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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

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


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
1.1 Overview
1.2 Key Benefits
1.3 Questions Answered By Report
1.4 Target Audience
2.0 Introduction To Big Data
2.1 Data Explosion
2.2 Data From Inside And Outside
2.3 What Is Big Data?
2.4 The V’s Of Big Data
2.5 A Sampling Of Big Data Facts
2.6 Why One Can’t Ignore Big Data
2.7 Big Data Market
2.8 Market Conditions That Are Driving Big Data Adoption
2.9 Technology Trends Influencing Big Data Adoption
3.0 Big Data: Opportunities And Challenges
3.1 Opportunities And Rewards
3.2 Business Cases And Examples
3.3 Business Ideas To Capitalize On Humongous Data
3.4 Big Data’s Big Problems
3.5 Big Data Regulation
3.6 Big Data Trends 2014
3.7 Big Data Talent Requirement
3.8 The New Data Scientist
3.9 Tips For Winning Over Big Data Talent Shortage
4.0 Putting Big Data To Work
4.1 Big Data Analytics Pipeline
4.2 Big Data Ecosystem
4.3 Getting Started With A Big Data Project
4.4 Best Practices In Big Data Success
5.0 Business Intelligence (Bi)
5.1 How Big Data Is Clouding Business Intelligence
5.2 How Is Bi Getting Impacted?
5.3 Predictions For Business Intelligence
5.4 Key Business Intelligence Solutions Providers
6.0 Bi And Big Data Integration
6.1 Advantages Of Bi-big Data Integration
6.2 Challenges In Bi-big Data Integration
6.3 Approaches For Integrating Big Data Platform With Bi Infrastructure
6.4 Three Steps To Bi-big Data Framework
7.0 Conclusions And Recommendations
List of Figures
Figure 1: How the Internet is Collecting Data
Figure 2: The V’s of Big Data
Figure 3: Big Data Market Forecast, 2011-2017 ( in $US Billion)
Figure 4: Market Conditions Driving Adoption of Big Data
Figure 5: Strategies for Making Data Profitable
Figure 6: Big Data’s Darker Side
Figure 7: Key Regulatory Areas for Big Data Growth
Figure 8: Big Data Talent Requirement
Figure 9: Demand Supply Gap for Data Scientists
Figure 10: Who is the New Data Scientist?
Figure 11: Winning Over the Talent Shortage
Figure 12: Big Data Analytics Pipeline
Figure 13: Big Data Ecosystem
Figure 14: Getting Started with Big Data
Figure 15: Best Practices in Big Data Success
Figure 16: Challenges in Integration of BI and Big Data Systems
Figure 17: Approaches to Integrating BI Infrastructure to Big Data
Figure 18: BI Big Data Framework
Figure 19: Three Steps to Bi Big Data Framework
Figure 20: Global Big Data Revenue 2014 - 2019
Figure 21: Big Data Revenue by Region
List of Tables
Table 1: Key Differences between BI & Big Data Analytics

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