Big Data and Business Intelligence: Convergence of Business Intelligence and Big Data Analytics

Big Data and Business Intelligence: Convergence of Business Intelligence and Big Data Analytics

Category : ICT & Media
September  2014  Pages : 49



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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?

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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:

 

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 & Big Data Analytics     37


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