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​From  insights  to  production  with  Big  Data  Analytics
​Eliano  Marques  – Senior  Data  Scientist
​November  2015
Large scale solutions typically are part of a discovery
process and fully integrated with the organization strategy
Big Da...
Use case – Predictive Maintenance
Business analytics roadmap
CFO  &  Director  of  
Assets/Production
• What  is  the  out...
Use case – Predictive Maintenance
Experimentation
Production  Team
Experiment  Owner
Business	
  and	
  
data	
  Workshops...
Use case – Predictive Maintenance
Validation
Business	
  case	
  
assumptions
Business	
  case	
  
development
Workshop	
 ...
Use case – Predictive Maintenance
Productionisation
Release	
  
Planning
Create	
  Project	
  
Backlog
Production	
  
Depl...
Think	
  you
Thank Big
Big Data Analytics: From Insights to Production
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Big Data Analytics: From Insights to Production

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This presentation given by Think Big's senior data scientist Eliano Marques at Digital Natives conference in Berlin, Germany (November 2015), details how to go from experimentation to productionization for a predictive maintenance use case.

Big Data Analytics: From Insights to Production

  1. 1. ​From  insights  to  production  with  Big  Data  Analytics ​Eliano  Marques  – Senior  Data  Scientist ​November  2015
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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?
  6. 6. 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 ✔
  7. 7. Think  you Thank Big
  • mmcdevitt89

    Nov. 21, 2015

This presentation given by Think Big's senior data scientist Eliano Marques at Digital Natives conference in Berlin, Germany (November 2015), details how to go from experimentation to productionization for a predictive maintenance use case.

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