Intelligence for the connected world How European First-Movers Manage IoT Analytics Projects Successfully

Transcription

1 Intelligence for the connected world How European First-Movers Manage IoT Analytics Projects Successfully Thomas Rohrmann, Michael Probst Analytics Experience 2016, Rome #analyticsx C opyr i g ht 2016, SAS Ins titut e Inc. All rights res er ve d.

2 INTERNET OF THINGS JUST A HYPE?

3 INTERNET OF THINGS 75 FIRST MOVERS COMPANIES INTERVIEWED Executives and IoT Project Leads

4 INTERNET OF THINGS 75 FIRST MOVERS COMPANIES INTERVIEWED Download sas.com/iotebook

5 SURVEY FINDINGS & THOUGHTS SURVEY Expectations and Risks Lessons Learnt Organizational Impact Challenges

6 SURVEY FINDINGS & THOUGHTS Expectations and Risks SURVEY

7 SURVEY FINDINGS & THOUGHTS EXPECTATIONS FROM IOT INITIATIVES? Fig 1: Diverse expectations Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q1: What does IoT or the 4th industrial revolution mean for your customers, your business, your competitors?

8 SURVEY FINDINGS & THOUGHTS Fig 1: Diverse expectations EXPECTATIONS FROM IOT INITIATIVES? Efficiency & Cost Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q1: What does IoT or the 4th industrial revolution mean for your customers, your business, your competitors?

9 SURVEY FINDINGS & THOUGHTS EXPECTATIONS FROM IOT INITIATIVES? Fig 1: Diverse expectations Efficiency & Cost Offering & Revenue Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q1: What does IoT or the 4th industrial revolution mean for your customers, your business, your competitors?

10 SURVEY FINDINGS & THOUGHTS RISKS OF NOT ENGAGING? Fig 8: IoT expected to re-define addressable market Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q8: What do you see as the biggest risks associated with not fully exploiting Internet of things opportunities?

11 SURVEY FINDINGS & THOUGHTS RISKS OF NOT ENGAGING? Fig 8: IoT expected to re-define addressable market Efficiency risks Revenue risks Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q8: What do you see as the biggest risks associated with not fully exploiting Internet of things opportunities?

12 RECOMMENDATIONS EXPECTATIONS AN RISKS Efficiency Revenue Up-/Cross-Selling, New Business Disruptive new products Innovative new services Company Optimization of production & logistics Operational efficiencies Better resource management

13 RECOMMENDATIONS EXPECTATIONS AN RISKS Efficiency Revenue Competitor A Competitor B New Competitor guy in town C

14 KEY TAKEAWAY Don t focus on a specific project. Set up an IoT infrastructure! C opyr i g ht 2016, SAS Ins titut e Inc. All rights res er ve d.

15 SURVEY FINDINGS & THOUGHTS Lessons Learnt SURVEY

16 SURVEY FINDINGS & THOUGHTS HOW TO DEAL WITH SPEED OF IOT? Fig 9: Benchmarking Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q9: How do you assess if your appetite and initiatives around Internet of Things is moving as quickly as the market demands?

17 SURVEY FINDINGS & THOUGHTS Fig 9: Benchmarking HOW TO DEAL WITH SPEED OF IOT? Invest in innovations Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q9: How do you assess if your appetite and initiatives around Internet of Things is moving as quickly as the market demands?

18 SURVEY FINDINGS & THOUGHTS MAJOR LESSONS LEARNT? Fig 3: Design thinking imperative Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q3: What were the main lessons you learnt from these initiatives that will guide how you plan future IoT ventures?

19 SURVEY FINDINGS & THOUGHTS Fig 3: Design thinking imperative MAJOR LESSONS LEARNT? Test, test, test, retest and scale it up Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q3: What were the main lessons you learnt from these initiatives that will guide how you plan future IoT ventures?

20 RECOMMENDATIONS AGILE DATA DISCOVERY & MODEL DEVELOPMENT How do I prepare my information for project and top management? Is the time series data complete? Which model fits best? Project Analyst Engineer Can I identify outliers that point to measurement flaws? Are there relevant pattern and correlations visible? Data Scientist How does my data distribute and how can I interpret the content?

21 RECOMMENDATIONS AGILE DATA DISCOVERY & MODEL DEVELOPMENT How do I prepare my information for project and top management? Visualize, communicate Overview / integrity Is the time series data complete? Which model fits best? Develop models Project Analyst Data Scientist Engineer Plausibility Can I identify outliers that point to measurement flaws? Are there relevant pattern and correlations visible? Identify analytical relevance Understand contents How does my data distribute and how can I interpret the content?

22 RECOMMENDATIONS AGILE DATA DISCOVERY & MODEL DEVELOPMENT

23 RECOMMENDATIONS ANALYTICS LIFE CYLCE Innovation Data Science Prepare Interactive Iterative New data Fail Fast Explore Discovery Ask Experiments Leading Edge Deep Learning Model DATA

24 RECOMMENDATIONS ANALYTICS LIFE CYLCE Innovation Data Science Prepare Implement Regulated Automated Interactive Iterative New data Explore Fail Fast Experiments Leading Edge Discovery Ask Deployment Act Governed Robust Actions Decisions Consistent Deep Learning Model Evaluate Documented DATA

25 RECOMMENDATIONS HOW TO GET STARTED? Prepare Identify & evaluate Exploration Analysis ExploreFast model development Discovery Quick validation Ask Implement Operationalize Model Deployment Performance Management Stream-Processing Act Deployment Decisions and actions Model IoT Analytics Lab DATA Evaluate IoT Analytics Factory

26 KEY TAKEAWAY Keep the analytical life cycle in mind! Support agile testing and smooth deployment. C opyr i g ht 2016, SAS Ins titut e Inc. All rights res er ve d.

27 SURVEY FINDINGS & THOUGHTS SURVEY Organizational Impact

28 SURVEY FINDINGS & THOUGHTS Fig 7: Breaking organisational silos VISION OF ORGANIZATIONAL IMPACT Cross-organisational Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q7: Thinking about the businesses and support functions across your organisation, which would you consider to be the top 3 today in terms of mature use of IoT?

29 RECOMMENDATIONS APPROACHABLE ANALYTICS

30 RECOMMENDATIONS APPROACHABLE MEANS HAVING MANY PERSONAS AND NEEDS IN MIND Expand analytic toolkit. Code in the language of your choice. Scale methods without redefining code. Easily wrangle data. Interactively interrogate. Discover insights through visual interface. Guided workflows. Visual interface accelerate investigations. Analytic intelligence built-in. Embed analytics in applications. Documented, public REST APIs. Centralized management of analytic assets. One governed environment. Multi-cloud for flexibility. Failover protection. Consistent, trustworthy answers. Use current hardware and extend existing skills sets. Nurture innovation for production.

31 RECOMMENDATIONS APPROACHABLE ANALYTICS JOURNEY Value

32 RECOMMENDATIONS APPROACHABLE ANALYTICS JOURNEY Mobile Office Integration Cloud ONE INTEGRATED SOLUTION FOR ALL ROLES

33 KEY TAKEAWAY IoT Analytics must be accessible and approachable for many organizational units and persons. C opyr i g ht 2016, SAS Ins titut e Inc. All rights res er ve d.

34 SURVEY FINDINGS & THOUGHTS SURVEY Challenges

35 SURVEY FINDINGS & THOUGHTS CHALLENGES TECHNOLOGY, CHANGE & SKILLS Fig 4: Predictable challenges Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q4: What were the challenges associated with exploiting the IoT fully?

36 SURVEY FINDINGS & THOUGHTS Fig 4: Predictable challenges CHALLENGES TECHNOLOGY, CHANGE & SKILLS Technology Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q4: What were the challenges associated with exploiting the IoT fully?

37 SURVEY FINDINGS & THOUGHTS CHALLENGES TECHNOLOGY, CHANGE & SKILLS Fig 4: Predictable challenges Technology Change & Skills Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q4: What were the challenges associated with exploiting the IoT fully?

38 SURVEY FINDINGS & THOUGHTS CHALLENGES TECHNOLOGY, CHANGE & SKILLS Fig 5: Skills surprise Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q5: What skills did you find most useful during your IoT deployment?

39 SURVEY FINDINGS & THOUGHTS Fig 5: Skills surprise CHALLENGES TECHNOLOGY, CHANGE & SKILLS Wide skill range needed Source: SAS IoT Impact Study, September N = 75. Multiple responses allowed. Q5: What skills did you find most useful during your IoT deployment?

40 RECOMMENDATIONS STRATEGY & INFRASTRUCTURE TECHNOLOGY PEOPLE PROCESS

41 RECOMMENDATIONS STRATEGY & INFRASTRUCTURE TECHNOLOGY PEOPLE PROCESS

42 RECOMMENDATIONS STRATEGY & INFRASTRUCTURE TECHNOLOGY PEOPLE PROCESS

43 RECOMMENDATIONS DATA DRIVEN APPROACH TO IOT ANALYTICS Sensor Datastore (z.b. Hadoop) Enterprise Data Streaming Analytics SAS/Analytics Realtime Decisions Actuator Other actions (eg. API Call) Collect Enrich Collaboration Engineer Data Scientist Exploration Analytics Easy data access Data management Fast and scalable in-memory technology Copyright 2016, SAS Institute Inc. All rights reserved.

44 RECOMMENDATIONS STRATEGY & INFRASTRUCTURE TECHNOLOGY PEOPLE PROCESS

45 RECOMMENDATIONS SENSE UNDERSTAND ACT Data ETL Data Storage Deploy Alerts / Reports / Decisioning Deploy IoT Data Intelligent Filter / Transform Streaming Model Execution

46 RECOMMENDATIONS EXAMPLE IOT ANALYTICS ARCHITECTURE ESP to control Alert / KPI s Visualization / Analysis / Root Cause / Model Development & Management Storage ESP Engine Fog Batch ABT Other Data Sources Deploy Other Cloud Applications Production Flow SAS Cloud Company Cloud

47 RECOMMENDATIONS MOVE ANALYTICS TO THE EDGE Sensor Sensor Readings Edge of Network Gateways Operational Apps Operational Data Store Enterprise Data Warehouse Databases Analytics

48 CISCO & SAS GLOBAL ALLIANCE SINCE AUGUST 2016 Announcement: EDGE-ANALYTICS VALUE Leaders Coming Together

49 KEY TAKEAWAY People, process and technology need to work hand in hand so that IoT analytics create value. Be ready to apply analytics into the data stream where real-time action matters. C opyr i g ht 2016, SAS Ins titut e Inc. All rights res er ve d.

50 INTERNET OF THINGS MORE ON IOT Download sas.com/iotebook Visit IoT demo stations Smart Home Demo: The DEMOtic Home Connected Truck Demo: SAS and Intel

51 Thank you #analyticsx C opyr i g ht 2016, SAS Ins titut e Inc. All rights res er ve d.