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市場調查報告書

跟資訊與問題點的適合:實際世界的諸產業上巨量資料策略的設計

Information Meets Matter: Devising Big Data Strategies for Real-World Industries

出版商 Lux Research 商品編碼 329615
出版日期 內容資訊 英文 27 Pages
商品交期: 最快1-2個工作天內
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跟資訊與問題點的適合:實際世界的諸產業上巨量資料策略的設計 Information Meets Matter: Devising Big Data Strategies for Real-World Industries
出版日期: 2015年04月02日 內容資訊: 英文 27 Pages
簡介

隨著感測器和網絡連接設備的台數增加與高度化,所得到的資訊數、量、速度也在擴大/上升中,為此,巨量資料便日益熱門。以物質為準的產業,精緻農業、製造業上的「產業4.0」、數位油田、智慧型城市等領域也對活用巨量資料愈來愈有興趣。但這些領域的巨量資料風險毫無疑地愈來愈大,其優點又不如銀行、媒體用資訊產業的情況那般明確。巨量資料策略在發展時(現實世界中)各產業不應一口氣全面引進、活用,而應該要配合應用案例開始引進。

本報告提供各種產業上巨量資料應如何有效引進·活用之相關分析,提供您到目前為止的引進案例和問題點,每項產業的引進效果,今後更普及的步驟·各種問題的解決方法等相關之調查與考察。

摘要整理

市場環境

  • 隨著來自零售產業和通訊業的大金額支出,巨量資料也成了「大生意」,但其所提供的服務,尚未符合客戶產業方面複雜的設備環境,龐大的處理產品,持續移動的產品等現狀。

分析

  • 由於巨量資料部門的黑字化,各企業需要探索策略性需求,有助於籌劃適切IoT引進計劃的利用案例

未來展望

註腳

表格一覽

  • 圖:巨量資料支出額:有全體擴大趨勢
  • 圖:巨量資料的質性·主觀的·利己的定義
  • 表格:巨量資料的普及途徑與行動通訊和Web類似:IoT的情況也一樣嗎?
  • 表格:各產業針對引進巨量資料的配合措施
  • 圖:IoT·巨量資料·雲端運算·分析功能的資料收支力之相互關係
  • 圖:巨量資料·雲端·分析功能的子市場區隔和供應商
  • 圖:在發展巨量資料策略時,應該開始關注最後目的點
  • 表格:物質型產業上巨量資料·計劃的策略性·戰術性目標的設定
  • 圖:每產業叢集的巨量資料的優點:流程/產品線的評估
  • 表格:物質型產業上各產品的巨量資料使用案例
  • 表格:化學工業的製造流程上巨量資料的使用案例和供應商
  • 圖:企業內部結構·要素下的使用案例明細
  • 表格:IoT/感測器資料調查:石油、天然氣幫浦的假設·案例
  • 圖:對各產業來說的巨量資料之意義-基礎資料,獨家分析,創新的經營模式
目錄

Big data is a moving target, as a growing number of sensors and sophistication of connected objects increase the volume, velocity, and variety of information in the world. Material-centric industries chase big data visions like precision agriculture, "Industry 4.0" in manufacturing, digital oilfields, and intelligent cities. But the benefits to them are less clear than to information industries like banking and media, while risks are greater. To develop a big data strategy, real-world industries need to start with use cases before leaping into hype.

Table of Contents

EXECUTIVE SUMMARY

LANDSCAPE

Big data is big business, driven by billions in spending by retailers and telcos. But offerings don't yet fit physical industries' complex equipment, tons of materials, and moving products.

ANALYSIS

To transform big data promises into profits, industrial firms need use cases that address strategic needs, and plan IoT deployment accordingly.

OUTLOOK

ENDNOTES

TABLE OF FIGURES

  • Figure 1: Graphic Estimates of Big Data Spending Are Scattered but Consistently Climbing
  • Figure 2: Graphic Big Data Definitions Are Qualitative, Subjective, and Self-interested
  • Figure 3: Table Big Data Disruption Follows a Particular Pattern Across Mobile and Web; Will IoT Follow?
  • Figure 4: Table Industry Monikers for Big Data-related Initiatives
  • Figure 5: Graphic IoT, Big Data, Cloud Computing, and Analytics Feed One Another
  • Figure 6: Graphic Subsegments and Vendors in Big Data, Cloud, and Analytics
  • Figure 7: Graphic To Develop a Big Data Strategy, Begin with the End in Mind
  • Figure 8: Table Setting Tactical and Strategic Goals for Big Data Projects in Material-Centric Industries
  • Figure 9: Graphic Benefits of Big Data by Industry Cluster Along Process and Product Lines
  • Figure 10: Table Sample Big Data Use Cases for Manufactured goods in Physical Industries
  • Figure 11: Table Big Data Use Cases and Vendors in the Chemical Manufacturing Process
  • Figure 12: Graphic Break Down the Use Case Into Business Case Factors
  • Figure 13: Table IoT/Sensor Data Survey - Hypothetical Oil and Gas Pump Case
  • Figure 14: Graphic Big Data for Industry Means Primary Data, Proprietary Analytics, and Innovative Business Models
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