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Cable Mining Its Big Data to Improve Service Performance

出版商 Heavy Reading 商品編碼 356651
出版日期 內容資訊 英文 16 Pages
商品交期: 最快1-2個工作天內
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纜線開採:為了改善服務性能的巨量資料 Cable Mining Its Big Data to Improve Service Performance
出版日期: 2016年04月22日 內容資訊: 英文 16 Pages

本報告以纜線供應商的BDA (巨量資料分析) 的利用為焦點,提供纜線供應商考慮巨量資料利用的5個主要業績指標 (KPI) 的領域 (網路性能,服務性能,顧客服務,預測建模及虛擬化) 識別,主要供應商比較,為您概述為以下內容。

第1章 摘要整理

第2章 市場概要

  • 背景
  • 資料來源

第3章 機會、課題

  • 機會
  • 纜線供應商的計劃
  • 課題
    • 資料數量、多樣性
    • 多數的資源、筒倉
    • 開放原始碼、標準化
    • 成本、優先權
    • 資料的預測
    • 擁有、共享
    • 競爭壓力
    • 安全
    • 法規的課題

第4章 未來技術

  • 未來的需求
  • 新的功能
    • 虛擬化
    • 機器學習、認知式運算
  • 供應商市場比較

第5章 結論



For years, cable providers have been saddled with legacy billing systems that hampered their ability to add services or enhance customer relationships in a meaningful way. While other industries talked of new operations/business support system (OSS/BSS) capabilities, customer relationship management (CRM) solutions and data mining, cable has remained in the dark ages of data analysis in many respects. And yet, cable providers have been collecting a treasure trove of data from their billing systems, call centers, delivery infrastructure, daily operations and the millions of set-top box (STB) and cable modem activities that Americans engage in every day.

This report identifies five key performance indicator (KPI) areas where cable providers are seeking to apply big data: network performance, service performance, customer care, predictive modeling and virtualization. These five categories cover the gamut of cable activities, from network management and service delivery to operations and customer experience. Real-world use cases are taking root in the field in which providers are discovering new ways to combine and utilize their big data. The magic of big data analytics (BDA) occurs when data is aggregated from various sources to offer new insights into a situation and identify proactive steps for service improvements.

Big data comes with big promises, but it also comes with big challenges. There are many hurdles to transforming yesterday's cable operations into tomorrow's super-networks. First, cable providers must have the capability to get their arms around all of their data and figure out what it means. Data comes from multiple sources and in different forms, including billing systems, call centers, STBs, modems, edge routers, network monitoring systems and on and on. The data needs to be aggregated and analyzed in actionable and proactive ways.

‘Cable Mining Its Big Data to Improve Service Performance’ describes the ways that cable providers are exploring BDA to improve performance on a variety of fronts. The report also includes a comparison of key suppliers that are working with U.S. cable providers on data analytics technology.

Cable operations exist in a sea of data. From headends and data centers to billing systems and CPE, data points flow to and fro throughout a cable ecosystem. Once collected in data lakes or warehouses, that data can be curated and analyzed to provide meaningful intelligence to keep operations running smoothly and improve performance overall. The following excerpt illustrates how cable lives in a sea of data. Virtually every part of a cable enterprise creates, collects or uses data to inform, operate or manage daily operations. The data sources identified here provide a comprehensive list but probably still do not encompass all of the available sources.

‘Cable Mining Its Big Data to Improve Service Performance’ is published in PDF format.

Table of Contents


  • 1.1. Key Findings


  • 2.1. Background
  • 2.2. Data Sources


  • 3.1. Opportunities
  • 3.2. Cable Provider Plans
  • 3.3. Challenges
    • Volume & Variety of Data
    • Multiple Sources & Silos
    • Open Source & Standardization
    • Costs & Priorities
    • Making Data Predictive
    • Ownership & Sharing
    • Competitive Pressure
    • Security
    • Regulatory Issues


  • 4.1. Future Demands
  • 4.2. Emerging Capabilities
    • Virtualization
    • Machine Learning & Cognitive Computing
  • 4.3. Supplier Market Comparison



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