Outline of the generic process for an end-to-end data science project, beginning with definition of business requirements and ending with value-add brainstorming.

1. 1
The Analytic Process
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2. 2
At a glance
Requirements
Research
Rationale
• Define business problem
• Determine scope
• Develop high-level strategy
• Choose platform
• Craft algorithms
• Construct solutions
• Vindicate investment
• View / present / publish
• Value-add
• pain points, opportunities
• knowledge, tools, products
• implementation, success
• in-house vs Cloud, O/S vs ™
• pre-processing, modelling
• investigate, hypothesise, test
• explain, prove
• make the data tell the story
• discover, predict, exploit
Technology
Techniques
Tactics
Validation
Visualisation
Vision

3. 3
Define the business problem
 Background
 Business environment
 Generic challenges, best
practice, current R&D
 Specific challenges
 Problem Statement
 What do we want to
understand / fix / build ?
Requirements, Research, Rationale
• High-level client SME’s give
overview
• Low-level SME’s provide
operational constraints
• Root cause analysis
• Decision-support tools
• New product / New industry

4. 4
Determine the scope
 Investigation
 Questions & answers
 Decision-Support Tools
 Specificity / generality
 Tactical / strategic
 Data Products
 Library / application
 Stand-alone / add-in / service
Requirements, Research, Rationale
• What do we need to reveal?
• How far shall we let the data take
us?
• Domain of application?
• Who is the user?
• Future-proofing?
• Extensibility?
• Commercial quality?

5. 5
Develop the high-level strategy
 Data
 in-house / open / proprietary
 static / longitudinal / streaming
 structured / unstructured
 Methods
 supervised / unsupervised ML
 Deep Learning
 Outcomes
 what should success look like?
Requirements, Research, Rationale
• quality / completeness
• proxies, compound inference
• Big Data
• technological constraints
• theoretical constraints
• metrics / KPIs
• before / after / ongoing

6. 6
Choose Platform
 Analytic tools
 entrenched or preferred tools
 Analytic infrastructure
 internally-managed servers
 Cloud provider
 Deployment infrastructure
 internal vs Cloud
 analytic (Big Data?) capability
Technology, Techniques, Tactics
• licensing
• familiarity
• code / tool base
• agility
• scalability
• flexibility
• scalability

7. 7
Craft Algorithms
 Data preparation
 wrangling & munging
 Exploring and testing
 features & correlations
 hypotheses
 Machine learning
 differentiators, clusters
 predictive modelling
Technology, Techniques, Tactics
• repeatable, reusable
• archiving
• get to know the data
• test for relationships
• seeing what drives what
• testing scenarios
• surprises

8. 8
Construct Solutions
 Input
 automatic
 manual / pipeline
 Processing
 adaptive
 self-diagnostic
 Output
 targeted
 relevant
Technology, Techniques, Tactics
• data owner?
• data veracity?
• data validity?
• maintainable
• robust
• commercial quality?

9. 9
Vindicate Investment
 Early wins
 small, relevant use cases
 proof of concept
 ROI
 sanity checks, KPIs
 effectiveness of solution
 Empowerment
 operational intelligence
 strategic intelligence
Validation, Visualisation, Vision
• useful insights
• immediate actions
• immediate value
• lifetime value
• how to convert insight into
maximum business benefit?

10. 10
View / Present / Publish
 Proof of solution
 field tests
 ROI estimates
 Graphic presentation
 static / interactive
 knowledge transfer
 Story telling
 essential points
 implications
Validation, Visualisation, Vision
• test recommendations
• test predictions
• web-based models,
dashboards
• infographics
• explaining the past
• predicting the future

11. 11
Value-Add
 Fine-tuning
 data selection & pre-processing
 parameter adjustment
 Expanded use cases
 other products, processes
 Future directions
 new applications
 new markets
Validation, Visualisation, Vision
• actuality vs prediction
• feedback from users
• brainstorming with the
wider business
• licensing opportunities
• disruption

12. 12
Data Information Insight
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