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Big Data in Financial Services Industry: Market Trends, Challenges, and Prospects 2014 - 2019

Big Data in Financial Services Industry: Market Trends, Challenges, and Prospects 2014 - 2019

Big Data and predictive analytics solutions are poised to optimize the value of data within the financial services sector. Banks, investment firms, insurance companies and other service providers are learning to leverage the value of data and gain competitive advantage, minimize costs, convert challenges to opportunities, and minimize risk in real-time.

Big Data technologies and related business intelligence solutions 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.

Big Data in Financial Services Industry: Market Trends, Challenges, and Prospects 2014 - 2019 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?

All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

Big Data companies
Telecom service providers
Financial services companies
Regulatory and policy makers
Data services and analytics companies
Cloud and telecom infrastructure providers

Report Benefits:

Big Data in financial services forecasting through 2019
Identify leading companies and solutions for financial sector
Understand the role and importance of Big Data in financial services
Recognize the future prospects for Big Data in financial services industry
Identify initial and ongoing implementation areas for Big Data and analytics

Companies in Report:

1010DATA
10GEN
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.


1.0 Executive Summary
2.0 Big Data In Financial Services
2.1 Financial Services Industry
2.2 Financial Services: An Industry In Transformation
2.3 Role Of Big Data In Financial Services
2.4 Big Data To Become Essential Component For Financial Service Sector
2.5 A Three-way Big Data Approach Towards Financial Services
2.5.1 Information Based Sorting
2.5.2 Information Based Brokering
2.5.3 Information Based Delivery
2.6 Steps For Big Data Functioning In Financial Services
2.6.1 Data Acquisition, Collection And Detection
2.6.2 Data Management And Integration
2.6.3 Data Analysis
2.7 Big Data As Competitive Differentiator For Financial Services
2.8 Financial Big Data Management: Reference Data
2.9 Future Of Big Data In Financial Sector
3.0 Big Data Initiatives Of Financial Services Providers
3.1 Current Stage Of The Big Data Implementation In Financial Services
3.1.1 Financial Service Provider Big Data Initiatives
3.2 Top Big Data Initiatives In Financial Services Sector
3.2.1 Provide Real-time Response To Consumer Queries
3.2.2 Assess Customer Behavioral And Tendency Data Using Predictive Analytics
3.2.3 Measure Customer Sentiments And Take Appropriate Action
3.2.4 Mass Customization Data Remodeling
3.2.5 Big Data For Big Revenue
3.2.6 Big Data For Predicting Fraud And Other Financial Crimes
4.0 Big Data In Financial Services: Global Markets 2014 - 2019
4.1 The Global Big Data Market
4.1.1 The Unstructured Data Market
4.1.2 The Third Platform Perspective
4.1.3 Data Process Magnitude
4.1.4 Towards The Zettabytes Market
4.1.5 Global Markets For Big Data 2014 - 2019
4.1.6 Data Analytics Is The Battleground For Competition
4.2 Learning From Big Data In Financial Services Sector In 2013
4.3 Global Market For Big Data In Financial Sector 2014 - 2019
4.4 Focus Areas For Financial Services Sector Investment 2014 - 2019
5.0 Companies And Solutions
6.1 Big Data Financial Management Solutions
6.2 Companies And Solutions
6.2.1 1010data
6.2.2 10gen
6.2.3 Actian
6.2.4 Alteryx
6.2.5 Amazon
6.2.6 Attivio
6.2.7 Booz Allen Hamilton
6.2.8 Capgemini
6.2.9 Cisco Systems
6.2.10 Cloudera
6.2.11 Csc
6.2.12 Dell
6.2.13 Emc
6.2.14 Fusion-io
6.2.15 Gooddata
6.2.16 Google
6.2.17 Guavus
6.2.18 Hp
6.2.19 Hitachi
6.2.20 Ibm
6.2.21 Informatica
6.2.22 Intel
6.2.23 Marklogic
6.2.24 Microsoft
6.2.25 Mu Sigma
6.2.26 Netapp
6.2.27 Opera Solutions
6.2.28 Oracle
6.2.29 Paraccel
6.2.30 Qliktech
6.2.31 Sap
6.2.32 Sgi
6.2.33 Splunk
6.2.34 Teradata
6.2.35 Tibco Software
6.2.36 Vmware
7.0 Conclusions And Recommendations
Figures
Figure 1: Big Data Approaches for Financial Services
Figure 2: Big Data Functional Levels
Figure 3: Big Data as Competitive Differentiator for Financial Services
Figure 4: Financial Big data Management Paradigm
Figure 5: Big Data for Predicting Financial Crimes
Figure 6: Big Data Paradigm
Figure 7: Migration Process of Platform Technology
Figure 8: Data Universe Zettabytes Generation 2013 - 2020
Figure 9: Global Big Data Market Forecast 2014 – 2019
Figure 10: Global Big Data Markets by H/W, S/W, and Services 2014 – 2019
Figure 11: Big Data in Financial Services by Components 2014 - 2019
Figure 12: Big Data Revenue Share by Vendor Solutions
Figure 13: Hadoop and NoSQL Vendor Revenue Share
Tables
Table 1: Global Big Data Market 2014 – 2019
Table 2: Global Big Data Markets by Components
Table 3: Global Markets for Big Data in Financial Sector
Table 4: 1010data Big Data Financial Management Solutions
Table 5: 10gen Big Data Financial Management Solutions
Table 6: Actian Big Data Financial Management Solutions
Table 7: Alteryx Big Data Financial Management Solutions
Table 8: Amazon Big Data Financial Management Solutions
Table 9: Attiivio Big Data Financial Management Solutions
Table: 10 Booz Allen Hamilton Big Data Financial Management Solutions
Table 11: Capgemini Big Data Financial Management Solutions
Table 12: Cisco Big Data Financial Management Solutions
Table 13: Cloudera Big Data Financial Management Solutions
Table 14: CSC Big Data Financial Management Solutions
Table 15: Dell Big Data Financial Management Solutions
Table 16: EMC Big Data Financial Management Solutions
Table 17: Fusion-IO Big Data Financial Management Solutions
Table 18: GoodData Big Data Financial Management Solutions
Table 19: Google Big Data Financial Management Solutions
Table 20: Guavus Big Data Financial Management Solutions
Table 21: HP Big Data Financial Management Solutions
Table 22: Hitachi Big Data Financial Management Solutions
Table 23: IBM Big Data Financial Management Solutions
Table 24: Informatica Big Data Financial Management Solutions
Table 25: Intel Big Data Financial Management Solutions
Table 26: MarkLogic Big Data Financial Management Solutions
Table 27: Microsoft Big Data Financial Management Solutions
Table 28: Mu Sigma Big Data Platforms
Table 29: MuSigma Big Data Financial Management Solutions
Table 30: NetApp Big Data Financial Management Solutions
Table 31: Opera Solutions Big Data Financial Management Solutions
Table 32: Oracle Big Data Financial Management Solutions
Table 33: ParAccel Big Data Financial Management Solutions
Table 34: Qlick Tech Big Data Financial Management Solutions
Table 35: SAP Big Data Financial Management Solutions
Table 36: SGI Big Data Financial Management Solutions
Table 37: Splunk Big Data Financial Management Solutions
Table 38: Teradata Big Data Financial Management Solutions
Table 39: Tibco Software Big Data Financial Management Solutions
Table 40: VMware Big Data Financial Management Solutions

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