1. BIG DATA

2. The information and the ability to store, analyze, and predict based on that information that is delivering a competitive advantage

3. A decade ago, data storage scalability was one of the major technical issues data owners were facing. According to IBM, “every day we create 2.5 quintillion bytes of data—so much that 90% of the data in the world has been created in the last two years alone.” 11 Industry research firm Forrester estimates that the overall amount of corporate data is growing by 94% per year. With this kind of growth, every company needs a Big Data roadmap. At a minimum, companies need to have a strategy for capturing data, from machine log files generated by in-house computer systems to user interactions on web sites. Nevertheless, a new brand of efficient and scalable technology has been incorporated and data management and storage is no longer the problem it used to be. In addition, data is constantly being generated, not only by use of internet, but also by companies generating big amounts of information coming from sensors, computers and automated processes.

4. Characterization of Big_data: Volume, Velocity, Variety (V3)

6. Big data can be described by the following characteristics: Volume: The quantity of data that is generated is very important in this context. It is the size of the data which determines the value and potential of the data under consideration and whether it can actually be considered as Big Data or not. The name ‘Big Data’ itself contains a term which is related to size and hence the characteristic. Variety: The next aspect of Big Data is its variety. This means that the category to which Big Data belongs to is also a very essential fact that needs to be known by the data analysts. This helps the people, who are closely analyzing the data and are associated with it, to effectively use the data to their advantage and thus upholding the importance of the Big Data. Velocity: The term ‘velocity’ in the context refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development. Variability: This is a factor which can be a problem for those who analyse the data. This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively. Veracity: The quality of the data being captured can vary greatly. Accuracy of analysis depends on the veracity of the source data.

7. Big-Data in Gartner Hype-Cycle 2011

8. Why Big-Data? Key enablers for the growth of "Big Data" are: Increase of storage capacities Increase of processing power Availability of data Manage Data Better: data scientists analyze, collect and sift through various types of data. While it does take some technical know-how to define how the data is collected and stored, many of today's big data and business intelligence tools let users sit in the driver's seat and work with data without going through too many complicated technical steps. One example is real-time video processing. The 2012 Summer Olympic Games in London made heavy use of closed-circuit video, with 1,800 cameras monitoring Olympic Park and the athletes' village. Teams of analysts used applications to process data pertaining to those who were filmed and flag any individuals behaving suspiciously.

9. Benefit From Speed, Capacity and Scalability of Cloud Storage Organizations that want to utilize substantially large data sets should consider third-party cloud service providers, which can provide both the storage and the computing power necessary crunch data for a specific period. Advantage: lets companies analyze massive data sets without making a significant capital investment in hardware to host the data internally. End Users Can Visualize Data While the business intelligence software market is relatively mature, a big data initiative is going to require next-level data visualization tools, which present BI data in easy-to-read charts, graphs and slideshows. Due to the vast quantities of data being examined, these applications must be able to offer processing engines that let end users query and manipulate information quickly—even in real time in some cases. Applications will also need adaptors that can connect to external sources for additional data sets.

10. Type of available data

11. Data available from social networks and mobile devices

12. Data available from Internet of Things

13. Gains from Big-Data per sector

14. Predicted lack of talent for Big-Data related technologies