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

Sentiment Analysis is a text mining technique used to analyze the sentiment (unstructured data representing opinions, emotions or attitudes) about a particular product or topic, employing machine learning technique. Organizations across the world are extracting insights from the social data to understand how public feels about the product at a particular point in time. Sentiments are first categorized into negative or positive and then analyzed using various software like Hadoop. It can also be performed on excel. To know more, read the article at: https://www.brandwatch.com/2015/01/understanding-sentiment-analysis/

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

Data management has become the epicenter of success model for any organization. Human beings are generating huge amount of data everyday but most of the data is in unstructured form. The need to effectively manage data and create new flexible systems for effective integration of data across all databases is the need of the moment. Junk records, duplicate records and outdated records have only added to the costs of the organization and success for any organization will be how effectively its employees are able to create and manage the data on regular basis. The duty of cleaning up the data is often imposed on the IT department but they are just the gatekeepers and it is the employee who needs to take care of his or her data, be it a clerk, manager or even the CEO. To know more:

http://it.toolbox.com/blogs/insidecrm/why-is-data-so-difficult-to-control-68067

CRM systems are used in organizations all over the world to collect and maintain data about customers and prospects in a structured form. However, what about the huge amount of data that is present in vague formats before it is structured in CRM systems. "Content Intelligence" is a natural language processing system that extracts meaningful data from E-mails, reports, customer interaction meeting minutes and assigns metadata to the content for ease of access by the CRM system users. This reduces a lot of workload for them as they do not have to read through vast amounts of customer interaction data. For more visit:

http://it.toolbox.com/blogs/insidecrm/harnessing-unstructured-data-for-crm-68008

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/

Big data, legacy data, operational data and streaming data - these are all impacting the ever-increasing volumes, velocity and variety of data. - Deepak Belur (In4Group). Data and analytics have become essential to formulate critical business decisions. The big data storage technology is capable of analyzing massive amounts of data. But, analyzing a big data rarely gives you a full picture. To analyze full picture, a company need to analyze “unstructured data” which is present in mainframe servers. This data can be analyzed by using performance analytics. Enterprises need to combine relational and non-relational data, open system and mainframe, to retrieve the combined information via a simple, single query or request. And they need agile data architecture. Timeliness, accuracy, usability and accessibility of data are critical factors in the successful decision making process. Read more at: http://www.itweb.co.za/index.php?option=com_content&view=article&id=143791

Nowadays, companies are focusing on the best possible answers than asking the right questions to get the minutest details. To get the best details, a set of connect questions has to be answered which requires using a connected set of data sources. This is called unified analytics. It will enable the access and analysis of data from multiple sources on a single interface. But we need to keep certain things in mind like integration of unstructured and structured data which is not an easy process. Also, we need to look beyond traditional sources within firewall and focus the data strategy on customers. Read more at: 

 http://www.analytics-magazine.org/web-first/1266-unified-analytics-a-new-mastery-over-the-data-wave

Over the past two years, one of the seminal issues regarding Big Data was storage, especially with respect to the exponential growth and size of unstructured data that did not fit into databases. Today, however, the competitive landscape is very different. Proper storage is merely a pre-condition to finding the real jewels in Big Data-turning data from massive streams into knowledge, and thereby actionable intelligence in real time as events unfold. The following five steps are imperative to master Big Data and drive business growth:

1. Infer, Infer, Infer- Inferences transform data into knowledge, which results in greater process transparency and improvements.

2. Empower a C-Level Data and Predictive Analytics Champion. - With big data analytics changing rapidly and straining information structures, corporations and governments need “executive horsepower” behind its data initiatives.

3. Assess And Modify Your Supply Chain In A Multidimensional Global Context. - Analysis of supply chain will ultimately include relationships with parties such as customers, manufacturer, etc. 

4. Give Your Data Time-Critical Situational Awareness. - Analytics help a business line identify potential points of improvement.

5.   Rely On a Core Platform That Creates Derivative Intelligence and Knowledge in Real Time -statistical inferences can turn data into actionable intelligence that supports reasoned decisions. Read more at: 

http://www.forbes.com/sites/benkerschberg/2014/01/03/five-steps-to-master-big-data-and-predictive-analytics-in-2014/

Companies take efforts to develop all-encompassing voice of the customer (VOC) programs. The success of these programs is dependent on the quality and variety of data gathered from customers. Increasingly, companies are gathering customer feedback from structured as well as unstructured data. For companies establishing a VOC program, unstructured data seems overwhelming. Because of this, companies may avoid analyzing it. Capturing and analyzing unstructured data can be daunting to VOC program managers. Automation is an important step in successfully optimizing the value of unstructured data in a VOC program. Unstructured data creates excellent insight into the underlying drivers of loyalty, and it's vital that organizations make use of their available information. The companies that listen in the right places, using unstructured data to destroy structured, solicited feedback, and taking action accordingly, will have a huge advantage over their competitors. Unlocking the capacity of unstructured data is manageable if taken one step at a time, and the results will be well worth the effort. Read more at: 

http://www.destinationcrm.com/Articles/Web-Exclusives/Viewpoints/Unlock-the-Potential-of-Unstructured-Data-for-a-Successful-VOC-Program-97918.aspx

Big data is a popular term used to describe the exponential growth of structured and unstructured data. Despite of such an understanding by business organizations, according to a survey, second annual analytic usage, trends, and future initiatives report, 75 percent of businesses have yet to reach big data production. However, just 12.6 percent of respondents said their company has completed several big data projects that are now in production. Drew Rockwell (Lavastorm CEO)said “It’s organizations that go the next level by removing complexities from the analytics process and empowering others in the organization, namely business analysts, who are going to be able to turn data insights into actionable business enhancements for long-term success.” According to another survey, one concern for executives who are experimenting with big data is a shortage of expertise in that field. With a lack of big data skills, organizations are reluctant to take the plunge since a clear ROI immediately available. Without being open to a little disruption, organizations will have a much more difficult time adapting to changing consumer mindsets. Businesses should conduct their own research and see what options best fit their needs. Read more at: 

http://www.pymnts.com/in-depth/2014/is-big-data-just-a-big-problem/#.U6KBPZS1Yn7