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DATA SCIENCE AND BIG DATA ANALYTICSCHAPTER 2 DATA ANA.docxrandyburney60861
DATA SCIENCE AND BIG DATA
ANALYTICS
CHAPTER 2:
DATA ANALYTICS LIFECYCLE
DATA ANALYTICS LIFECYCLE
• Data science projects differ from BI projects
• More exploratory in nature
• Critical to have a project process
• Participants should be thorough and rigorous
• Break large projects into smaller pieces
• Spend time to plan and scope the work
• Documenting adds rigor and credibility
DATA ANALYTICS LIFECYCLE
• Data Analytics Lifecycle Overview
• Phase 1: Discovery
• Phase 2: Data Preparation
• Phase 3: Model Planning
• Phase 4: Model Building
• Phase 5: Communicate Results
• Phase 6: Operationalize
• Case Study: GINA
2.1 DATA ANALYTICS
LIFECYCLE OVERVIEW
• The data analytic lifecycle is designed for Big Data problems and
data science projects
• With six phases the project work can occur in several phases
simultaneously
• The cycle is iterative to portray a real project
• Work can return to earlier phases as new information is uncovered
2.1.1 KEY ROLES FOR A
SUCCESSFUL ANALYTICS
PROJECT
KEY ROLES FOR A
SUCCESSFUL ANALYTICS
PROJECT
• Business User – understands the domain area
• Project Sponsor – provides requirements
• Project Manager – ensures meeting objectives
• Business Intelligence Analyst – provides business domain
expertise based on deep understanding of the data
• Database Administrator (DBA) – creates DB environment
• Data Engineer – provides technical skills, assists data
management and extraction, supports analytic sandbox
• Data Scientist – provides analytic techniques and modeling
2.1.2 BACKGROUND AND OVERVIEW
OF DATA ANALYTICS LIFECYCLE
• Data Analytics Lifecycle defines the analytics process and
best practices from discovery to project completion
• The Lifecycle employs aspects of
• Scientific method
• Cross Industry Standard Process for Data Mining (CRISP-DM)
• Process model for data mining
• Davenport’s DELTA framework
• Hubbard’s Applied Information Economics (AIE) approach
• MAD Skills: New Analysis Practices for Big Data by Cohen et al.
https://en.wikipedia.org/wiki/Scientific_method
https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining
http://www.informationweek.com/software/information-management/analytics-at-work-qanda-with-tom-davenport/d/d-id/1085869?
https://en.wikipedia.org/wiki/Applied_information_economics
https://pafnuty.wordpress.com/2013/03/15/reading-log-mad-skills-new-analysis-practices-for-big-data-cohen/
OVERVIEW OF
DATA ANALYTICS LIFECYCLE
2.2 PHASE 1: DISCOVERY
2.2 PHASE 1: DISCOVERY
1. Learning the Business Domain
2. Resources
3. Framing the Problem
4. Identifying Key Stakeholders
5. Interviewing the Analytics Sponsor
6. Developing Initial Hypotheses
7. Identifying Potential Data Sources
2.3 PHASE 2: DATA PREPARATION
2.3 PHASE 2: DATA
PREPARATION
• Includes steps to explore, preprocess, and condition
data
• Create robust environment – analytics sandbox
• Data preparation tends to be t.
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SAJJAD KHUDHUR ABBAS
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Development
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Product and Technology Roadmaps and Roadmapping Processes - Dave Litwiller - Sept 19 2022.pptx
1. Dave Litwiller
September 19, 2022
PRODUCT & TECHNOLOGY
ROADMAPS, AND,
ROADMAPPING PROCESSES
SEPTEMBER 19, 2022
DAVE LITWILLER
2. GOAL OF
ROADMAPPING
• To capture, visualize, manipulate, and manage information
to decrease complexity in foresight
• To translate vision and strategy into:
• A focused, scope-bounded outlook
• Exploration of the innovation landscape capabilities and
opportunities
• Identification of innovation pathway options
• Overcoming innovation barriers, closing gaps, leveraging
enabling factors, and identifying intermediate milestones
• Tangible outputs and measurable impact
• A baseline plan to revisit for future validation and iterations
3. SUCCESS FACTORS
• For information and perspectives, internal and external:
• Gathering
• Integration
• Synthesis
• Building sufficient consensus among participants, and
being able to make decisions and commit to action even
when there is split opinion
• Systems-, enterprise-, and even industry-engineering are
typically all in play
• Ability to trade-off within and across domains
5. ROADMAPPING PROCESS -
MAIN DRIVERS AND INPUTS
Philips Process
• Emphasis on market, product, and technology considerations
Credit: Robert Phaal,
Cambridge Roadmapping
9. COMMON PROCESS FOR
ROADMAP GENERATION
Emphases in this
example:
1) Market/business
2) Product, service
3) Technology
Note:
The process can also
use other
drivers such as IP,
regulatory
standards/certification,
operations, supply
chain etc.
12. SCHEDULE UNCERTAINTY OF FAR
RANGE AND TECHNOLOGICALLY
RISKIER ROADMAP ELEMENTS
Credit: Lee & Thomas, Bakke & Haskins
• Using NASA’s Technology Readiness Levels (TRLs), base rate data:
13. SET-BASED CONCURRENT
ENGINEERING ROADMAPS
Credit: The 2Gemba Blog
• Used in complex systems engineering with multiple high risk subsystems
• Keeps open multiple sub-system possibilities until late in the design cycle
• Avoids premature lock-in of architecture, interfaces, and subsystems
16. EXAMPLE OF A HIGH
STAKES INDUSTRY-WIDE
TECHNOLOGY ROADMAP
International Roadmap for (Semiconductor) Devices and Systems, 2022
Source: IRDS
17. EXPECTATIONS AND
MINDSET FOR SUCCESS
• Expect multiple roadmap iterations to take place for a
leadership group to evolve toward a shared view and mutual
understanding
• Don’t try to do too much in a single session
• It is a journey for everyone involved
• This is especially so toward the fuzzier front end
• The roadmap is the catalyst, but really the output of a
roadmapping process for management is the dialog, mutual
understanding among different points of view, and resolving
competition among ideas through innovation in products and
business processes
• It usually takes some tension and even some conflict for the
best ideas to emerge
18. EXPECTATIONS AND
MINDSET FOR SUCCESS
• Being as explicit as possible about hypotheses,
assumptions and dependencies usually helps bring
different perspectives into focus
• For entrenched points of view, asking what it would take to
change the person’s or group’s mind can further help
reveal why people feel the way they do
• Often, one of the underlying issues for a leadership group
through the roadmapping process is to develop a more
integrated, shared view about risk:
• What risk is
• What risks are appropriate to take
• What rewards and opportunities are worth the price
19. EXPECTATIONS AND
MINDSET FOR SUCCESS
• No-feature-left-behind roadmaps are typically a strong sign
of insufficient choice, deliberation and conflict resolution in
the process
• To fix:
• Product and technology roadmaps have roots in strategy
formulation and design of strategic actions
• Overly broad roadmaps usually mean that strategic choices
haven’t been sufficiently narrowed or distilled to the essential
issues
• This challenge of roadmap sprawl can be particularly
pronounced in cultures which expect or demand that most or
all issues be resolved collaboratively
• Coherent choice requires some ultimate, binding decision
authority
20. FURTHER
DISCUSSION
For additional dialog about product and technology
roadmaps and roadmapping processes in start-ups and
scale-ups:
dave.litwiller@communitech.ca