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Examining Use Cases for Big Data in Banking

出版商 Ovum, Ltd. 商品編碼 296092
出版日期 內容資訊 英文 20 Pages
商品交期: 最快1-2個工作天內
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銀行的巨量資料的使用案例考察 Examining Use Cases for Big Data in Banking
出版日期: 2014年02月04日 內容資訊: 英文 20 Pages


第1章 摘要

  • 發展要素
  • Ovum的見解
  • 主要的訊息

第2章 市場趨勢提高巨量資料的評估

  • 銀行有產生營收和風險/遵守管理的課題
  • 商務上洞察力、風險/遵守管理促進巨量資料的機會
  • 零售銀行的管理資訊系統(MIS)支出預計2018年末達到93億美元

第3章 銀行巨量資料的實際使用案例

  • Bank of America與NoSQL把圖表和表格資料的整合作為必要
    • 巨量資料有助於基礎設施的最佳化
    • 巨量資料促進風險、遵守、客戶分析
  • Citi建立巨量資料平台作為共享資源平台
    • Citi把巨量資料企劃與R&D和商務分開
    • Citi的巨量資料平台是共享資源
    • 巨量資料產生新的商務洞察力,削減儲存費用
  • Zions Bank從來自巨量資料的客戶趨勢活用行銷
    • Zion的巨量資料企劃擴展1個以上的群組
    • 巨量資料的主要優點是來自各種的資訊來源的收集、分析各種資料的能力

第4章 巨量資料企劃全憑資料

  • 資料與流程中包含複數的重複和矛盾
  • 巨量資料企劃由人、流程、技術參與

第5章 提案

  • 對企業的提案
  • 對供應商的提案


Product Code: IT003-000594

The volume of data that can be used for analysis has exploded across the Internet-connected world and is now impacting the banking industry, thanks to growing customer engagement with banking channels and expanding capabilities to track and analyze machine-generated data. Although it is not only used with analytics, Big Data will dramatically extend the footprint of areas such as fraud analytics, customer analytics, and web analytics, and other analytics areas used in banking.


  • Banks are challenged mainly by revenue generation and risk and compliance management, but they are also investing in technology tools that will allow them to achieve these goals. Results from Ovum's ICT Enterprise Insights survey show that the top IT projects of retail and corporate banks and wealth managers are managing enterprise risk, security, and compliance and exploiting information for business insights. Big Data projects specifically enhance areas such as web security, compliance checks, and customer analytics.
  • It is essential for banks to know their data, understand it, and understand the characteristics of the data that they need. Banks need to look to capture more information than they are used to, going beyond risk and marketing data, and treat it as an enterprise asset.

Features Benefits

  • Provides C-level executives' views on current business priorities and business strategies, and demonstrates the potential for Big Data projects to support these.
  • Considers the performance of Big Data initiatives across data management and analysis as well as operational efficiency, innovation, functionality, and agility.

Questions Answers

  • How should I leverage Big Data for current and future banking initiatives?
  • How can I facilitate the launch of a Big Data project at my bank?

Table of Contents



  • Catalyst
  • Ovum view
  • Key messages
    • Big Data will boost the effectiveness of analytics in banking
    • Spending on management information systems (MIS) will hit $9.3bn by the end of 2018
    • Creating a Big Data project is not only a technology issue
    • Banks must discover and understand their data


  • Banks are challenged by revenue generation and risk and compliance management
  • Business insights and risk and compliance management drive opportunities for Big Data
  • Retail banks' spending on MIS will hit $9.3bn by the end of 2018


  • Bank of America sees the need to glue NoSQL data with graph and columnar data
    • Big Data helps to optimize infrastructure
    • Big Data will enhance risk, compliance, and customer analytics
  • Citi has built its Big Data platform as a shared resource platform
    • Citi is taking its Big Data project out of R&D and the business
    • Citi's Big Data platform is a shared resource
    • Big Data provides new business insights and reduces storage costs
  • Zions Bank optimizes marketing with customer insights from Big Data
    • Zions' Big Data project expands beyond one group
    • The main benefit of Big Data is the ability to collect and analyze various types of data from a variety of sources


  • Data and processes are contained in multiple overlapping and conflicting silos
  • Creating a Big Data project will involve people, processes, and technology
    • People: overcoming cultural inertia and turf issues
    • Common processes: setting the ground rules for managing information
    • Technology: getting core data sources and common process in place


  • Recommendations for enterprises
    • The transformation process needs to start at C-level
    • Discover and understand your data
  • Recommendations for vendors
    • Defining the solution category is a significant investment that may result in solid gains
    • Focus on value and innovation


  • Further reading
  • Methodology
  • Author
  • Ovum Consulting
  • Disclaimer


  • Figure 1: Retail banks' top business challenges
  • Figure 2: Retail banks' top IT project areas
  • Figure 3: Retail banks' spending on MIS, 2013-2018
  • Figure 4: Bank of America's visualization of job-processing bottlenecks using Big Data
  • Figure 5: Gluing together Big Data platforms
  • Figure 6: Citi's shared Big Data Platform Map
  • Figure 7: Retail banks' criteria in selecting an IT provider
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