Big Data And The Internet of Things: Gaining Actionable Insight From Raw Data

Transcription

1 Big Data And The Internet of Things: Gaining Actionable Insight From Raw Data Paul Hepperla, Director Enterprise Prod. Mgmt And New Solutions Development, Emerson

2 What We ll Cover An Example Definitions Industry Success Of Big Data A Facilities Related Perspective How Your Organization Can Move Forward 2

3 38⁰F Assume 150 Temperature Sensors In One Store Reading Every 5 Seconds In Real Time Logged Once Every 30 Seconds 1b Per Read 3

4 One Dot = 1 Second Each Block = 100 Seconds Entire Block = 1 Day 1 Day = 24 Hours 24 Hours = 1,440 Minutes 1440 Minutes = 86,400 Seconds 38⁰F 12:45:45 PM EST Logged Once Every 30 Seconds 2880 Readings 2,880B Per Temp Sensor 432,000B Or 4.32MB Per Sensor 4

5 Circuit 2 38⁰F 12:52 PM EST 5 Circuits Per Store 5

6 Rack A Circuit 2 38⁰F 12:52 PM EST 5 Circuits Per Store 2 Racks Per Store 2.5 Circuits Per Rack 6

7 4.32MB x 10 x 2.5 x 2 = 216MB For Temp Sensor Reads Per Day 78,840MB Or 78.84GB Per Year For Temp Sensors Readings For Each Store Rack A Circuit 2 38⁰F 12:52 PM EST MT Case 2 Racks Per Store 2.5 Circuits Per Rack 10 Cases Per Circuit One Temp Sensor Per Case 7

8 Big Data: Do We Qualify? Volume Does Our Situation Have Volume? Velocity Are We Producing Data At A High Speed? Variety Does It Have Variety? Veracity Can We Trust The Data? 8

9 Addressing Variety 9

10 Variety Increased Design Consultants Competition OEMs Weather ERP Contractors Sustainability Real Estate Food Safety External IT 10

11 Definitions M2M Machine-To-Machine IoT Internet Of Things Wireless Trend Rank Query Ad-Hoc FDD Fault Detection Analytics Benchmark Reporting Dashboard Ask A Question About Something, Especially In Order To Express One's Doubts About It Or To Check Its Validity Or Accuracy Technologies That Allow Both Wireless And Wired Systems To Communicate With Other Devices Of The Same Type Scenario In Which Objects, Animals Or People Are Provided With Unique Identifiers And The Ability To Transfer Data Over A Network Without Requiring Human- To-Human Or Human-To-Computer Interaction Formed, Arranged, Or Done For A Particular Purpose Only Pinpointing One Or More Root Causes Of Problems, To The Point Where Corrective Action Can Be Taken Set Of Indicators That Relate To The Observable Environment Formatted And Organized Presentation Of Data The Systematic Computational Analysis Of Data Or Statistics 11

12 Definitions M2M Machine-To-Machine IoT Internet Of Things Wireless Trend Rank Query Ad-Hoc FDD Fault Detection Analytics Benchmark Reporting Dashboard Ask A Question About Something, Especially In Order To Express One's Doubts About It Or To Check Its Validity Or Accuracy Technologies That Allow Both Wireless And Wired Systems To Communicate With Other Devices Of The Same Type Scenario In Which Objects, Animals Or People Are Provided With Unique Identifiers And The Ability To Transfer Data Over A Network Without Requiring Human- To-Human Or Human-To-Computer Interaction Formed, Arranged, Or Done For A Particular Purpose Only Pinpointing One Or More Root Causes Of Problems, To The Point Where Corrective Action Can Be Taken Set Of Indicators That Relate To The Observable Environment Formatted And Organized Presentation Of Data The Systematic Computational Analysis Of Data Or Statistics 12

13 Industry Success How Many Companies Have Implemented Big Data? 35% How Much Was/Will Be Spent on Big Data in 2013? 2020? $31B/$114B Of Those Who Had Implemented, How Many Are Successful? 27% Or 9.5% Of ALL Have Been Successful Source: Capgemini, titled Successful Big Data Implementations Elude Most Organizations 13

14 Reasons For Failure Dealing With Scattered Data Silos 79% Have Not Integrated Data Sources Ineffective Coordination Of Analytics Initiatives 67% Are Lacking Well Defined Success Criteria Lack Of A Clear Business Case For Funding Big Data Initiatives 54% Have No Joint IT/Business Collaboration Key Differentiator of Success: Dedicated Analytics Teams 14

15 A Facilities Perspective Data Organization 1 Cloud Provider (Emerson) Customer s Enterprise X,000s Site Or Location X00,000s Device/Equipment XX,000,000s Sensor 15

16 Use Cases Data Flow / Organization 16

17 How Your Organization Moves Forward What Problem Are You Trying To Solve? Do You Have The Data To Help You? Where Is It? How Can You Aggregate It? Can You Normalize It? Can You Correlate It? 17

18 An Example Hypothesis: Setpoint Changes Increasing Energy Use Interval Meter Usage Interval Meter Multiplier Interval Meter Time/Date Stamp Setpoint Value Actual Values Change Occurrence Time/Date Actual Value Before/After Time/Date Asset Identified In Setpoint Change User Who Accessed System Setpoint Approval Standard Asset Performance Store Issues Data Elements Required Potential Correlation Every Time A Setpoint Change Occurs, Understand Energy Impact Contractor Performance On Energy Use Why Setpoints Are Being Changed Projected Benefits Energy Baseline Held Contractor/Technician Management Root Cause Identification 18

19 Key Points To Take Away We Are Relevant In Big Data More Devices Are Communicating Success Is Not A Guarantee Definitions Are Important Data Aggregation Can Be Difficult There Are Business Cases Everywhere Emerson Can Help Have You Considered Us As An Big Data Applied Data Provider? 19

20 Contact Paul Hepperla