1. Executive Summary
1.1 Introduction
1.2 Market Forecast
2. Market and Technology Issues
2.1 Energy Cloud Driving Data Generation
2.2 Data Generation Driving Business Transformation
2.3 Business Transformation Driving Data Management
2.4 Defining Data Management
2.4.1 Connected Devices
2.4.2 Edge Computing
2.4.3 Data Storage
2.4.3.1 Time-Series Databases
2.4.3.2 Historian
2.4.3.3 Data Warehouse
2.4.3.4 Data Lake
2.4.3.5 Use Case: Green Mountain Power
2.4.3.6 Use Case: Narragansett Electric Company
2.4.4 Data Platforms
2.4.4.1 Use Case: Adger Energi
2.4.4.2 Growing Vendor Landscape
2.4.4.3 Use Case: Enel
2.4.5 IoT Analytics
2.5 Market Drivers
2.5.1 Digital Transformation
2.5.2 Declining Costs
2.5.3 Security Threats
2.5.4 Asset Management (Predictive Maintenance)
2.5.5 Regulation
2.5.5.1 General Data Protection Regulation
2.5.5.2 California Consumer Privacy Act of 2018
2.6 Market Challenges
2.6.1 Financial Constraints
2.6.2 Skills Shortage
2.6.3 Data Quality
2.6.4 Silos Hinder Collaboration
2.6.5 Uncertain Outcomes
2.6.6 Complexity
2.6.7 Cloud-Averse
3. Market Forecasts
3.1 Forecast Methodology
3.1.1 Devices
3.1.2 Edge Computing
3.1.3 Data Storage
3.1.4 Data Platforms
3.1.5 IoT Analytics
3.2 Devices
3.3 Edge Computing
3.4 Data Storage
3.5 Data Platforms
3.6 IoT Analytics
3.7 Conclusions and Recommendations
3.7.1 Know Your Data
3.7.2 Focus on Use Cases and Value Propositions
3.7.3 Commit Permanently to Data Management
3.7.4 Create a Storage Strategy
3.7.5 Cut Through the Analytics Hype
3.7.6 Establish at the Executive-Level
4. Acronym and Abbreviation List
5. Table of Contents
6. Table of Charts and Figures
7. Scope of Study, Sources and Methodology, Notes