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.
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
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
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.
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.