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Big Data in Financial Services Industry: Market Trends, Challenges, and Prospects 2013 – 2018

Big Data in Financial Services Industry: Market Trends, Challenges, and Prospects 2013 – 2018

Big Data is making a big impact already in certain industries such as the healthcare, industrial, and retail sectors. With the exception of the government sector, no other industry has more to gain from leveraging Big Data than the financial services sector. Big Data technology will help financial institutions maximize the value of data and gain competitive advantage, minimize costs, convert challenges to opportunities, and minimize risk in real-time.

Big Data technologies provide financial services firms with the capability to capture and analyze data, build predictive models, back-test and simulate scenarios. Through iteration, firms will determine the most important variables and also key predictive models.

There is a huge opportunity for financial services firms to apply new data sets and new algorithms to optimize capital allocation, cash management, and currency processing. The financial implications are manifest in improved capital flows and profitability for many firms within the ecosystem.

This report evaluates Big Data prospects and opportunities within the financial services sector and answers the following key questions:

  • How is Big Data expected to impact the financial services industry?
  • What are the Big Data players financial management solutions and their impact?
  • What are the Big Data financial management models and how are they applied?
  • What are the near-term and long-term benefits to the financial services industry?
  • What are the specific challenges that the financial services industry faces with Big Data?
The report also analyzes Big Data prospects for financial services within the emerging markets including Brazil, China, and India.

Target Audience:
  • Big Data companies
  • Telecom service providers
  • Financial services companies
  • Data services and analytics companies
  • Cloud and telecom infrastructure providers
Companies in Report:
  • 1010DATA
  • 10GEN 65
  • ACTIAN
  • ALTERYX
  • AMAZON
  • ATTIVIO
  • BMC
  • BOOZ ALLEN HAMILTON
  • CAPGEMINI
  • CISCO SYSTEMS
  • CLOUDERA
  • CSC
  • DELL
  • EMC
  • FUSION-IO
  • GOODDATA
  • GOOGLE
  • GUAVUS
  • HITACHI
  • HP
  • IBM
  • INFORMATICA
  • INTEL
  • MARKLOGIC
  • MICROSOFT
  • MU SIGMA
  • NETAPP
  • OPERA SOLUTIONS
  • ORACLE
  • PARACCEL
  • QLIKTECH
  • SAP
  • SGI
  • SPLUNK
  • TERADATA
  • TIBCO SOFTWARE
  • VMWARE


General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


Executive Summary
Introduction
Big Data Market Trends
1.1 The Global Big Data Market
1.2 The Big Data: At A Glance
1.3 The Unstructured Data Market
1.4 Advent Of 3rd Platform Technology
1.5 Digitization Of Financial Products And Services
1.6 Data Process Magnitude
1.7 Towards The Zettabytes Market
1.8 Data Analytics As The Battleground For Competition
Big Data In Finance: The Challenges
1.9 Financial Big Data Management: Reference Data
1.10 Big Data, Changing Business Financial Models
1.11 Big Data In Finance: Its Functional Levels
1.12 Technology Advancement Vis-à-vis Expanding Consumer Expectation
1.13 Behavioral And Tendency Data Thru Predictive Analytics
1.14 Customer Feedback Thru Sentiment Analysis
1.15 Mass Customization Data Remodeling
1.16 Big Data For Big Revenue
1.17 Big Data For Predictive Financial Crimes
Big Data In Finance: An Analysis
1.18 Understanding The Relevance Of Big Data In The Financial Service Market
1.19 Differentiating Big Data Analytics From Financial Econometrics
1.20 Could Financial Big Data Analytics Prevent Economic Recession?
1.21 Transforming Big Data Analytics For Financial Gains
1.22 Customer-focused Big Data Financial Initiatives: Banking Sector Experience
1.23 Big Data For Effective Financial Consolidation: The Jabil Success Story
1.24 Business Intelligence Averting Financial Service Problem: The Klout’s Experience
1.25 Big Data And Analytics In Financial Services: The Case Of Becker Underwood
1.26 Big Data Security/Privacy Issues In Financial Services: The Google Lawsuit
Big Data In Finance: The Competitive Market Landscapes
4.1 Big Data Financial Management Solutions
4.1.1 Ibm
4.1.2 Hp
4.1.3 Teradata
4.1.4 Dell
4.1.5 Oracle
4.1.6 Sap
4.1.7 Emc
4.1.8 Cisco Systems
4.1.9 Microsoft
4.1.10 Fusion-io
4.1.11 Splunk
4.1.12 Netapp
4.1.13 Hitachi
4.1.14 Opera Solutions
4.1.15 Csc
4.1.16 Mu Sigma
4.1.17 Booz Allen Hamilton
4.1.18 Amazon
4.1.19 Intel
4.1.20 Capgemini
4.1.21 Marklogic
4.1.22 Cloudera
4.1.23 Actian
4.1.24 Sgi
4.1.25 Gooddata
4.1.26 1010data
4.1.27 10gen
4.1.28 Google
4.1.29 Alteryx
4.1.30 Guavus
4.1.31 Vmware
4.1.32 Paraccel
4.1.33 Tibco Software
4.1.34 Informatica
4.1.35 Attivio
4.1.36 Qliktech
Big Data In Finance: Prospects And Opportunities
4.2 The Future Of Big Data In Financial Services
4.3 Multichannel Marketing In Big Data
4.4 Emerging Markets In Big Data In Finance
4.4.1 Brazil
4.4.2 China
4.4.3 India
4.4.4 Europe
4.4.5 North America
Conclusions
List of Figures
Figure 1 Big Data Market Forecast 2013-2018
Figure 2 Big Data Paradigm
Figure 3 Migration Process of Platform Technology
Figure 4 Data Universe Zettabytes Generation 2013-2020
Figure 5 Financial Big Data Management Paradigm
Figure 6 Big Data Approaches for Financial Services
Figure 7 Big Data Functional Levels
Figure 8 Big Data for Predictive Financial Crimes
Figure 9 Big Data in Finance Market 2013-2018
Figure 10 Big Data as Competitive Differentiator for Financial Services
Figure 11 Big Data Revenue Share by Vendor Solutions 2013
Figure 12 Hadoop and NoSQL Vendor Revenue Share 2011-2013
Figure 13 Big Data in Finance Market 2014-2020
Figure 14 Big Data Market in Brazil 2013-2018
Figure 15 Market for Big Data in China 2013-2018
Figure 16 Big Data Market in India 2013-2018
Figure 17 Big Data Market in Europe 2013-2018
Figure 18 Big Data Market in North American 2013-2018
List of Tables
Table 1 IBM Big Data Financial Management Solutions
Table 2 HP Big Data Financial Management Solutions
Table 3 Teradata Big Data Financial Management Solutions
Table 4 Dell Big Data Financial Management Solutions
Table 5 Oracle Big Data Financial Management Solutions
Table 6 SAP Big Data Financial Management Solutions
Table 7 EMC Big Data Financial Management Solutions
Table 8 Cisco Big Data Financial Management Solutions
Table 9 Microsoft Big Data Financial Management Solutions
Table 10 Fusion-IO Big Data Financial Management Solutions
Table 11 Splunk Big Data Financial Management Solutions
Table 12 NetApp Big Data Financial Management Solutions
Table 13 Hitachi Big Data Financial Management Solutions
Table 14 Opera Solutions Big Data Financial Management Solutions
Table 15 CSC Big Data Financial Management Solutions
Table 16 Mu Sigma Big Data Platforms
Table 17 MuSigma Big Data Financial Management Solutions
Table 18 Booz Allen Hamilton Big Data Financial Management Solutions
Table 19 Amazon Big Data Financial Management Solutions
Table 20 Intel Big Data Financial Management Solutions
Table 21 Capgemini Big Data Financial Management Solutions
Table 22 MarkLogic Big Data Financial Management Solutions
Table 23 Cloudera Big Data Financial Management Solutions
Table 24 Actian Big Data Financial Management Solutions
Table 25 SGI Big Data Financial Management Solutions
Table 26 GoodData Big Data Financial Management Solutions
Table 27 1010data Big Data Financial Management Solutions
Table 28 10gen Big Data Financial Management Solutions
Table 29 Google Big Data Financial Management Solutions
Table 30 Alteryx Big Data Financial Management Solutions
Table 31 Guavus Big Data Financial Management Solutions
Table 32 VMware Big Data Financial Management Solutions
Table 33 ParAccel Data Financial Management Solutions
Table 34 Tibco Software Big Data Financial Management Solutions
Table 35 Informatica Big Data Financial Management Solutions
Table 36 Attiivio Big Data Financial Management Solutions
Table 37 Qlick Tech Big Data Financial Management Solutions

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