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Visualizing IOT Data for Smarter Decision Support Systems

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Presentation on theme: "Visualizing IOT Data for Smarter Decision Support Systems"— Presentation transcript:

1 Visualizing IOT Data for Smarter Decision Support Systems
Devi Kyanam & Joe Wartick January 12th, 2017

2 Agenda Background Problem Definition High-level Architecture
Visualization Q & A

3 IOT and Connected Devices
Exponentially increasing development of machines that can communicate data to other machines Retrofitting options for existing devices to allow them to communicate machine to machine (making them “Smart”) Estimates of devices capable to machine to machine communication (“Smart” Devices): 2012 = 5 billion devices Today = 20 billion devices 2020 = 50 billion devices

4 Application of Internet of Things in Target Property Management
Enables ability to remotely control and troubleshoot in-store assets from Target Headquarters such as Refrigeration, Heating, Ventilation, Cooling, and Lighting equipment Allows implementation of efficient data driven asset and energy management strategies near real time

5 Properties Network Enabled Assets
HVAC: Zone Temperatures Component Functionality Influences Guest Comfort / Energy Lighting: Lighting Status (%) Scheduled Changes to Lighting Influences Brand / Energy Refrigeration: Case Temperatures / Dew Points Component Functionality Influences Food Spoilage / Energy Electrical Submetering: Electrical Usage by Area Detect Power Outages / Surges Influences Energy Irrigation: Water Consumption / Runtime Potential Leak Detection Influences Brand / Water Usage Trash Compactor: Pressure within Unit Estimates Fullness of Unit Influences Hauling Costs

6 Store View - Retail Perspective

7 Store View – Asset / Equipment Perspective
* Note: Numbers and metrics displayed are not accurate and only for illustration

8 Problem: How to identify problematic dehumidification units
Context: Condensation during summer months is problem for retailers across the industry. Dehumidification units are designed to reduce humidity in stores, resulting in less condensation. What could be done to proactively address condensation? Can we use data to identify and rectify problematic dehumidification units? How do we communicate to members of field operations teams to address issues effectively? How do we keep this communication simple and actionable?

9 High Level Architecture

10 Visualization: Communicating Summary Level Data

11 Visualization: Communicating Detailed Level Data

12 Visualization: Key Takeaways
What action should be taken based on the visualizations that you create and what metric result will signal that action Summary metrics should shape high-level decision making and users should know what action will be driven from these visualizations when certain A visualization that does not provide value will quickly be discarded by users Involve your users in creating visualization to ensure it is most effective


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