Scaling databases for enterprise require to have to integrate wildly disparate data sources, satisfy stakeholders with competing expectations, and find the structure hidden in unstructured data.One has to carefully consider tradeoffs between data integrity and constant uptime, between.You may have a legacy system that stores data in tab-delimited files, unstructured text files coming from handwritten notes, and one or more conventional database management system and data from all of these sources needs to be read by and integrated into a single system.Read full article at : https://www.oreilly.com/ideas/insights-on-scaling-and-integrating-databases

The word Big Data is connected with 4 Vs' Velocity, Volume, Variety, Veracity and each V plays a significant part in the Big Data world. The event that combines all these components, paints a clarified picture of what big data actually means. Big Data management methods adopted by many companies involve various stages: 1. Collecting Data: It includes accumulation of data from various information sources. 2. Store: It includes storing data in the appropriate database framework and server 3. Information Organization: It involves masterminding information on the premise of Organized, unstructured and semi-unstructured data. Read more at : http://www.bigdatanews.com/profiles/blogs/how-to-collect-big-data-big-data-a-new-digital-trend

The emergence of massive unstructured data sources like Facebook and Twitter has created a need to develop distributed processing systems for Big Data Analytics. Hadoop (A Java based programming framework) has become the first choice of developers and industry experts mainly because its: Highly scalable, flexible, and cheap. An application is broken down into various small parts which runs on thousands of nodes to achieve fast computing speed and reduce overall operation time. Hadoop architecture continues to operate even if a node fails. Its incredible design allows you to process large volumes of data and extract computationally difficult features of users/customers.

Read more at : http://www.datasciencecentral.com/forum/topics/how-to-use-hadoop-for-data-science

The Internet of Things (IoT) generates semi-structured or unstructured data in real time. Organizations take advantage of cloud because big data can be best managed in the cloud. By utilizing fog computing, organizations can decrease time to action; reduce costs, infrastructure and bandwidth; and can get greater access to data. The advantages of the decentralized method of fog computing and IoT analytics cover both the organization and the end user. One of the benefits of centralization is to focus and understand the data location and the accessibility. The decentralized method is associated with flexibility and agility. This tends to describe the data management trends and applications. Read more at the article written by Jelani Harper (blogger) : http://data-informed.com/the-internet-of-things-and-the-necessity-of-fog-computing/

The development of mobile, IoT, and the cloud has increased the need of analytics to solve challenges in the customer, product, operations, and marketing domains. The established companies need to restructure their business and technology to increase their sales. Organizations need to involve cross-functional teams to establish data governance. Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. Now in the era of Analytics 3.0, when tools make decisions and measure the impact. For more read the article written by chandramohan Kannusamy (Technical Architect) : http://data-informed.com/analytics-3-0-and-data-driven-transformation/

The value of data is no longer unknown to the business world.  Both structured and unstructured data are important because they are unprocessed raw materials which go into analysis. Many business leaders and IT professionals prefer structured data, while it is currently being observed that unstructured data also has a lot to offer. Unstructured data coming from various social network sites help business leaders to gauge customer sentiments and grievances. It also helps to reduce costs and adapt to a changing market situations. Sometimes there can be challenges handling unstructured data such as collection, organization, integration and analysis. text analytics, auto taxonomy generation, auto tagging and other techniques are vital when it comes extracting value from unstructured data. To know more read: http://www.cio.com/article/2941015/big-data/solving-the-unstructured-data-challenge.html

There is an upward trend in the use of big data in the present scenario of the business world. The structured and the unstructured data comprise of the big data. The big data gets analyzed from the market view point and looks for the right time to fetch the right customers. The big data not only helps in sustainable productivity but a development for the employees on an individual levels as well .The use of big data have benefited many sectors in the business and will help more with the combined effect of the modern technology.

Read more at: 

http://www.business2community.com/big-data/big-data-a-big-impact-on-productivity-01274278

There is no doubt about the revolution that big data has brought to the way business is done. But, most of the talk has been around the structured data. It has been increasingly becoming clear that the potential of big data can be truly understood if we take up the challenge of harvesting unstructured data. Jonathan Buckley, senior vice president of marketing at Qubole, in an article in Smartdatacollective emphasizes that if businesses want to remain relevant and profitable then it’s the right time to turn their attention to text analytics. The most important advantage that text analytics have is that it provides with a much larger sample of customer sentiment and extract data which is otherwise not quantifiable. But all of this boils down to having the right technology. For more on this follow the link http://www.smartdatacollective.com/jonathanbuckley/329383/text-analytics-next-frontier-big-data

Human intelligence is found in every organization. It is difficult to derive meaningful information from unstructured data and hence it's a big mistake not to use it in decision making.  Content Intelligence is the combination of technology and information science which allows machines to model, interpret, analyze and visualize human intelligence within an organization. It is used to generate new revenue streams, gain operational efficiencies, increase customer satisfaction, rise in productivity and avoiding costly networks. The rising pressure on enterprises increases costs and riskiness when content intelligence isn't available. It makes unstructured information self-describing and hence allows content-based information to be described in a similar way as structured data.For further details on content intelligence, please follow the link :  http://www.dataversity.net/what-is-content-intelligence/