Capturing Big Data in Social and Detection Systems: Market Opportunities and Challenges 2013 - 2019
Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. In addition to the large volume, much of this data is unstructured, making it hard to manage. This so called “Big Data” is a challenge for industry verticals yet also an opportunity.
The enormous growth potential of Big Data caught the attention of various large players such as IBM and Oracle, to name only a couple, who have developed a key strategic focus in this area and various vertical-specific offerings.
By now many people across various industry verticals understand the basic issues or at least have heard that Big Data is a big deal. However, most of the focus to date has been on the importance of optimizing data management and analytics. There has been very little analysis about challenges and opportunities for capturing Big Data.
This report provides critical evaluation of the front-end of Big Data: capturing data from various sources. This report focuses specifically on social systems as well as obtaining data through sensing/detection with an emphasis on RFID. The report includes Big Data revenue forecasts, predictions about the future of Big Data and social/detection, and related recommendations.
- Social, search, and ecommerce systems are a key source of unstructured data
- Future Big Data systems will rely upon an automated feed from various commercial and private sources
- Big Data is a big opportunity for those companies that position themselves for future systems integration
- There is an important role for hybrid Big Data and API solutions that combine the best of both worlds for data
- Big Data companies
- RFID and NFC companies
- Social media and networks
- Presence detection companies
- Fortune 1000 corporations of all types
- Database and related infrastructure providers
- ICT infrastructure and service providers of all types
- Data aggregators, storage and management providers
- Telecommunications infrastructure and service providers
- Government including homeland security and law enforcement
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.