Here are a few tips on how to bridge that gap between big data and the real world. New product development is a field full of quantifiable unknowns. While working on a new concept, the decisions are based on evidence that proves that their idea outmatches the other alternatives. Identifying connections between the data on social media and online communities and the comparison of this with their competitors’ development policies then become their groundwork for smarter solutions. Visualization of data quickly converts the data is understandable forms. This speeds up the decision process. It is important to note that more data doesn’t always mean smarter data. The motive should be to capture relevant Read more at: http://www.innovationexcellence.com/blog/2015/06/29/actionable-ways-big-data-analytics-can-actually-improve-innovation/

Big Data is paving the way to the emergence of Smart Data. Huge, differentiated and big volumes of Big Data practically needs smart data for its everyday working because they facilitate -
• Unstructured and structured data aggregation and analytics
• Simplified and accelerated data modeling
• Access and data governance
Big Data is inexorably transforming into smart data. It is the preferred technology used in the diverse application of Big Data including the Internet of Things, Cognitive Computing, Semantic Graph Databases, Data Lakes and Artificial Intelligence.
The nature of Smart Data represents an insurgence in the logic applied to data driven processes. Big Data is important in Data Management as it has the ability to implement action from real-time analytical insight and consolidate all of one’s data in the process. Applications such as Internet of Things automate processes that would otherwise take too long. In context of Smart Data’s ability to increase the utility of Data Lakes is its ability to help clarify the sort of role-based access that is a pillar of proper Data Governance. Smart Data Modeling is preferred in analytics because there is a degree of flexibility and agility in the modeling required for Smart Data that exceeds non-Semantic Data. Along with its advantages for analytics, application development, data integration and Big Data Governance, Smart Data’s reconfiguration of transactional data will establish the fact that Big Data is surely evolving into Smart Data.

Read more at: http://www.dataversity.net/the-evolution-of-big-data-to-smart-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