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Overview:

The landscape of data gathering and analysis is rapidly changing as the amount of data generated in conjunction with data sources and means of extracting data continues to accelerate. One of the key issues is how to most efficiently and effectively realize value from this seemingly boundless sea of unstructured (Big) data.

Big Data is much more than its technical definition implies: A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tool. Big Data is already changing the way business decisions are made since big data exceeds the capacity and capabilities of conventional storage, reporting and analytics systems, it demands new problem-solving approaches.

Business Intelligence (BI) represents a set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI has existed in various forms for a long time but arguably is lacking when it comes to unstructured data.

This research evaluates the relationship between BI and Big Data including benefits, issues, and challenges in terms of planning and integration. The report also answers important questions such as:
Is BI being replaced by Big Data approaches?
How is Big Data clouding Business Intelligence?
What are the important steps in BI-Big Data integration?
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.

Report Benefits:

Understand why we can't ignore Big Data, and what new insights Big Data can provide that BI can't today
look at limitations and risks involved in handling large unstructured data for better business decision making
Learn why there is a need to marry Big Data and BI solutions and the associated benefits and challenges
Learn the questions every organization should consider and find answers to them in order to overcome the roadblocks in implementing new data technologies that make the Big Data ecosystem

Target Audience:

Business intelligence companies
Big Data and analytics companies
Data as a Service (DaaS) companies
Cloud-based service providers of all types
Data processing and management companies
Application Programmer Interface (API) companies
Public investment organizations including investment banks
Private investment including hedge funds and private equity

Table Of Contents

Big Data and Business Intelligence: Convergence of Business Intelligence and Big Data Analytics
1.0 EXECUTIVE SUMMARY 6
1.1 OVERVIEW 6
1.2 KEY BENEFITS 6
1.3 QUESTIONS ANSWERED BY REPORT 6
1.4 TARGET AUDIENCE 7
2.0 INTRODUCTION TO BIG DATA 8
2.1 DATA EXPLOSION 8
2.2 DATA FROM INSIDE AND OUTSIDE 8
2.3 WHAT IS BIG DATA? 8
2.4 THE V'S OF BIG DATA 9
2.5 A SAMPLING OF BIG DATA FACTS 10
2.6 WHY ONE CAN'T IGNORE BIG DATA 11
2.7 BIG DATA MARKET 13
2.8 MARKET CONDITIONS THAT ARE DRIVING BIG DATA ADOPTION 13
2.9 TECHNOLOGY TRENDS INFLUENCING BIG DATA ADOPTION 15
3.0 BIG DATA: OPPORTUNITIES AND CHALLENGES 16
3.1 OPPORTUNITIES AND REWARDS 16
3.2 BUSINESS CASES AND EXAMPLES 18
3.3 BUSINESS IDEAS TO CAPITALIZE ON HUMONGOUS DATA 19
3.4 BIG DATA'S BIG PROBLEMS 21
3.5 BIG DATA REGULATION 24
3.6 BIG DATA TRENDS 2014 25
3.7 BIG DATA TALENT REQUIREMENT 26
3.8 THE NEW DATA SCIENTIST 27
3.9 TIPS FOR WINNING OVER BIG DATA TALENT SHORTAGE 28
4.0 PUTTING BIG DATA TO WORK 30
4.1 BIG DATA ANALYTICS PIPELINE 30
4.2 BIG DATA ECOSYSTEM 32
4.3 GETTING STARTED WITH A BIG DATA PROJECT 33
4.4 BEST PRACTICES IN BIG DATA SUCCESS 34
5.0 BUSINESS INTELLIGENCE (BI) 36
5.1 HOW BIG DATA IS CLOUDING BUSINESS INTELLIGENCE 36
5.2 HOW IS BI GETTING IMPACTED? 36
5.3 PREDICTIONS FOR BUSINESS INTELLIGENCE 37
5.4 KEY BUSINESS INTELLIGENCE SOLUTIONS PROVIDERS 39
6.0 BI AND BIG DATA INTEGRATION 41
6.1 ADVANTAGES OF BI-BIG DATA INTEGRATION 41
6.2 CHALLENGES IN BI-BIG DATA INTEGRATION 41
6.3 APPROACHES FOR INTEGRATING BIG DATA PLATFORM WITH BI INFRASTRUCTURE 42
6.4 THREE STEPS TO BI-BIG DATA FRAMEWORK 44
7.0 CONCLUSIONS AND RECOMMENDATIONS 46

List of Figures

Figure 1: How the Internet is Collecting Data 9
Figure 2: The V's of Big Data 10
Figure 3: Big Data Market Forecast, 2011-2017 ( in $US Billion) 13
Figure 4: Market Conditions Driving Adoption of Big Data 14
Figure 5: Strategies for Making Data Profitable 20
Figure 6: Big Data's Darker Side 21
Figure 7: Key Regulatory Areas for Big Data Growth 24
Figure 8: Big Data Talent Requirement 27
Figure 9: Demand Supply Gap for Data Scientists 27
Figure 10: Who is the New Data Scientist? 28
Figure 11: Winning Over the Talent Shortage 29
Figure 12: Big Data Analytics Pipeline 30
Figure 13: Big Data Ecosystem 32
Figure 14: Getting Started with Big Data 33
Figure 15: Best Practices in Big Data Success 34
Figure 16: Challenges in Integration of BI and Big Data Systems 42
Figure 17: Approaches to Integrating BI Infrastructure to Big Data 43
Figure 18: BI Big Data Framework 44
Figure 19: Three Steps to Bi Big Data Framework 45
Figure 20: Global Big Data Revenue 2014 - 2019 47
Figure 21: Big Data Revenue by Region 48

List of Tables

Table 1: Key Differences between BI and Big Data Analytics 37

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