The healthcare sector is now exposed to data breaches as it has adopted the online mechanism, for maintain health records or say for remote patient monitoring. Data breaches are not only expensive in terms of costs incurred by the healthcare provider but also in terms of patients' lives. Data analytics helps in interpreting unstructured data, which may help to prevent not only data breaches but also (say, medical prescription) frauds.  Big data analytics allows organizations to amalgamate and analyze their data irrespective of the source, type, size or format and finally understand the patterns needed to address fraudulent activities. Again, by updating, pharmacies, doctors, and hospitals on data insights, concerning substance history information of patients, it is possible to take better prescribing decisions and identify potential prescription drug abuse. Read more at: http://venturebeat.com/2015/03/31/big-data-analytics-can-prevent-health-care-fraud-heres-how/

Sigmaway consultants have worked with clients in benefits solutions workspace providing analytics on healthcare plans. For more details visit http://www.sigmaway.us/

Smartphones are fantastic examples of devices whose success largely depends on the performance it delivers. Big data researchers are working towards developing the network, computing and cloud architecture required to sustain their projects. The sheer volume of data generated by us doubles every two years and there is an urgency to make storage devices manageable and affordable. SSA (Solid State Array) at the hardware level and SDS (Software-Defined Storage] at the software level are becoming quickly affordable. With the evolution of in-device memory, a variety of data will now come into light that may revolutionize the applications of Big Data in the mobile industry. In-memory databases will allow real-time analytics and awareness of the ‘live’ transaction data – rather than delayed ‘fact-based’ analysis of old data. Read more at: http://www.forbes.com/sites/howardbaldwin/2015/06/22/flash-ways-to-make-big-data-run-faster/

The film Moneyball based on a 2003 book, talks about the Oakland Athletics baseball team whose focus is on the team's analytical, evidence-based approach to improve the baseball team despite its poor revenue system. This film made the plot for professional sport associations to spend big on analytics. Analyzing data points captured by video cameras around the stadium uncover patterns that coaches and players might miss. Data can also be collected by wearable devices to help prevent injury by measuring its likelihood. So, companies need to follow get up and move up to the major leagues of Big Data. But predictive analytics can decrease efficiency as supporting analytics platform records, analyzes and visualizes trends. Read more about this article at: http://www.tibco.com/blog/2015/05/26/how-big-data-analytics-are-transforming-sports/

Businesses have made use of data for operations management, for decades now. Data being used for operations management involve continuous flows of information and mining of data. Such data are handled and untangled using advanced and unique storage, management, analysis and visualization techniques. Big data analytics is usually dependent on extensive storage capacity and quick processing power. Insightful data can be gathered from various sources like web, social media etc. Big data is now being used to predict customer purchase decisions. Big data when linked with production applications and timely operational processes enables continuous improvements. Organizations are now realizing that data can bring value through continuous improvement in their operations. Thus, data needs to be collected, filtered and analyzed. Read more at: http://www.business2community.com/big-data/crunching-big-data-operations-management-01255094

Big data can be best understood using graph theory. Several open-source graph databases have come up and are used by several organizations. These databases are becoming popular as graphs are the best way for storing and querying huge amount of data. Graphs are being used by several domains of the industry as it provides competitive advantage. Social media uses graphs to provide accurate and personal recommendations to users. It is being used by a major online shopping company to compute fast and provide localized door-to-door delivery of goods. Graph databases store data safely and make querying it fast and easy. According to Dr. Marsten, creating and analyzing graphs will lead us to answers. Read more at: https://channels.theinnovationenterprise.com/articles/7132-the-key-to-understanding-big-data-lies-with-the-graph

Big data and big data analytics has been a trend setter in recent years. Unlike the traditional cause-effect analysis, big data analytics generate predictions based on huge volumes of data, using statistical tools and advanced computing techniques. An interesting case study on the use of big-data analytics was the prediction of a flu pandemic in the United States by simply analyzing Google search data. The spread of the disease was detected before any medical organization or national agency, simply on the basis of people frequently searching Google for the symptoms and remedy of flu. Big data analytics enables us to generate reliable analyses, even in the absence of clear links or causes. To know more read:http://www.forbes.com/sites/huawei/2015/02/24/how-do-big-data-analytics-enhance-network-security/

Data analytics with its growing importance is now used to plan mergers and acquisitions which require a well thought game plan. Throughout the buying process, data analytics can be used to see how market will respond to a deal being made. While merging with another company, data gets doubled and so it is important for employees to analyze all sets of data thus allowing business users to make better decisions. All through the M&A process employees must be trained to possess analytical process, needed to survive in this data-driven economy. Organizations must be aware of how effective and intelligent data management and analytics can help drive Mergers and Acquisitions win. Thus Big Data can offer success to M&A. Read more at: https://channels.theinnovationenterprise.com/articles/7467-the-big-data-game-plan-in-mergers-and-acquisitions

A specific kind of data may be important for a few seconds or sometimes even a few years. Changing times catch up with all kinds of data. So what to do when data reaches its golden age? Archive! But it isn't enough to dump old data in a traditional data warehouse. We must think not only of data's immediate analytical value, but also at how to retain the value of data as it ages. This requires a new approach to archiving. We must ensure regulatory compliance and follow policies of data governance. We must provide access to data while preserving immutability of it or its metadata. Data should query enabled when needed, to generate new insights. Archives must be cost-efficient, enabling you take advantage of advances in data compression. Read more at : http://www.forbes.com/sites/teradata/2015/05/22/archived-but-accessible-retirement-planning-for-your-big-data/

When it comes to Data Analytics, failure is a very important part of the innovation process. A part of the data scientist’s job is trial-and-error and assumptions to vet data for new insights. Pushing the analytical process to uncover new uses of data and new ways of applying analytics necessarily involves risk and failure. Leading websites embrace this by testing hundreds or thousands of both minor and large scale changes to their site daily. Nothing is rolled out broadly on a major website today unless it has gone through rigorous testing. An organization based on rapid experimentation, exploration of new ideas and educated in doing analytics right is one destined to succeed. Read more at: http://www.forbes.com/sites/teradata/2015/05/27/why-being-wrong-is-the-best-way-to-get-your-analytics-right/ 

With the rising importance of big data, organizations are now collecting and storing data, but many don't put it to good use. Retailers can leverage customer data to make personalized recommendations about offers and promotions thus providing a shopping experience customized to individuals. Analyzing customer data can help companies to identify customer preferences for products and the prices they are willing to pay. Customer data can be used to identify the most relevant users to ask for feedback, create new products or services and provide better customer services. By analyzing customer data, companies can identify patterns of behavior of customer data and thus formulate targeted marketing strategies, improve organizational effectiveness and reduce risk and fraud. Read more at: http://www.cio.in/feature/8-ways-to-make-the-most-out-of-your-customer-data

Analytics is being widely used in many areas and sports is no exclusion. Data analytics has successfully scripted victories for many teams, already. Data analytics can be applied to any sport be it tennis, baseball or cricket. But, sports analytics is a completely new concept in India. Sports analytics is the amalgamation of sports and information technology. Sports teams are now hiring data analysts who feed data in their algorithms, which then process the data fed and perform numerical calculation to figure out the strategy for the next match. The analyst then informs the coach and the team players about the prescriptive strategies. Read more at: http://www.cio.in/feature/big-data-is-here-to-have-a-good-innings

Getting around in big cities without facing traffic jams is almost impossible. After the businesses, it's the city planners and transportation experts now who are resorting to Big Data when making improvements to city transportation. Commuters have certain patterns they like to keep to while travelling. By analyzing these actions and the factors responsible for them, transportation experts understand why certain routes and modes of transportation are preferred over others. Gathering call data records help provide access to data about travelers, which data scientists can use to decode the transport pattern of travelers. Countries like Australia and Brazil have already implemented Big Data to upgrade public transportation to keep up with the current demand of metropolitan cities. Read more at: http://channels.theinnovationenterprise.com/articles/big-data-s-impact-on-public-transportation

Big data developments in 2015 show significant change in terms of data storing as well as data analyzing techniques. Measuring data agility is becoming more and more popular, as opposed to the conventional system of storage and management of data resources. The investment and scope of big data projects will be directly related to the impact and of an organization's response to changing trends among consumers, markets and competition. Another big trend organizations will focus on in 2015 is usage of data lakes. Using data lakes, Hadoop and BI systems and cloud computing, real time data analysis and decision making is expected to be fast. So the dynamic change in technology will bring revolution in terms of business decision making. Read more at: http://channels.theinnovationenterprise.com/articles/advancements-in-big-data-in-2015 

Mobile applications are an integral and inseparable part of modern data analytics. Mobile applications not only influences consumer`s decision to make purchases, but also allows companies to get insights into consumption pattern and market demand. Enterprises seek immediate-data from a wide range of sources, to fuel business processes by means of big data and analytics and this is provided by different types of mobile applications. It plays an important role, particularly in the field of global supply chain management and predictive analytics. On the other hand mobile applications are also being fuelled by data analytics, so that it cannot only collect the data but also analyze it and process it simultaneously to provide instantaneous results in real time. Read more about this article at: http://www.forbes.com/sites/benkerschberg/2014/12/17/how-big-data-business-intelligence-and-analytics-are-fueling-mobile-application-development/ 

Predictive marketing has taken hold of enterprises today. Marketers are now making use of predictive analytics and this has given birth to predictive marketing. Predictive marketing is the application of algorithms and machine learning tools to Big Data in order to help marketers direct their efforts in the most profitable direction. Using predictive analytics tools, marketers can gauge future sales and thus formulate appropriate marketing strategies to help boost sales. Predictive analytics can be used for segmenting customers, deciphering the pattern in their behavior and thus offering them the appropriate deals. Marketing and sales are the areas where predictive analytics is most used. Read more at: http://www.cio.com/article/2934274/why-marketers-are-betting-big-on-predictive-analytics.html

Big Data and analytics have a huge role to play in the future of healthcare. Data will play a major role in deciding how healthcare companies, practitioners and technologies interact with their patients. Big Data ensures efficiency and accuracy in predicting treatments. Using data, doctors can make more accurate predictions based on the specifics of the patient rather than using a general rule for treatment. Major companies are venturing into healthcare by formulating pre-defined algorithms; making it easier to cure complex medical conditions. With the advent of Big Data in healthcare, people will have the opportunity to keep a track of their health; doctors will have a better idea of somebody’s health thus allowing them to provide health advices on the go. Read more at: http://channels.theinnovationenterprise.com/articles/big-data-has-a-huge-part-to-play-in-the-future-of-healthcare

Data mining results can be used by organizations to prevent losses ahead of time. Organizations usually use indications like inventory levels or employee turnover, which occur later in the business cycle to gauge shrinkage cost but by that time the losses start to incur and there is nothing much that can be done. According to a report by PricewaterhouseCoopers, if companies gather and analyse data, they are able to get better insights into the business and use the early warning indicators to stop losses from happening by making informed, strategic decisions, thereby saving time, money and effort. Read more at: http://www.tibco.com/blog/2015/06/03/strategic-data-analytics-to-help-reduce-shrinkage/

Now big data will help diagnose climate change. Shalene Gupta from Fortune talks about five big data projects that are on verge of changing the way we look at the problem of climate change. From tracking deforestation in Amazon, graphing animal life in African Savannah, tracking illegal forest fires to telling people how much energy they consume compared to their neighbors, we have it all in these 5 big data projects-

• Google Earth Engine

• Madingley Model form Microsoft

• Data.gov’s climate

• Global Forest Watch

• Opower

For more interesting insights follow the link http://fortune.com/2015/02/14/big-data-climate-change/

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

Fascinating, the first word that comes to the mind. Drones and big data are bonding pretty well these days and can be a successful recipe for innovations. 

Jonathan Buckley, senior vice president of marketing at Qubole, gives us a glimpse of this promising combo but not without the challenges. 

Agriculture, disaster relief and anti-poaching are some area where the combination has worked wonders. Needless to say more such wonders are in pipeline. But, yes there is a ‘butt’ of challenges attached here. The data gathered by drones is immense, it will require real time big data analytics and ad-hoc analysis to improve. But, one can feel optimistic about the future.

Have a look at  http://smartdatacollective.com/jonathanbuckley/316611/growing-relationship-between-drones-and-big-data