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市場調查報告書

巨量資料及商業智慧:商業智慧和巨量資料分析的匯流

Big Data and Business Intelligence: Convergence of Business Intelligence and Big Data Analytics

出版商 Mind Commerce 商品編碼 312613
出版日期 內容資訊 英文 49 Pages
商品交期: 最快1-2個工作天內
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巨量資料及商業智慧:商業智慧和巨量資料分析的匯流 Big Data and Business Intelligence: Convergence of Business Intelligence and Big Data Analytics
出版日期: 2014年09月15日 內容資訊: 英文 49 Pages
簡介

由於與資料來源及資料提取方式傳動產生的資料量持續劇增,資料收集、分析情勢急速變化。如何從這個一看廣大未體系化的(巨量)資料海,實現最有效率、效果的價值為具大的課題之一。

本報告提供商業智慧(BI)和巨量資料的關係相關資料、優點及規劃、整合相關問題和課題評估。

第1章 摘要整理

第2章 簡介:巨量資料

  • 資料的爆炸性增加
  • 來自內外的資料
  • 巨量資料是什麼?
  • 巨量資料的「V」
  • 巨量資料的實例
  • 不能忽視巨量資料的理由
  • 巨量資料市場
  • 促進巨量資料引進的市場情況
  • 影響巨量資料引進的技術趨勢

第3章 巨量資料:機會、課題

  • 機會、利潤
  • 商務案例、實例
  • 資本化巨大資料的經營理念
  • 巨量資料的巨大問題
  • 巨量資料的法規
  • 巨量資料趨勢
  • 巨量資料的人力資源需求
  • 新的資料科學家
  • 爭取巨量資料的人力資源不足的提示

第4章 巨量資料的投入

  • 巨量資料分析的開發平台
  • 巨量資料的生態系統
  • 巨量資料計劃的開始
  • 巨量資料成功的最佳業務實踐

第5章 商業智慧(BI)

  • 巨量資料如何損害商業智慧?
  • BI帶來怎樣的影響?
  • 商業智慧的預測
  • 主要的商業智慧解決方案供應商

第6章 BI和巨量資料的整合

  • BI-巨量資料整合的優點
  • BI-巨量資料整合的課題
  • 巨量資料平台和BI基礎設施整合的方法
  • BI-巨量資料組成架構的3個階段

第7章 結論、建議

圖表清單

目錄

The landscape of data gathering and analysis is rapidly changing as the amount of data generated in conjunction with data sources and means of extracting data continues to accelerate. One of the key issues is how to most efficiently and effectively realize value from this seemingly boundless sea of unstructured (Big) data.

Big Data is much more than its technical definition implies: A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tool. Big Data is already changing the way business decisions are made since big data exceeds the capacity and capabilities of conventional storage, reporting and analytics systems, it demands new problem-solving approaches.

Business Intelligence (BI) represents a set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI has existed in various forms for a long time but arguably is lacking when it comes to unstructured data.

This research evaluates the relationship between BI and Big Data including benefits, issues, and challenges in terms of planning and integration. The report also answers important questions such as:

  • Is BI being replaced by Big Data approaches?
  • How is Big Data clouding Business Intelligence?
  • What are the important steps in BI-Big Data integration?

All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Report Benefits:

  • Understand why we can't ignore Big Data, and what new insights Big Data can provide that BI can't today
  • look at limitations and risks involved in handling large unstructured data for better business decision making
  • Learn why there is a need to marry Big Data and BI solutions and the associated benefits and challenges
  • Learn the questions every organization should consider and find answers to them in order to overcome the roadblocks in implementing new data technologies that make the Big Data ecosystem

Table of Contents

1.0. EXECUTIVE SUMMARY

  • 1.1. OVERVIEW
  • 1.2. KEY BENEFITS
  • 1.3. QUESTIONS ANSWERED BY REPORT
  • 1.4. TARGET AUDIENCE

2.0. INTRODUCTION TO BIG DATA

  • 2.1. DATA EXPLOSION
  • 2.2. DATA FROM INSIDE AND OUTSIDE
  • 2.3. WHAT IS BIG DATA?
  • 2.4. THE V'S OF BIG DATA
  • 2.5. A SAMPLING OF BIG DATA FACTS
  • 2.6. WHY ONE CAN'T IGNORE BIG DATA
  • 2.7. BIG DATA MARKET
  • 2.8. MARKET CONDITIONS THAT ARE DRIVING BIG DATA ADOPTION
  • 2.9. TECHNOLOGY TRENDS INFLUENCING BIG DATA ADOPTION

3.0. BIG DATA: OPPORTUNITIES AND CHALLENGES

  • 3.1. OPPORTUNITIES AND REWARDS
  • 3.2. BUSINESS CASES AND EXAMPLES
  • 3.3. BUSINESS IDEAS TO CAPITALIZE ON HUMONGOUS DATA
  • 3.4. BIG DATA'S BIG PROBLEMS
  • 3.5. BIG DATA REGULATION
  • 3.6. BIG DATA TRENDS 2014
  • 3.7. BIG DATA TALENT REQUIREMENT
  • 3.8. THE NEW DATA SCIENTIST
  • 3.9. TIPS FOR WINNING OVER BIG DATA TALENT SHORTAGE

4.0. PUTTING BIG DATA TO WORK

  • 4.1. BIG DATA ANALYTICS PIPELINE
  • 4.2. BIG DATA ECOSYSTEM
  • 4.3. GETTING STARTED WITH A BIG DATA PROJECT
  • 4.4. BEST PRACTICES IN BIG DATA SUCCESS

5.0. BUSINESS INTELLIGENCE (BI)

  • 5.1. HOW BIG DATA IS CLOUDING BUSINESS INTELLIGENCE
  • 5.2. HOW IS BI GETTING IMPACTED?
  • 5.3. PREDICTIONS FOR BUSINESS INTELLIGENCE
  • 5.4. KEY BUSINESS INTELLIGENCE SOLUTIONS PROVIDERS

6.0. BI AND BIG DATA INTEGRATION

  • 6.1. ADVANTAGES OF BI-BIG DATA INTEGRATION
  • 6.2. CHALLENGES IN BI-BIG DATA INTEGRATION
  • 6.3. APPROACHES FOR INTEGRATING BIG DATA PLATFORM WITH BI INFRASTRUCTURE
  • 6.4. THREE STEPS TO BI-BIG DATA FRAMEWORK

7.0. CONCLUSIONS AND RECOMMENDATIONS

List of Figures

  • Figure 1: How the Internet is Collecting Data
  • Figure 2: The V's of Big Data
  • Figure 3: Big Data Market Forecast, 2011-2017 ( in $US Billion)
  • Figure 4: Market Conditions Driving Adoption of Big Data
  • Figure 5: Strategies for Making Data Profitable
  • Figure 6: Big Data's Darker Side
  • Figure 7: Key Regulatory Areas for Big Data Growth
  • Figure 8: Big Data Talent Requirement
  • Figure 9: Demand Supply Gap for Data Scientists
  • Figure 10: Who is the New Data Scientist?
  • Figure 11: Winning Over the Talent Shortage
  • Figure 12: Big Data Analytics Pipeline
  • Figure 13: Big Data Ecosystem
  • Figure 14: Getting Started with Big Data
  • Figure 15: Best Practices in Big Data Success
  • Figure 16: Challenges in Integration of BI and Big Data Systems
  • Figure 17: Approaches to Integrating BI Infrastructure to Big Data
  • Figure 18: BI Big Data Framework
  • Figure 19: Three Steps to Bi Big Data Framework
  • Figure 20: Global Big Data Revenue 2014 - 2019
  • Figure 21: Big Data Revenue by Region

List of Tables

  • Table 1: Key Differences between BI & Big Data Analytics
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