florida, usa. [email protected]; · 2020. 7. 31. · • module 1: introduction to cybersecurity...

Bhuvan UNHELKAR, PhD, FACSProfessor of IT, Univ. Of South Florida,

(Sarasota-Manatee campus),

Florida, USA. [email protected];

mailto:[email protected]

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

3

• 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


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


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


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)

13

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


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


Think Data – Handset, Dataset, Toolset, Mindset

19

•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


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;


Chapter 3: Business Analysis Planning and Monitoring• Planning for Stakeholder Engagement, Business Analysis Governance

and Identifying Business Analysis Performance Improvements

21

• Data -> Cloud Vendors

• Cybersecurity

Stakeholders

• Data -> Value-based KPIs

• Tools for Governance

BA Governance • Kaizen (Agile)

• Incremental

Performance Improvements


Chapter 4: Elicitation and Collaboration

• Preparing for and Conducting Elicitation; Communicate Business Analysis Information and Managing Stakeholder Collaboration

22

• Interviews++• Exploration

(Research)

Conduct Elicitation

• Iteratively • Shorter feedback

cycles

Communicate BA Information • In-side and outside

the Organization• Business, Project,

Operations

Stakeholder Collaboration


Chapter 5: Requirements Life Cycle Management

• Maintain, Prioritize and Trace Requirements; Assess Requirements Changes and Approve Requirements

23

• Not one-off• Not project-

based

Requirements

• Ongoing basis• Impact on

Data sources

Assess Changes • Dynamically

• Holistically

Approve Requirements


Chapter 6: Strategy Analysis

• Analyze Current State, Define Future State, Assess Risks and Define Change Strategy

24

• 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


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

25

• Visual Models• What-if

Scenarios

Requirement Specs

• Tools-based• Data-Algorithm-

Visualization

Verify & Validate • Keeping Data &

Context in Mind• Cybersecurity in

Mind

Recommend Solutions


Chapter 8: Solution Evaluation

• Measure Solution Performance, Analyze Performance Measures, Assess Solution Limitations, Assess Enterprise Limitations and Recommend Actions to Increase Solution Value

26

• Tools-based• Security-based

Solution Performance

• Collaborative Enterprise?

• Governance-Risk-Compliance?

Enterprise Limitations • More, relevant

Data• Wider

Correlations

Increase Solution Value


Chapter 9: Underlying Competencies

27

• 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


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

28


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

29


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).

30


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.

31


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.

32


• 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.

33

https://www.usf.edu/work-at-usf/careers/

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

41

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


Data Patterns

Correlations

Application

Scope the data; (Approximations, Heuristics)

Process of Applying ML algorithms to Big Data: for asking Questions!

42

Identify Patterns; WITHOUT Goals; Let the Data Speak!

Apply to “Sample” business situation; Create Themes.

Discover “Value” from Application; Identify further Correlations!


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)

43

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:

[email protected]

Many Thanks!!Questions

mailto:[email protected]

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

florida, usa. [email protected]; · 2020. 7. 31. · • module 1: introduction to cybersecurity...

Documents