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