Cover Image

自助服務可視化商業智慧 (BI) /分析

Market Landscape: Self-Service Visual Business Intelligence/Analytics, 2016

出版商 Ovum, Ltd. 商品編碼 364095
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
Back to Top
自助服務可視化商業智慧 (BI) /分析 Market Landscape: Self-Service Visual Business Intelligence/Analytics, 2016
出版日期: 2016年07月07日 內容資訊: 英文





  • 使用了反映優勢的策略的市場進入


  • 怎麼選擇市場播放器?


  • 自助服務分析企業分析的消費者人體像
  • 自助服務分析技術的超級堆疊
  • 企業的自助服務分析解決方案引進的促進要素


  • 自助服務分析供應商多的尺寸、風味登場
  • 市場某種程度穩定前進
  • 自助服務分析市場展望
  • 自助服務分析的價值鏈
  • 業者情勢
  • 生態系統的互相依賴、影響


  • 對自助服務BI而言進化的試驗指標的開端
  • 整體情形 vs. 正確的情形


  • IBM
  • Oracle
  • Microsoft
  • SAS
  • SAP
  • Tableau
  • Qlik
  • Chartio
  • Logi
  • MicroStrategy
  • Domo
  • Yellowfin
  • GoodData
  • Birst
  • Amazon QuickSight
  • Salesforce
  • Platfora
  • Datameer
  • Looker Data Sciences


Product Code: IT0014-003120

Eighteen vendors/products are covered in this report, including Amazon (QuickSight), Birst, Chartio, Datameer, Domo, GoodData, IBM, Logi Analytics, Looker, Microsoft, MicroStrategy, Oracle, Platfora, Qlik, Salesforce Wave, SAS, Tableau, and Yellowfin.


  • Large enterprise adoption and the active participation of mega-vendors validates the importance of this market. It will likely consume the world of curated analytics, or be subsumed by it; coexistence is imperative.
  • "Where do I sit in the value chain?" is one of the most pressing questions for vendors, prompting vendors of all sorts and sizes to actively seek information validating their relevance, strategy, and opportunity in the self-service analytics market.
  • Assesses the market landscape for self-service analytics.
  • Details the key enterprise adoption drivers for self-service analytics.

Features Benefits

  • Assesses the market landscape for self-service analytics.
  • Details the key enterprise adoption drivers for self-service analytics.

Questions Answers

  • Who are the key vendors in this market and how do they stack up against each other?
  • What are the key differentiators of market vendors?

Table of Contents


  • Catalyst
  • Key messages
  • Ovum view

Recommendations for vendors

  • Go to market with strategies that reflect your strengths

Recommendations for enterprises

  • How do I select among market players?

Defining and exploring self-service analytics

  • Self-service analytics is the consumer persona of enterprise analytics
  • The self-service analytics technology super-stack
  • Enterprise adoption drivers for self-service analytics solutions

Market landscape and participants

  • Self-service analytics providers come in many sizes and flavors
  • A market moving towards some consolidation
  • Self-service analytics market outlook
  • Self-service analytics value chain
  • Vendor landscape
  • Ecosystem interdependencies and influences

Self-service analytics: Where is it heading next?

  • Self-service is the beginning of the evolutionary endpoint for BI
  • Big picture versus exact picture: We don't believe in the "two camps" theory

Vendor profiles

  • IBM: Bringing cognitive to self-service
  • Oracle: Embedded self-service on functional/industry clouds
  • Microsoft: The most ubiquitous self-service analytics vendor
  • SAS: Self-service for a broad sweep of users, novice to expert
  • SAP: Self-service paradigm across a massive portfolio
  • Tableau: The pioneer of self-service of analytics, going strong
  • Qlik: Visual analytics for the enterprise
  • Chartio: Agile analytics solution growing quickly past its start-up status
  • Logi: Visualization, analytics, and embedding
  • MicroStrategy: Version 10 is all about enterprise data discovery
  • Domo: Facebook meets self-service analytics
  • Yellowfin: Collaborative dashboarding and reporting
  • GoodData: Cloud analytics geared to "data monetization"
  • Birst: Networked analytics with self-service
  • Amazon QuickSight: "Aha moment" - native analytics for Amazon data
  • Salesforce: Ramping up on the adoption curve
  • Platfora: Big Data Discovery on Apache Hadoop and Spark
  • Datameer: Data discovery for Hadoop, end to end
  • Looker Data Sciences: Analytics on live data


  • Methodology
  • Further reading
  • Author
Back to Top