The quality of big data is more important than the quantity of big data. Three types of data a company should be stressing on:
1. Social data- Marketers are now focused on estimating the number of followers of the company website, and its products now that there are ample opportunities to share the knowledge of their products in social platforms like, Facebook, LinkedIn and Pinterest.
2. On-line IDs- Data has taken marketing to a whole new level. Data can be collected and used to personalize messages and send offers through mediums most preferred by consumers. This is often done by connecting off-line data with on-line identities.
3. hard-to-find data- Enterprises produce large quantities of data everyday as a by-product of their day-to-day business transactions. Many companies, realizing the benefits of monetizing these data assets are making it available to other firms and the fact that the data has already been mined and sourced, adds value to the whole procedure.
Read more at:

http://www.business2community.com/big-data/3-big-data-types-will-increase-roi-01257959

Big data can also be applied in the field of humanities specially history. With advancement in text analytics, historians can now go through a huge number of historical documents and discover patterns and trends from the past. These then can be used as evidence to check if theories are correct. Research students analyzed a huge number of novels using big data analytics which allowed them to leverage insight on what the semantics implied about the society. Given the nature of historical documents, it is difficult to digitalize and apply text analytics to those. Thus data analytics is being considered by historians now. Big data when applied to history will help bring information scattered over the world thus helping historians to gain insight and predict the future. Read more at: https://channels.theinnovationenterprise.com/articles/data-looking-at-the-past

When it comes to the healthcare industry and its ongoing revolution, Big Data and Internet of Things have become an integral part of it. As more and more health data is now being digitally stored, the seer amount collected on patients is growing at exponential rates. Big data, already used for the mapping of the human genome, is now showing its value in the analysis of large complex data and thereafter in predictive analytics to find people most at risk of a certain illness. Predictive analytics isn't the only place where Big Data and IoT have helped in the advancement of healthcare services. There is now the possibility of analysis of data generated by IoT devices to help calculate medication tailored for each patient and also calculate their results in advance. Read more at: http://www.predictiveanalyticsworld.com/patimes/the-iot-and-big-data-are-teaming-up-to-push-healthcare-further-than-ever-0627156/

By making use of excess of data available and information and communication technologies (ICT), smart cities can now address the various economic, spatial, social and ecological problems. Although data analytics forms the core of business strategies, yet very little is known about new business models within smart cities and urban living. Recent advances in Data Analytics have shown promise than city data analysis and its use can generate value for municipalities, companies and individuals. From relatively simpler analytics techniques to more sophisticated one, the available tools can adopted to obtain the desired results. For example, when the enormous volume of the data poses serious challenge, clustering technique can make the dataset manageable. Read more:- https://icrunchdatanews.com/smart-data-analytics-smart-cities/

A metadata fabric that provides efficient data analysis and data driven decisions, is of great value to an enterprise which utilises IOT generated data. The metadata fabric presents data and analytics, together in a business consumable format and interface. The major types of metadata are maps, derivations and complex events. An enterprise’s first concern is the ability to create and store the metadata in a business friendly interface, which will also enable its exploration, its usage in data analysis and will accept updates with changing business trends. The metadata fabric needs to adapt itself to situations, where data values can change over time or appear in a fragmented manner or even encrypted, at times. The information, regarding the analyses performed by previous personnel needs to be a part of the metadata layer, available for successive employees. When users and systems are not able to search through, or update metadata, the metadata fabric is probably broken, and so it is, when data exists in disconnected islands. Read more at:

http://www.cio.com/article/2939114/data-analytics/the-grand-unified-theory-of-metadata-governance.html

Supply chains help in harmonizing and maintaining the balance between the production and delivery of those goods to the customers. The Internet of Things (IoT) helps in optimizing the supply chains for better profits in a faster and more efficient manner. The IoT provides companies with improved market opportunities. With the help of IoT the usage of the products by the customer can be tracked and accordingly the supply chains can be used to deliver the supplies on time rather than when requested. Change in the market can be observed very quickly and thus they can respond much faster and gain benefits.
Read more at: http://www.industryweek.com/supply-chain/internet-things-iot-opportunities-smarter-supply-chains

With IoT becoming the norm rather than the exception, there is an overwhelming amount of customer data at the disposal of companies. But with huge data comes huge challenges. The challenge of combining, cleaning and shaping data before the analysis. Cari Jaquet, vice president of marketing at Paxata, in a special guest feature in Inside Bigdata talks about why data preparation solutions are important to make data more consumable. According to a recent Gartner report, it has been advised to use simple data preparation tools to first, save analysts time and second simplify the task of data preparation from diverse sources. For a deeper insight into data preparation follow the link  http://insidebigdata.com/2015/06/19/data-preparation-the-key-to-unravelling-the-big-data-opportunity/

Large amounts of real-time data is being daily generated by internet usage. Nowadays, devices are being interconnected and smart products are also connected with the internet. In order to use this data efficiently, organizations need to re-structure their supply-chain. The motivation is not just to use historical data in the traditional manner; but to combine data from multiple product interactions generated by both consumers and suppliers, connected via cloud portals. Supervised machine learning can search for and capitalize on the patterns and relations that they derive in the data and help in supply chain based decisions. Once implemented, they can be continuously evaluated and improved based on performance. The end aim is to accurately predict the attributes of future demand. Read more at: https://hbr.org/2015/06/inventory-management-in-the-age-of-big-data

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/

With fast advancement of consumer-driven technologies, new alternate ways to measure workplace risks are emerging. Technology improvements like growth of the Internet of Things (IoT) help us in assessing the working environment in real time.  The increase use of wireless communications for performance specification in the field of safety, health and industrial hygiene allow them to safely monitor multiple workers in real time and be alerted when exposure levels become significant or excessive. Another recent trend is the emergence of Big Data and the use of data mining as an analysis and management tool that will forever change the safety and health profession. With the increase in samples, the accuracy grows and statistical analysis takes decisions regarding the limit of exposures. This helps identify outliers (individuals, processes and practices that result in greater exposure levels) as well as gives confidence to the management about the accuracy of assessment of the workers. Read more at: https://ohsonline.com/Articles/2015/05/01/Technology-Megatrends.aspx?Page=1

Big data is completely transforming the way we look at business. Analytics is being used to foster growth in every industry. The entertainment industry uses predictive analysis to forecast the box office collections, gauging the success of the movie before they even start shooting it. Production houses can use big data to plan strategic release dates and to retain consumers by analysing patterns derived from the data. By collecting data from the social media sites, they can formulate plans based on consumer sentiments as to what kind of music, casting, genre, are in trend thereby increasing the ROI and giving improved entertainment to the end user. Read more at: http://analyticsweek.com/how-big-data-is-changing-the-entertainment-industry/

Predictive analytics helps police officers in America and the UK solve crimes. To reduce crime rates, there is an increase in budgeting, planning, installing cost which aren't considerable to the proportionate reduction. Thus more money spending would help improve public spending. New equipment will also assist public safety. Likewise, gradually predictive analysis will also be used to solve terrorism. PredPol is one such systems to target robberies, crime etc. Obviously, like others it too has backdrops as it involves mass data collection. But the advantage that has occurred and might occur because of it are much important to focus on than its backdrops. Read more about this article at: https://channels.theinnovationenterprise.com/articles/how-predictive-analytics-is-revolutionising-public-safety

Predictive analysis helps in analyzing the choice and behavior of the viewers. This in turn helps in finding the target audience as well as optimizes revenue. Combination of system dynamics and agent based modeling can be used to predict the profits from the shows or movies. Proper techniques along with real time analysis can assess the content precisely and accurately.
Social media analytics or sentiment analytics allows the industry to understand the perception of the audience and the critical reviews that they give.
“Bag-of-words opinion analysis” approach can help in evaluating the tweets from various social media sites. Cost efficiencies can also be achieved with proper efficient use of data analytics.
Read more at: http://sites.tcs.com/blogs/agile-business/analytics-entertainment-industry

Location analysis is a very important part of Big Data classification in automobile segment. Big data analysis is used in every stage of production and marketing of automobile industry.
Product Design – Data is collected from customers who would help them to make newer improved models.
Supply Chain – With the help of Big data analysis manufacturers can now analyze different suppliers based on various aspects and reach a decision.
Marketing – Helps in identification of the right kind of customers.
Aftermarket – Helps In improving logistic and sourcing processes.
Financing – It enhances the understanding regarding the market of each model.
Read more at: http://www.smartdatacollective.com/anandsmartdata/325598/big-data-automobiles-use-cases-today-and-opportunities-future

With the advent of Internet of Things (IoT), businesses can now avail additional ways of communicating with their customers. Sensors can be embedded in household appliances allowing consumers to control those which simplify household chores. Doctors nowadays can use apps to link patient statistics and provide personalized advice. Thus healthcare market makes ample use of IoT to provide better services. Security concern should be kept in mind as organizations are now deploying IoT in their enterprise. Organizations should also encourage application developers to maintain a certain standard to prevent poor codes from being deployed. Cloud technology, big data and analytics, mobile computing and social networking along with IoT makes a complete package. Read more at: https://channels.theinnovationenterprise.com/articles/deploying-iot-in-the-enterprise

Through predictive algorithms, text analytics in Big Data can discover a wide range of happening things about a business. On the positive side, text analytics can be fed a lot of unstructured text. Customer experience management, brand monitoring, compliance and brand management can be done with the help of text analytics. This was of great use during the US election as it found out reasons behind interested voters.  Though on the negative side, text analytics tool get confused for words which has a dual meaning in the same culture or for different cultures. Hence, it should be made such that it can stand up successful for different sentiments too which many are trying to establish now. Read more about this article by Elliot Pannaman (International Events Director) at: https://channels.theinnovationenterprise.com/articles/maximizing-business-performance-with-text-analytics

The Great Recession was the general economic decline observed in world markets around the end of the first decade of the 21st century. The exact scale and timing of the recession is debated and varied from country to country. The most probable cause behind this recession is the use of bad data. Generally, retailers lose their revenue because of out-of-stock issues which is due to the use of incorrect and outdated data. Hence bad data is destructive. It results in losses because of dwindling customer satisfaction and incurs additional expenses in order to correct the faulty data. Big data can affect both small and large enterprises but companies can be immunized against bad data by being active and using the correct tools.

Read more at: http://www.business2community.com/big-data/bad-data-a-21st-century-business-epidemic-01252370

In modern world, analytics plays a significant role and therefore various organizations develop cohesive analytics for customer satisfaction and thereby leading to enhancement of sales. According to some retailers, customer experience drives revenue while analytics focuses on an organization's commitment in terms of value to the customers.

 Audi representative Aaron Cohen (the company's general manager of strategy and analytics) explained the process of optimizing customer experience and hence the importance of data blending. Companies are shifting their focus to freemium services, open source platforms for valuable insights. In this context, Monobina Saha (senior data engineer at Sears) highlighted the significance of R-programming among data scientists.

Read more at: http://www.cmswire.com/analytics/how-are-you-capitalizing-on-customer-analytics/

Just like other areas of economy, marketing, too, has been impacted significantly by the growth of data analytics. Although the importance of analytics has been recognized in marketing, yet it is observed that in most cases the tools are not being used efficiently. For this, aligning data with the goals of the company needs to be done. Secondly, the smooth transition from big data to useful data and determination of the relevant metrics and adoption of those metrics are required. And last but not the least, hiring of the right people with analytical and creative minds to properly use the data is imperative. Read more: http://www.forbes.com/sites/johnrampton/2015/06/21/how-can-cmos-make-sense-of-big-data/

Industries are growing techno-savvy and understanding that being able to gather, identify, and comprehend patterns in data is critical for their long-term success. We are finally entering an age of data-powered farming that work on open data sets of weather and yields, IoT, drones and smart agricultural machinery. Such digital tools are becoming as important as traditional tools in the agricultural sector. IoT sensors are also helping companies predict and manage risk for automobile policies in real-time. Governments and industry are cooperating to share data on natural catastrophes in the hope to predict them. Drones are even being used to inspect and manage energy sites like turbines and rigs. Governments too will start using Big Data for evidence-based policy making. Read more at: http://yourstory.com/2015/06/big-data-unleashing-value-for-startups/