Big Data Analytics: From Insights to Production
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The document outlines a strategy for implementing big data analytics in predictive maintenance, emphasizing the integration of analytics use cases with business operations. It details the processes of experimentation, validation, and productionization, highlighting key activities and outputs necessary for assessing the impact of capital investments and maintenance planning. The approach follows agile principles to ensure continuous improvement and efficiency in maintenance operations.
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Big Data Analytics: From Insights to Production
- 1. From insights to production with Big Data Analytics Eliano Marques – Senior Data Scientist November 2015
- 3. Large scale solutions typically are part of a discovery process and fully integrated with the organization strategy Big Data Analytics Strategy and Ambition 1 Business analytics roadmap Capture of analytics use cases and development of analytics roadmap(s) with business areas Productionisation Large scale deployment of analytics use case based on agile scrum principles & methods Analytics 1 23 4 Experimentation Agile analytics discovery PoC on offline/ online data to prove analytics potential prior to decision on large scale productionisation Validation Decision on whether to promote analytics use case for productionisation Shared Big Data Analytics governance
- 4. 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
- 5. 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 engineers Data 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
- 6. 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 Datafor Ingest Enterprise Data Lake Sequence Automate Apply Structure Compress Protect DashboardEngine 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?
- 7. 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 Team Scrum 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 3 Model 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 Datafor Ingest Enterprise Data Lake Sequence Automate Apply Structure Compress Protect DashboardEngine Collect & Manage Metadata Perimeter-Authentication-Authorisation Ingest Solution running 4 ✔