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ユーティリティ向けビッグデータ管理の世界市場分析・予測:コネクテッドデバイス、エッジコンピューティング、データストレージ、データプラットフォーム、IoTアナリティクス

Big Data Management for Utilities - Devices, Edge Computing, Data Storage, Platforms and Analytics: Global Market Analysis and Forecasts

出版日: | 発行: Guidehouse Insights (formerly Navigant Research) | ページ情報: 英文 41 Pages; 30 Tables, Charts & Figures | 納期: 即納可能 即納可能とは

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ユーティリティ向けビッグデータ管理の世界市場分析・予測:コネクテッドデバイス、エッジコンピューティング、データストレージ、データプラットフォーム、IoTアナリティクス
出版日: 2019年05月21日
発行: Guidehouse Insights (formerly Navigant Research)
ページ情報: 英文 41 Pages; 30 Tables, Charts & Figures
納期: 即納可能 即納可能とは
  • 全表示
  • 概要
  • 図表
  • 目次
概要

当レポートでは、ユーティリティ産業におけるデータプラットフォームおよびその他のビッグデータ管理ソリューションの主な動向、促進要因、および障壁について分析し、技術・用途・地域別による世界市場の予測、データ管理の各種用途 (データプラットフォーム、コネクテッドデバイス、エッジコンピューティング、データストレージ、IoTアナリティクス) の分析などを提供しています。

第1章 エグゼクティブサマリー

第2章 市場・技術課題

  • エネルギークラウドが促進しるデータ生成
  • データ生成が促進するビジネス転換
  • ビジネス転換が促進するデータ管理
  • データ管理の定義
    • コネクテッドデバイス
    • エッジコンピューティング
    • データストレージ
    • データプラットフォーム
  • 市場成長促進要因
    • デジタル転換
    • コスト削減
    • セキュリティ脅威
    • 資産管理 (予知保全)
    • 規制
  • 市場課題
    • 財務上の抑制事項
    • 人材不足
    • データ品質
    • コラボレーションを阻害するサイロ
    • 不確実な結果
    • 複雑さ
    • クラウド回避型

第3章 市場予測

  • 予測手法
  • デバイス
  • エッジコンピューティング
  • データストレージ
  • データプラットフォーム
  • IoTアナリティクス
  • 結論・提言

第4章 頭字語・略語リスト

第5章 目次

第6章 図表

第7章 調査範囲・情報ソース・調査手法・注記

図表

List of Charts and Figures

  • Device and Data Management Revenue by Region, World Markets: 2018-2027
  • IoT Device Revenue by Region, World Markets: 2018-2027
  • IT and Analytics for Edge Computing Revenue by Region, World Markets: 2018-2027
  • Data Storage Revenue by Region, World Markets: 2018-2027
  • Data Platform Revenue by Region, World Markets: 2018-2027
  • IoT Analytics Revenue by Region, World Markets: 2018-2027
  • The Evolution of Grid Intelligence
  • Data Lake Architecture

List of Tables

  • Data Platform Vendors
  • Device and Data Management Revenue by Region, World Markets: 2018-2027
  • Device and Data Management Revenue by Technology Type, World Markets: 2018-2027
  • Device and Data Management Revenue by Technology Type, North America: 2018-2027
  • Device and Data Management Revenue by Technology Type, Europe: 2018-2027
  • Device and Data Management Revenue by Technology Type, Asia Pacific: 2018-2027
  • Device and Data Management Revenue by Technology Type, Latin America: 2018-2027
  • Device and Data Management Revenue by Technology Type, Middle East & Africa: 2018-2027
  • Device and Data Management Revenue and Market Share by Category, World Markets: 2018-2027
  • IoT Device Revenue by Region, World Markets: 2018-2027
  • IoT Device Shipments by Region, World Markets: 2018-2027
  • IoT Device Installed Base by Region, World Markets: 2018-2027
  • IT and Analytics for Edge Computing Revenue by Region, World Markets: 2018-2027
  • IT and Analytics for Edge Computing Revenue by Technology, World Markets: 2018-2027
  • Data Storage Revenue by Region, World Markets: 2018-2027
  • Data Platform Revenue by Region, World Markets: 2018-2027
  • IoT Analytics Revenue by Region, World Markets: 2018-2027
  • IoT Analytics Revenue by Category, North America: 2018-2027
  • IoT Analytics Revenue by Category, Europe: 2018-2027
  • IoT Analytics Revenue by Category, Asia Pacific: 2018-2027
  • IoT Analytics Revenue by Category, Latin America: 2018-2027
  • IoT Analytics Revenue by Category, Middle East & Africa: 2018-2027
目次
Product Code: MF-DPBD-19

Data is changing the utility market. It frees up capital through efficiency savings, creates new digital products and services, and helps improve understanding of customers. Utilities are increasingly interested in big data management, whether at the device-level (edge computing) or in the back-office (databases, lakes, warehouses). The next evolution in the data management space comes from the advancement of Internet of Things (IoT) analytics platforms. These solutions include most functionality for the process of analyzing data from connected devices: data acquisition, preparation, cleansing, storage, integration, analysis, and the delivery of insights.

Despite the profusion of data management products, IoT platform adoption remains low, with few customers using these platforms at scale. Until the market further matures, utilities are likely to continue using traditional data stores in lieu of IoT platforms. Although traditional storage options such as historians, databases, and data lakes are often sufficient for utilities in 2019, they may not be in coming years.

This Navigant Research report analyzes the key trends, drivers, and barriers to adoption of data platforms and other big data management solutions within the utility industry. The study includes several submarkets within the utility data management space alongside global market adoption trends. Global market forecasts, segmented by technology, application, and region, extend through 2027. The report examines different application segments for data management: data platforms, connected devices, edge computing, data storage, and IoT analytics.

Key Questions Addressed

  • Which data management technologies present the largest market potential for vendors?
  • What factors are helping to drive the market for data management technologies?
  • What factors are inhibiting the market potential of data management technologies?
  • What are the most important trends in the data management space?
  • How large will the market for data management technologies grow and which global regions are growing the fastest?

Who Needs This Report

  • Utilities and grid operators
  • Communications vendors and service providers
  • Smart grid vendors and service providers
  • Non-utility providers
  • Systems integrators
  • Government agencies
  • Research community
  • Investor community

Table of Contents

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

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