration:
• Security and Quality of Analytics• Provide Validated Results
• Research Directions: • Defense & Cyber defense• Agriculture (Farming, Supply Chains) • Governance – Risk – Compliance in Business/Government• (in addition to Education and the Environment)
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Key Points of the Session• A Strategic approach to Business Analysis includes understanding of Data, its characteristics, its usage and its risks• Focus is still on Business and the Processes for Decision making• Cybersecurity integral to BA work in Data-driven organizations• Agile facilitates change in the data-driven world
For the Opportunity Contact:
bunhelkar@usf.edu
Many Thanks!!Questions
mailto:bunhelkar@sar.usf.edu
Slide Number 1Presenter Profile - Dr. Bhuvan Unhelkar �(BE, MBA, PhD, FACS, CBAP®, PSM)https://southflorida.iiba.org/event/virtual-role-business-analysis-data-driven-organization-hunting-valueSlide Number 4Sub-Module��Business AnalystBusiness analysts are responsible for…(BABOK v3.0)Slide Number 7Slide Number 8Sub-Module��A Data-Driven Organization?“Big” is a Relative Term (Context dependent) �IBM 5MB Hard Drive 1956What is Big Data?Slide Number 12Figure 3.3 : Detailed Characteristics of Big Data’s 3+1+1 Vs and the Types and Categories of data. (The fifth V for Value is the focus of BDFAB) Sub-Module��Business “Data” AnalystSlide Number 15Slide Number 16Slide Number 17Business Analysis as the Foundation of Big data strategies: Short- and Long-term decision making based on observations-data-information-knowledge-insights Think Data – Handset, Dataset, Toolset, MindsetSlide Number 20Chapter 3: Business Analysis Planning and MonitoringChapter 4: Elicitation and CollaborationChapter 5: Requirements Life Cycle ManagementChapter 6: Strategy AnalysisChapter 7: Requirements Analysis and Design DefinitionChapter 8: Solution EvaluationChapter 9: Underlying CompetenciesCybersecurity AnalysisData Security At Rest: Information Classification & CategorizationData Security In Transit: Encryption and KeysData Security In Transit: SSL/TLSData Security In Transit: Digital Signature and IdentificationSlide Number 33Slide Number 34Sub-Module�Strategic Business Analysis�for Data-Driven Enterprises: The Strategy Cube for Business AnalysisOpportunities (External):Optimization (Internal):Environment & Corporate Social Responsibility:Risk & Compliance Why Machine Learning is an Imperative? Process of Applying ML algorithms to Big Data: for asking Questions! Future DirectionsKey Points of the SessionSlide Number 45