Post on 13-Apr-2017
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From insights to production with Big Data AnalyticsEliano Marques – Senior Data Scientist
November 2015
Large scale solutions typically are part of a discovery process and fully integrated with the organization strategy
Big Data Analytics Strategy and Ambition
1Business analytics roadmap
Capture of analytics use cases and development of analytics roadmap(s) with business areas
ProductionisationLarge scale deployment of analytics use case based on agile scrum principles & methods
Analytics
1
23
4
ExperimentationAgile analytics discovery PoCon offline/ online data to prove analytics potential prior to decision on large scale productionisation
ValidationDecision onwhether to promote analytics use case for productionisation
Shared Big Data Analytics governance
Use case – Predictive Maintenance Business analytics roadmap
CFO & Director of Assets/Production
• What is the outcome of different capital investment for the next 5 years? How do I measure the impact on maintenance?
• Which assets/parts should be targeted for replacement? How to prioritise them over time?
• How to plan ahead overall costs? What options are available?Director of Operations
• How to predict demand for reactive maintenance? Can it be reduced? What is the optimal mix between pro-active vs. reactive maintenance?
• How to predict stock levels for assets/parts? Can it be minimise?
• What capacity is needed? Do we need to sub-contract?Field Teams
Lead
• How to increase field force efficiency? How can we reduce engineering visits?
• How to prioritise faults?
• How to predict false alerts?
Strategy
Tactical
Operational
1
Use case – Predictive Maintenance Experimentation
Production Team
Experiment Owner
Business and data Workshops
Experiment Development
Experiment Testing
Experiment Results
Key activities:
Key iterations:
Who’s involved:
Weekly sessions to check experiment progress and validate initial results
Delivery workshop with program management to share experiment results
Initial workshops between experiment owners, data owners, data engineers and data scientists
Data engineersData Scientists
Key Outputs:H1: What's the impact of different capital investment strategies?
H2: Can sensor data be use to predict time-to-fail or risk-to-fail of asset parts?
H3: How to minimise faults detection root-cause and uplift efficiency?
• Segment field force by time to detect root cause patterns
• Predict root-cause of failure by type of asset/part
• Validate/test models with key stakeholders
• Link sensors with faults• Prioritise sensors by criticality of failure
• Develop models and Predict time/risk to fail by asset/part
• Validate/test models with key stakeholders
• Build target investment models linked with maintenance, volumes and workforce
• Develop simulation tool and run scenarios on demand
• Validate/test solution with key stakeholders
2
Use case – Predictive Maintenance Validation
Business case assumptions
Business case development
Workshop preparation
Validation workshop
Key activities:
Key iterations:
Who’s involved:
Meeting with business area lead to validate business case
Validation workshop with steering committee to obtain approval for moving solution to production
Meetings with production team and business area leads to get business case inputs
Key Outputs:H2: Can sensor data be use to predict time-to-fail or risk-to-fail of asset parts?
Pos-experimentation question:
Is it worth moving to production?
Experiment team
Experiment Owner
Steering Comm.Production team
Analytics
Technology costs and changes assumptions
Business value assumptions
Business case
Downstream ApplicationsInformation Sources
Evaluate Source Data
Prepare Source Metadata
Prepare Data for Ingest
Enterprise Data Lake
Sequence Automate
Apply Structure
Compress Protect
Dashboard Engine
Collect & Manage
Metadata
Perimeter-Authentication-Authorisation
Ingest
3
• New ingestions? How many models? Prediction frequency? Rules engine?• How users will access and make decisions on demand?
• What’s the size of benefit? Is it tangible?
• Is the use case viable financially? What’s the ROI? What’s is the Pay-back period?
Use case – Predictive Maintenance Productionisation
Release Planning
Create Project Backlog
Production Deployment
Key activities:
Key iterations:
Who’s involved:
Bi-weekly sign-off of development progress by program management and business area lead
Regular meetings in an agile scrum format including sprint planning, daily scrums, and sprint review
Key Outputs:
Experiment team
Experiment Owner
Production TeamScrum Master
Gov., Maint & Training
H2: Can sensor data be use to predict time-to-fail or risk-to-fail of asset parts?
Pos-experimentation question:
Is it worth moving to production?
YES
Sprint Cycles
Model 3Model 2
Model 1
• Business and field engineers can now act on real time signals based on predictions of time/risk to fail for assets and parts
• Rules can be automated to act on high-risk threads
• Pro-active maintenance decisions can now be made to optimise costs and maintenance efficiency
Downstream ApplicationsInformation Sources
Evaluate Source Data
Prepare Source Metadata
Prepare Data for Ingest
Enterprise Data Lake
Sequence Automate
Apply Structure
Compress Protect
Dashboard Engine
Collect & Manage
Metadata
Perimeter-Authentication-Authorisation
Ingest
Solution running
4
✔
Think youThank Big