News agencies are venturing into analytics. A news agency also cleans the Internet for news, indexes it, and then allows users to ask questions of that data. It also allows you to extract facts and do trend analysis. Firms are taking data collected by machines or people, and using that data to generate news. Thanks to big data technology, it's becoming more practical to incorporate the field intelligence into the predictive models that executives increasingly rely on to make good, fast decisions.  Read more here: 

http://www.datanami.com/2015/05/29/how-big-data-is-changing-how-businesses-use-the-news/ana

Transport for London (TfL) data, collected through ticketing systems, censored vehicles, traffic signals, survey groups etc. is provided through open API's for 3rd party app developers. This data is then used to produce maps showing when and where people are traveling, and allowing analysis at the level of individual journeys by using Big Data. The key priority to initiate this data was to provide travel information which gives the routes customers use and to send travel updates to them. Thus Bernard Marr from Forbes in his article showed how big data played a big part in re-energizing London's transport network. Read more about this article at: http://www.content-loop.com/big-data-internet-things-improve-public-transport-london/

The present day drug traffickers are very proficient in using modern technology, manipulating it for obtaining new marketing opportunities for their drugs. These criminals are using digital networking to a large extent for distributional purposes via secure methods, provided by modern day technology, which go undetected.  Often investigators fail to identify the disturbance they must be looking for, whose data lies well concealed under suitable digital protection. Advanced crime analytics incorporates big data and advanced analytics along with crime science study to detect anomalies, often their sources and the associated people. Advanced crime analytics is also essential in controlling cross-border drug trafficking by not only protecting witness details but also speeding up the entire process. To know more follow the article by Craig Richardson (chief executive of the Wynyard Group) at:

http://www.canberratimes.com.au/comment/crime-analytics-software-proves-powerful-weapon-in-war-on-drugs-20150414-1mkp6r.html

Ski resorts now-a-days are using certain frequency recognition systems, whose data is used to upgrade skier's experience, in multiple ways. For example, the system has led to the replacement of the old paper system, which unnecessarily consumed a lot of excess time. Stats collected from individual skiers are used by programs, to get an idea about the number of lift rides taken, number of days spent in the slopes, height scaled etc. Skiers can also get rewards based on their performance as directed by the data, thus leading to a gaming experience, which is attracting more and more skiers to the slopes. Big data also ensures that the ski resorts can efficiently transfer proper information to the consumers using data management. Big data not only helps the resorts in predicting weather, with the right stats, but also helps in strategizing suitable marketing techniques. Read more at:

http://channels.theinnovationenterprise.com/articles/even-ski-resorts-are-benefiting-from-the-big-data-explosion

In business, the 'black swan' is a form of disruption, which is usually least expected by a firm. Again, 'ugly duckling' refers to the day to day disruptions, which are always expected, and the firm remains prepared for it, hence, the coining of the term.  Companies don't pay attention to these disruptions with the expectation that they will remain small and never grow up to be as big and disruptive as the 'black swan'. Majority of the companies have risk management departments who focus on regulations and the traditional risks, completely ignoring the extreme ('black swan') cases. The 'black swan' has the capability of even surpassing the traditional data analytics procedures. Hence, comes into the picture, big data and advanced analytics, which gathers newer insights even about the most uncommon and worst case scenarios that may happen in the near future. Armed with information, much beforehand, the companies can make themselves immune, even to the worst of the situations. Predictive analytics thus plays a major role in recognizing 'black swan' events, often from the data of past 'black swan' episodes. Read more at:

http://www.teradatamagazine.com/v15n02/Features/Ugly-Duckling-or-Black-Swan/

Paul Mandeville, Chief Product Officer, QuickPivot, in his article at cmswire.com has given 3 C’s of modern retail marketing. They are:

• Context
• Content
• Customer Experience

Combining these three with big data, can do wonders for retailers. All they need to do is to clean up their organization’s database and find a common thread between these three C’s of retail marketing.

The retailers need to realize the power of these C’s for capturing their customers’ hearts. To know more, please visit the following link:

http://cmswire.com/digital-marketing/the-3-cs-of-modern-retail-marketing/

Sustainable development is the development of the society, keeping in mind the needs of the present as well as future generations. It is this development that is the ultimate goal of every nation. For this, UN will launch a new set of Sustainable Development Goals (SDGs) in 2016. These goals will give rise to various important questions such as:

• How will we achieve these goals?
• Who will finance these goals?
• Which countries will need most resources and of what type etc.

For answer to these questions, the decision makers refer to the data provided by various international institutions like UN, IMF, World Bank, etc. But while doing this, they face a big hurdle i.e. Incompatibility of Data in terms of definitions, methodologies and sources.

Now, with the data revolution going on and big data and analytics taking the centre stage, we now have an opportunity to collect and produce high quality data that will provide the right information on the right things at the right time. The need is – its correct implementation.

To know more, please read the following article by Gail Hurley and Jos Verbeek at brookings.edu:

http://www.brookings.edu/blogs/future-development/posts/2015/05/26-financing-for-development-hurley%E2%80%8F

Big data combines unstructured data with structured data to produce a report which provides a complete view of the organization. This report helps in decision making. Big data provide real time or near real time data. According to Mark Shacklett (President of Transworld Data), big data analytics results have impacted corporate revenues, expenditure and customer satisfaction. Web-based analytics on e-tail users helps in generating more sales by assessing customer preferences. Network diagnostics toolset analyze network and machine-generated data and produce predictive reports. This tool set gives a real-time view of network traffic. Sensors placed on railways and tram tracks, help crew members to proactively repair or replace equipment and railway tracks before they fail. Read more at: http://www.techrepublic.com/article/4-ways-real-time-analytics-lead-to-competitive-advantages/

We have come across the word "Smart city" frequently in recent times and we will see it more frequently in coming years. What exactly is a Smart City and how is it created?  The idea of smart City is to embed the technologically advanced devices which are making the Internet of things a reality into our surroundings. Once the technology is hard wired with the infrastructure, we can interact with these devices using our smart phones or computers and also devices can interact among themselves. For example, a car will be guided to an empty parking space without any human intervention.

In order to realize these smart cities, software and hardware applications for the internet of things are being encouraged. Some of the applications planned for smart cities are intelligent street lighting systems to conserve energy, mapping energy use around the city to understand demand and mapping how people maximize use of bicycle and foot paths. People are also raising caution over this new technology as it will hurt privacy.Read more at:http://www.forbes.com/sites/bernardmarr/2015/05/19/how-big-data-and-the-internet-of-things-create-smarter-cities/2/

In the age of Big Data, the data tables are going to grow enormously in size. Millions of rows of data will be obtained. Computational techniques available now will no longer be efficient in analyzing and interpreting the data. Enter this June; MIT researchers will present a new algorithm that will reduce the size of the data tables by leaving out bunch of rows. The data/rows remaining will be the representative ones of the total data. This is called condensed data/matrix of original data/matrix. To know more about how to handle Big Data, follow:https://newsoffice.mit.edu/2015/algorithm-shrinks-big-data-0520

Big Data- Knowing what it is and how it can be used is not sufficient. To be successful, the need is to develop a strategy on how to optimize its use for your own advantage.

David A. Kelly, in his article in Q1 2015 issue of TeraData Magazine has compiled the views of three eminent researchers in this field - Vince Dell’Anno, David Stodder and Dan Vesset. They have suggested the following 3 best practices for developing big data strategies:

• View Big Data As A Valued Corporate Asset
• Foster A Culture Of Embracing Data
• Collect Diverse Data, Then Follow Up With Action

To understand them in detail, please visit the following link on forbes.com:

http://www.forbes.com/sites/teradata/2015/05/20/three-best-practices-for-executing-on-big-data-strategy/

• Apache Hadoop- as software vendors are targeting the distributed storage and processing architecture, need for Hadoop is increasing 
• Apache Spark-The rapid rise of the in-memory stack is being extended as a faster and simpler alternative to MapReduce-style analytics.
• NoSQL-Databases like MongoDB and Couchbase are taking over jobs previously handled by monolithic SQL databases like Oracle and IBMDB2.
• Machine Learning and Data Mining- Big data pros who can harness machine learning technology to build and train predictive analytic apps are in high demand Statistical and Quantitative Analysis-Add in expertise with a statistical tool like R, SAS, Matlab, SPSS, or Stata is of high demand in today’s world. 
• SQL- SQL is still in demand for the next-generation of Hadoop-scale data warehouses. 
• Data Visualization-It has become most important in the job market. 
• Progamming Languages-Knowing programming languages like Java, C, Python, or Scala could give you the edge over other candidates. Read more at: 

http://www.datanami.com/2015/01/07/9-must-skills-land-top-big-data-jobs-2015/

Natural disasters unlike other man-made disasters are the most terrifying events in the world since they cannot be controlled. However, by using the power of big data, it is possible to help in disaster management. For this purpose big data can be used through crowdsourcing which can be achieved by the increasing use of social- networking in the present days. In case of earthquakes, instead of using dedicated sensors which are highly expensive one can use the almost similar sensors in smartphones through Wi-Fi-hotspot and GPS to collect data to create an overall picture. For this, infrastructure needs to be set up so that information can be uploaded from the affected areas so that the affected people can be tracked down. Moreover the maps created through crowdsourced collaboration helps to optimize the recovery process. Read more at :http://channels.theinnovationenterprise.com/articles/big-data-in-a-crisis

The Retail industry is very competitive. Retailers need to apply analytics to analyze consumer behavior and retain them. Predictive Analytics help retailers to predict the response of customers regarding new offer, discount or product. But barrier of culture and stage fright, stop them to apply big data analytics.

Leslie Dinham (Teredata) in her article "two ways retailers are overcoming barriers to analytics adoption," talks about solutions to these barriers or adoption blockers. They are:

Barrier 1# Culture is the culprit: Employees get rigid due to working in the same culture, performing same job or duties. They don’t want to change their decision making process and roles. It becomes difficult to apply data analytics in this culture. The solution to this problem could be informing employee about the benefits of using data analytics and provide necessary training.

Barrier 2# Stage Fright: Many times, retailers won’t get success while applying analytics in their organization because they won’t able to choose the right team, tool or technology, won’t able to integrate new analytical capabilities into operations or the culture of the organization is not innovative. Paying attention while applying analytics in these things can help organizations to successfully apply analytics.

To know more about these barriers and solution to them, read an article at: http://www.forbes.com/sites/teradata/2015/05/13/two-ways-retailers-are-overcoming-barriers-to-analytics-adoption/

“Data is the new oil”

This comparison of big data with oil has always been there, ever since big data came into limelight. It is considered that like oil, the more you extract from big data, the more you benefit.

Now look at this new statement:

“Data is the new soil”

This statement reflects the growth in the field of big data. From being used only for extracting information, it is now being used to explore new avenues. Big Data is now being used as a raw material from which new ideas can be generated and further processed into new products and services. Many examples of this were given at Sapphire Now, SAP’s annual user conference, where innovators demonstrated various fields in which they have started using big data sets to create unique products. Some of them are:

• Handle the short and medium term challenges that climate change creates
• Help “local spaces” understand what mobile customers want
• Provide shoppers with a contextual in-store experience
• Help companies create solutions and discover things like energy and profit leaks, make predictable promotions based on clustered buyer preferences

Thus, big data is now providing a new range of solutions to make our lives easier as well as better. To know more, read the following article by Virginia Backaitis, Senior Partner at Brilliant Leap, at cmswire.com:

http://www.cmswire.com/cms/big-data/is-data-the-new-soil-sapphirenow-029124.php

The retail industry is B2C industry. In B2C industry, forecasting and planning future demand and supply is a very important function to improve operation's efficiency. But, consumer behavior is very unpredictable. To analyze this unpredictable behavior, retail stores need to analyze big data. In Consumer Goods Analytics Summit in Chicago, suggestions on applying Big Data Analytics in Retail Industry were discussed. Let’s have a look on some of them:

·        By using big data analytics try to find out actual problem and their solution.

·        Apply analytics in every possible way from making sales report to multi-structured data to understand and improve customer service.

·        Always Interpret big data.

·        Recruit persons who understand the value of data analytics. 

To know more about Big Data Analytics in Retail, read the article link “Are retailers organized for Analytics” by Gib Basset, (Consumer Goods and Retail Industry Principal with Oracle Corp) at: http://www.retailwire.com/news-article/18266/are-retailers-organized-for-analytics

The Global Food System comprises of a number of stakeholders as well as data - consumers, producers, economics, trade agreements, financial transactions, demand data, supply data, forecasting models, climatology, large and small-scale farms, politics, distribution systems etc. How do all of these correlate in a useful manner and show results? This is not possible with traditional scientific methodologies and technologies as there is a robust volume of complex data available. Rather, there’s a need for Big Data Analytics that will help in following areas:

• Measurement of poverty and hunger levels
• Improve aspects of how we feed and eat
• Food policy actions, etc.

Therefore, we need to invest in larger data warehouses which will provide the backbone for big data analysis of local, regional, national and ultimately, the global food system.

To know more, please read the following article by Hari Pulapaka, Executive Chef and Co-Owner, Cress Restaurant, at The Hufffington Post:

http://www.huffingtonpost.com/hari-pulapaka-phd-cec/big-data-analytics-the-gl_b_7216378.html?ir=India&adsSiteOverride=in

Lots of experiments are going on in using predictive analytics in health care. But, only few succeed. The need is to learn from what has been done and work further.

Jennifer Bresnick, in her article on HealthITAnalytics.com, has summarized some of the ways healthcare organizations have already found success by turning big data into a strategic asset that can help providers react quickly and effectively to the ongoing challenges of quality care delivery. They are:

• Hospital quality and patient safety in the ICU
• Precision medicine, personalized care, and genomics
• Population health management, risk stratification and prevention
• Reducing preventable hospital readmissions

To know more, please visit the following link:

http://healthitanalytics.com/news/four-use-cases-for-healthcare-predictive-analytics-big-data

The hype around the word “big data” is ever increasing. It promises to bring a big revolution in marketing. But, in all this hype, myths also arise, which need to be cleared.

Joerg Niessing,INSEAD Affiliate Professor of Marketing, and James Walker, Partner Demand Analytics, Strategy&, in their article at knowledge.insead.edu, talk about eight commonly heard myths on big data. Some of them are:

• It’s big
• The more granular the data, the better
• Big Data is good data
• Big Data is a magic 8-ball

These myths need to be discarded before putting into use “the real Big Data”. To know more, please visit the following link:

http://knowledge.insead.edu/blog/insead-blog/the-eight-most-common-big-data-myths-3878

Managing large volumes of heterogeneous data and using it effectively has always been a problem question in the legal domain. The solution to this big question has now been obtained with the advent of big data. Legal Informatics, a field which has emerged from big data, ties together work in the representation of legal knowledge with the performance gains derived through distributed processing.

Many questions arise in the minds of lawyers such as:

• How does the Judge rule on certain types of cases can be studied by date and time?
• Does the judge dismiss cases for a consistent pattern of reasoning?
• How do holidays affect decisions?
• Do they sentence harder at different times of the day?

These questions can now be easily answered with the help of Legal Informatics.

But, like all things have two sides, use of big data analytics in legal domain also has its repercussions like routine tasks will now be easily undertaken by analytics, judges will come under increased pressure, etc.

To know more, read the following article by Robert Plant, Associate Professor at the School of Business Administration, University of Miami, at The Wall Street Journal.

http://blogs.wsj.com/experts/2015/04/24/what-big-data-means-for-the-legal-system/