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Global Big Data IT Spending in Financial Sector - Market Research 2015-2019

  • Executive summary
    • Highlights
  • Scope of the report
    • Market overview
    • Top-vendor offerings
      • Table Product, services, and solutions
  • Market research methodology
    • Research methodology
    • Economic indicators
  • Introduction
    • Key market highlights
  • Market Landscape
    • Definition of big data
      • Table Applications of big data
    • Big data in financial services sector
      • Table Applications of big data in financial services sector
    • Market size and forecast
      • Table Global big data IT spending market in financial services sector 2014-2019 ($ billions)
  • Market segmentation by product
    • Table Global big data IT spending market in financial services by product 2014
    • Table Global big data IT spending market in financial services sector by product 2014-2019
    • Hardware revenue
      • Table Hardware segment 2014-2019 ($ billions)
      • Table Hardware revenue segmentation 2014
    • Software segment
      • Table Software segment 2014-2019 ($ billions)
    • Services segment
      • Table Global big data IT spending market in financial services sector 2014-2019 ($ billions)
  • Geographical Segmentation
    • Table Geographical Segmentation 2014
    • Table Geographical segmentation 2014-2019
    • Americas
      • Table Big data IT spending market in financial services sector in Americas 2014-2019 ($ billions)
    • APAC
      • Table Big data IT spending market in financial services sector in APAC 2014-2019 ($ billions)
    • EMEA
      • Table Big data IT spending market in financial services sector in EMEA 2014-2019 ($ billions)
  • Market Growth Drivers
    • Table Activities that determine which solutions vendors adopt
  • Impact of drivers
    • Table Impact of drivers
  • Market Challenges
  • Impact of drivers and challenges
    • Table Impact of drivers and challenges
  • Market Trends
  • Vendor landscape
    • Competitive scenario
    • Other prominent vendors
  • Key vendor analysis
    • Alteryx
      • Table Alteryx: Products
    • Capgemini
      • Table Capgemini: Business segmentation by revenue 2014
      • Table Capgemini: Business segmentation by revenue 2013 and 2014 ($ billion)
      • Table Capgemini: Geographical segmentation by revenue 2014
    • IBM
      • Table IBM: Business segmentation
      • Table Global technology services revenue
    • Oracle
      • Table Oracle: Business segmentation by revenue 2014
      • Table Oracle: Business segmentation by revenue 2013 and 2014 ($ millions)
      • Table Oracle: Geographical segmentation by revenue 2014
    • SAP
      • Table SAP: Business segmentation by revenue 2014
      • Table SAP: Business segmentation by revenue 2013 and 2014 ($ billions)
      • Table SAP: Geographical segmentation by revenue 2014
    • SAS Institute
      • Table SAS: Revenue segmentation by industry 2014
      • Table SAS: Revenue segmentation by geography 2014
  • Appendix
    • List of abbreviations
  • Explore Technavio

About big data IT spending in financial services

The financial services are among the most data-driven industries. Financial services institutions operate within regulatory environments that require firms to store and analyze several years of transactional data. For making the most from the businesses, financial services relies on relational technologies coupled with business intelligence tools to handle the ever increasing data and analytics burden. In today’s world of information, the financial service industry is witnessing a disruptive change in the way do businesses worldwide. Regulatory reforms majorly drive this change. Ailing business and customer settlements, continuous economic crisis in other industry verticals, high cost of new technology and business models, and high degree of industry consolidation and automation are some of the other growth drivers.Many financial services currently focus on improving their traditional data infrastructure as they have been addressing issues such as customer data management, risk, workforce mobility, and multichannel effectiveness. These daily problems led financial organization to deploy big data as a long-term strategy. By the end of 2014, big data has turned out to be the fastest growing technology adopted by the financial institutions over the past five years.

Technavio's analysts forecast global big data IT spending in financial services market to grow at a CAGR of 25.5% over the period 2014-2019.

Covered in this report

This report covers the present scenario and the growth prospects of the global big data IT spending in financial services for 2015-2019. To calculate the market size, the report considers revenue generated from the sale of big data solutions only in the financial services sector. It includes big data hardware, software, and services revenue to calculate the market size. In order to calculate hardware, software, and IT services spending, the following sub-segments have been considered:

  • Hardware: Servers, networking equipment, and storage equipment
  • Software: Apache Hadoop-related solutions and cloud solutions
  • Services: Analytics, consulting, support, and professional services
Technavio's report, Global Big Data IT Spending in Financial Services Market 2015-2019, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers Americas, APAC, and EMEA. It also covers the landscape of the global big data IT spending in financial services market and its growth prospects in the coming years. The report includes a discussion of the key vendors operating in this market.

Key regions
  • Americas
  • APAC
  • EMEA
Key vendors
  • Capgemini
  • IBM
  • Oracle
  • SAP
  • SAS Institute
Other prominent vendors
  • Alteryx
  • Atos
  • Chartio
  • Cirro
  • Clearstory Data
  • Continuum Analytics
  • Datameer
  • DataStax
  • Emc2
  • Enthought
  • Maana
  • MapR technologies
  • Predixion Software
Key market driver
  • Explosive data growth
  • For a full, detailed list, view our report
Key market challenge
  • Lack of big data technology expertise
  • For a full, detailed list, view our report
Key market trend
  • High spending in customer engagements
  • For a full, detailed list, view our report
Key questions answered in this report
  • What will the market size be in 2019 and what will the growth rate be?
  • What are the key market trends?
  • What is driving this market?
  • What are the challenges to market growth?
  • Who are the key vendors in this market space?
  • What are the market opportunities and threats faced by the key vendors?
  • What are the strengths and weaknesses of the key vendors?


Press Release

Technavio Announces the Publication of its Research Report – Global Big Data IT Spending Market in Financial Sector 2015-2019

Technavio recognizes the following companies as the key players in the Global Big Data IT Spending Market in Financial Sector: Alteryx, IBM, SAS, SAP, Capgemini and Oracle

Other Prominent Vendors in the market are: Atos, Chartio, Cirro, Clearstory Data, Continuum Analytics, Datameer, DataStax, EMC, Enthought, Maana, MapR, and Predixion.

Commenting on the report, an analyst from Technavio’s team said: “Social media has started playing a major role in data sourcing among many organizations. This is because of its ability to offer instant feedback about businesses through social networking sites and blogs. Organizations are adding data warehouses with external data sources.”

According to the report, the major reason for growth in investments in marketing technology is the growing desire of companies to automate marketing. Some of the data intelligence tools help companies analyze the scope of improvement in areas such as sales, promotion, and product development.

Further, the report states that one of the major concerns with big data solutions is the difficulty in ascertaining the accuracy of the underlying data that is used to support corporate decision-making.

Companies Mentioned

Alteryx, IBM, SAS, SAP, Capgemini, Oracle, Atos, Chartio, Cirro, Clearstory Data, Continuum Analytics, Datameer, DataStax, EMC, Enthought, Maana, MapR, Predixion.

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