Big data generated by drones is useful in every sector including monitoring data of animal cruelty on farms and surveillance data from military drones. The drones' usage needs a revolution in big data cloud services. However, flying a drone and taking pictures is the first step in data collection process. Since software to reason directly from video feeds is still in a research phase, drone data handling needs to be improved. The use of a cloud-based in-memory computing platform can enhance analytics, processes, and predictive capabilities. Amazon recently proposed to increase sales and revenue by providing the delivery of food using drones. By gathering data on a large scale, service providers will be able to process unique levels of details and turn it into usable information. To know more, go through Abhishek Sharma (author of InfoQ)'s article: http://www.infoq.com/news/2014/09/drone-data-big-data-analytics

Big data is a key infrastructure in the Internet of Things (IoT), but it's far from the only piece of the fabric. In the coming global order, every element of the natural world, and even every physical person can conceivably be networked. Everything will be capable of being instrumented. If you think that the world of driverless cars, robots carrying out maintenance in hazardous locations like oilrigs, or advertising that reads and responds to individuals' unique facial expressions sound like science fiction. As these trends come to fruition, each of us will evolve into a walking, talking, living beneficiary of the Internet of Things. These are all developments happening today and they're prompting a new exciting phase in analytics that needs to be addressed now. Those that embrace data will be more likely to be surfing on top of the wave of creative destruction, instead of having it crash down on top of them.

Read more at: http://blogs.computerworld.com/business-intelligenceanalytics/23447/internet-things-what-it-and-what-does-it-mean-analytics

The launch of big data analytics has brought confidentiality concerns as a rising subject for the customers. Obtaining data is important in order to improve its ability to connect their business to the target consumer by analyzing customer behavior. Casting uncertainties from the consumers on how entrepreneurs use their personal information while collecting data endanger the trust that every business enterprise should build. The consumers are aware of the value of their private data. To a reasonable extent the consumers are willing to share. Privacy is more important to the consumers and they expect marketers to give it owing admiration. Businesses that do not respect the privacy of their customers will be likely to mislay their faith. some strategies to be taken to re-establish the trust of customers between business and the customers. Many customers are prepared to share their personal data if they are guaranteed of their privacy. Read more at: 

http://www.socialmediatoday.com/node/216026

Big-data analytics impacts any organization economically, but often data scientists hope for benefits.The reality of where and how data analytics can improve performance varies across industries. Customer-facing activities- the greatest opportunities lie in telecommunications. Here, companies benefit by focusing on analytics models which optimize pricing of services, maximize marketing spending by predicting on where product promotions will be most effective, and identify ways for withholding customers. Internal applications- In industries, like transportation services, models focus on process efficiencies-optimizing routes. Hybrid applications- Some industries need both. Retailers use data to influence next-product-to-buy decisions and to choose the best location for new stores or to catch flows of products through supply chains. Companies operate along two horizons: capturing quick wins to build momentum while keeping sight of longer-term. Open data- swelling reservoirs of external data. Models are often improved combining these data with the existing ones for better business outcomes.. Read more at: 

http://www.mckinsey.com/insights/business_technology/views_from_the_front_lines_of_the_data_analytics_revolution

There are significant opportunities to make use of big data techniques. Unlike technology and consumer retail sectors in which advanced analytics has been implemented, it can also be used in other industries like insurance, health care, banking and public sector. In insurance, data can be aggregated from public sources and specialist data providers, allowing companies to better target customers and frame policies accordingly. Banks are increasingly using big data to generate a much deeper view of their customers, combining the information collected from all of customer's interactions with bank with selective third-party data like paying patterns for mobile phone bills, tracking trends on social media platforms such as Twitter. Read more about this aspect in Dominic Barton (global managing director at McKinsey & Co.)'s article link:http://blogs.wsj.com/experts/2014/03/28/sectors-where-big-data-could-make-an-impact/?KEYWORDS=analytics 

Sampling error can cause problems if they are not taken care of. Errors in judgment about sample size can be fixed easily and sample sizes must be considered seriously if big data is being used for predictive analysis. A leader trying to use big data in predictive analysis should always consult the data scientist. The way to understand whether enough data has been collected or not for the purpose of prediction involves understanding the tolerance of the risk associated to accept the assumptions drawn from the sample size characteristics. There are two types of risk: the risk that you're going to take some action when you shouldn't and the risk that you are not going to take some action when you should. Also enough information should be available about the sample variation and precision of measurement to know whether enough data has been collected to make prediction. To know more about importance of sample size in predictive analytics, go to John Weathington (President and CEO of Excellent Management Systems, Inc.)'s link: http://www.techrepublic.com/blog/big-data-analytics/why-samples-sizes-are-key-to-predictive-data-analytics/ 

Unlike other retailers, Nordstrom (a fashion speciality retailer), wanted to learn more about its customers like how many came through the doors, how many were repeat visitors. The company started testing new technology that allowed it to track customers' movements by following the Wi-Fi signals from their smart phones. Nordstrom's experiment is part of a movement by retailers to gather data about in-store shoppers' behavior and moods, using video surveillance and signals from their cell phones and apps to get information as varied as their gender, how many minutes they spend in the candy aisle and how long they look at merchandise before buying it. If a consumer looks for Wi-Fi network, a store that offers Wi-Fi can pinpoint where that particular shopper can go and get Wi-Fi connection within a 10-foot radius. Stores can also recognize returning shoppers as mobile devices send unique identification codes when they search for networks. This means stores can now tell how repeat customers behave and the average time between visits. Read more at-http://www.nytimes.com/2013/07/15/business/attention-shopper-stores-are-tracking-your-cell.html?pagewanted=all&_r=0/

With the advent of healthcare industry, the flow of data has increased by leaps and bounds. Now with the presence of analytics, huge number of unstructured data can be easily analysed to find out patterns and behaviours which in turn helps the companies associated with healthcare to take more sound and logical decisions.

To know more kindly visit:-

http://www.healthcareitnews.com/news/healthcare-data-goes-big-great

The term big data refers to large amount of customer information gathered from social media, which helps companies to improve sales and services by analyzing those data. But unfortunately small businesses have limitations while analyzing big data. Also the huge volume of data may be confusing for small organization and some time they don't know where to begin. Social platform gathers information which can be important for organisations. Hence tools provided by Twitter, Facebook, LinkedIn, are a good start as they offer low start-up investment as per Evan Greenberg, CEO of marketing and communications firm Allscope Media. To read more about how big data can help businesses to think outside the box, follow Nicole Fallon's article in this link: http://www.businessnewsdaily.com/6190-smb-big-data-tips.html

Big data analytics is a strong process to enrich any business process. Ford automobile company is applying big data analytics to improve their business. In doing so, they polish the metrics from the company's best processes across myriad manufacturing efforts and through detailed outputs from in-use automobiles-- all to improve and help transform their business. Big data is allowing deeper insights in improving processes, quality control, and customer satisfaction. To know more on this topic, go through the article by Dana Gardner, president and principal analyst at Interarbor Solutions.

http://www.zdnet.com/ford-scours-for-more-big-data-to-bolster-quality-improve-manufacturing-streamline-processes-7000010451/