The relationship between structured and unstructured data in real time can be analyzed by turning the structured and unstructured data (wide data) into fast data. Origin of a problem can be found by wide data. Quicker decision and analysis can be made by fast data. With the boom of Internet of Things (IoT), new challenges relating to the structured and unstructured data may arise. By turning wide data into fast data we will be able to retrieve massive amounts of data from various sources and then use them to identify and potentially assess their behavior in real-time.
Unstructured data can also be used to meet real-time data demands. When wide data is turned to fast data it opens doors to various opportunities.
Read more at: http://www.tibco.com/blog/2015/07/17/big-datas-companion-wide-data/

Big Data means a huge amount of data and is characterized by 3Vs: Volume, Velocity, and Variety. Big Data has become very clumsy. So, the trend is shifting towards Fast Data, which means processing of massive data in real time to gain instant awareness and detect signals of interest on the spot. Another trend is Actionable Data, which synthesizes the predictive analytics and what-if analysis which enables you to take actions with feedback. Another new trend is Relevant Data which is critical to identify pertinence in the data set, and which leads to understanding of unrelated events and sequence. The last trend is Smart Data which is meaning-based computing and cognitive analytics that make solutions intelligent and self-improving. In a nutshell, Fast Data, Actionable Data, Relevant Data, and Smart Data (FARS) are replacing Big Data. Read more at: http://www.socialmediatoday.com/technology-data/2015-04-04/big-data-really-dead

In today's world data is not only growing at a very fast rate but it is also being accessed and processed at unprecedented rate. So now, businesses have to focus on the velocity of Big Data, acting on it with precision for real-time results. This is where Fast Data comes in. Fast Data can provide real-time insights from events as and when they take place and help to make a decision at that place and point in time. Fast Data is complementary to Big Data for managing large quantities of real-time data. There are many examples of why Fast Data is becoming increasingly important. In a telecom industry, a Fast Data approach can help telecoms manage resources more effectively. In the financial services industry, Fast Data is using event correlation to contextualize available financial data. In retail industry, customer service centers are using Fast Data for click-stream analysis and customer experience management. Healthcare is another area where Fast Data holds huge potential. Read more at:

http://www.informationweek.in/informationweek/perspective/296992/speeding-business-transformation-fast