Healthcare is being revolutionized by the advent of big data. New gadgets and technologies are aiding this transition. A continuous stream of health related data promises us the concept of continuous healthcare. This data will enable us to study behavioral changes among patients and diagnose problems better and faster. Real time disease management is fast becoming a reality and it will be even possible to tend to patients in the early stages of their disorders and take steps accordingly. Real time monitoring of treatment efficacy will immensely help in post-operative care, drug therapy. It also holds the power to point out which patients merit additional intervention. Read more at:

http://www.dataversity.net/how-big-data-will-enable-continuous-healthcare/

The combination of new software technology, industrial data science and visualized information is acting as a game changer in the aviation industry. Analysts monitor flight data every second on the ground, for variations and immediately alert the pilot of any anomalies. Real-time data monitoring also helps in reducing fuel consumption apart from providing safety and security. Tim Leonard of southwest Airlines while talking to business insider said that the predictive analytics tools help prevent disasters they make pilots, mechanics, and employees more aware. It also helps provide frequent and precise data on current weather conditions leading to better scheduling and minimising delays. Read more at: http://www.businessinsider.in/Why-every-flight-you-take-is-obsessively-monitored/articleshow/47694902.cms

Data science techniques are becoming increasingly popular these days to improve business outcomes. Hadoop, already showcased the broader use of big data technologies and their impact on businesses. In this new age, the limits of machine learning are constantly being tested as innovators are trying new techniques that decrease human intervention as much as possible. Companies are ready to work with the data they have in Hadoop. Penetration of SQL on Hadoop has been a great help as they have created an environment that has made data accessible to downstream apps and learning algorithms. Machine intelligence is catching up in all spheres, data science is becoming a new trend with data scientists coming in demand. To know more, please follow:

http://www.dataversity.net/predictions-for-data-science-over-the-coming-years/

Corporates today provide many pro women services like day-care centres, six months-maternity leave, transportation services etc., in fact, sometimes companies hire same number of men and women at entry levels, but still the proportion of women workforce is on a decline. According to Economic Times, a recent report shows that women “invest” in their careers differently. After obtaining a job, the effort to take it further reduces. Post marriage, her career becomes home oriented as she has both work and home responsibilities. Apparently, women who work with same passion as men are tagged “non-family type”. They are not able to develop a rapport with their bosses as strong as men’s. So, retaining female employees depends entirely on herself, the family and the supervisor. Read more at: http://blogs.economictimes.indiatimes.com/et-commentary/why-indian-companies-are-losing-their-women-workforce/

In the face of global competition, the business world needs to adapt new technologies and forgo past financial traditions. Following Derek Klobucher (Moderator of SAP Business Trends)'s article, most software were designed in the 70s, and 80s so they are slowly losing relevance in the modern era as the technology landscape is vastly changing. The way forward is adapting the next generation business platform. SAP is working on solutions to integrate instant real time insight, offer updated user interface and user experience which saves on transaction times by 30 to 40 percent, and be non-disruptive. Month end closing is a traditional; however modern times dictate fast, accurate and secure data analytics. To know more http://www.news-sap.com/how-real-time-analytics-will-kill-a-financial-tradition-sapinsider-2015/

Text data is the best answer in understanding customer feedback. It is best for answering the why and what questions. Text Analytics is becoming mainstream as interest is pouring in. Sources include call center notes, email messages, social media etc. Following Fern Halper's TDWI article, 5 fundamentals include: 1. Text analytics involves analyzing unstructured data to extract relevant information.
2. Extracting various information in the form of keywords, entities, concepts and sentiments
3. Considering taxonomy, while dealing with specific vocabularies.
4. Integrating text data with traditional data, depends on the kind of data
5. Text data is not as accurate as statistical techniques
To know more http://tdwi.org/Articles/2015/06/23/5-Text-Analytics-Fundamentals.aspx?Page=2

Like other conservative industries, chocolate industry is also interested in applying smart marketing techniques for their business. Companies need to be more scientific in their marketing ways and by now it's proven that future growth of the chocolate industry also depends on data-driven consumer marketing. Vosges Haut-chocolat company used the method of predictive analytics solution to reach its buyers. The difficulty in this task was to figure out how price sensitive were the consumers. With newly discovered software's and AgilOne predictive marketing cloud, Vosges tried to target all its customers who had left shopping carts and also found all different reasons for their - not purchasing the chocolates. Predictive analytics made it possible for the company to provide heavy discounts to the people segmented into proper user-groups and increase their sales so that consumers who would never buy any chocolates ever could be differentiated from the rest.

Read more at: http://www.smartdatacollective.com/socialmktgfella/326221/even-chocolate-needs-smart-data

Just as sourcing ingredients is essential to the quality of a great dish, the quality of labels determines the accuracy of a supervised or semi-supervised machine learning (ML) solution. What is a label?
Data labeling involves taking unclassified data and augmenting each piece of that data with some sort of meaningful ‘tag’, ‘label’, or ‘class’ that is somehow informative or desirable to know. Assigning a label is a judgment task to be performed by an analyst depending upon the kind of variables he/she wants to work with and is an essential part of the ML process.
How to ask Questions?
All tasks involving labels involves, at some point, asking a question to a human being for collecting the data. The questions should be relevant, clear and precise in addition to being plain and understandable and one for which answering does not involve much effort for the individual.
How to debug tasks?
The collected data may sometimes show discrepancies due to:
• Data: Certain factors which can cause bias in the workers have to be eliminated.
• Workers: We have to detect the expected errors arising due to human involvements, say, an error arising due to spammers rather than people making genuine mistakes.
• Tasks: If the problem still persists then there might be a problem with our initial assumptions. Time to rethink from the start.
How to assess Work Quality?
Specific domain based algorithms exist for every point during the project that determine the ongoing quality of work.
Labels are essential and cutting on effort in this regards is most likely to lead you to erroneous results.

For more information visit:
http://blogs.technet.com/b/machinelearning/archive/2015/06/23/label-quality-for-machine-learning.aspx

As the amount of data being generated daily is increasing, sales teams are now using analytics to boost sales. Several factors should be kept in mind while doing this like choosing the right set of data. The sales team may be lead astray when wrong data sets are chosen leading to drawing wrong conclusions. Choosing the right metric and making correct assumption is also crucial. Data on compensation can also provide insights into how sales can affect individuals or a group. Using this data and applying predictive analytics, one can easily forecast future performance and accordingly plan sales process. Read more at:http://www.cmswire.com/digital-marketing/dont-let-data-blind-your-sales-team/

A company’s success depends largely on its staff. So, hiring the best talent and motivating them is a challenge being faced by the HR professionals. A data-driven HR department can make use of analytics to accomplish several tasks like identifying the factors that influence performance of employees, retention rates and using statistics to understand how salary is related to performance. When compared to other departments in an organization, the HR department lags behind when it comes to using analytics due to lack of analytical skills required to convert data into insights. With the advent of cloud technology analytics solution can be generated at a fester rate. Several cloud based companies are now providing in-built analytics solution in their software. Read more at: https://channels.theinnovationenterprise.com/articles/hr-analytics-in-2015

19000 students across 340 countries around the world have chosen GOOGLE as their top choice when asked where they wanted to work according to a study. The other companies where they also liked to work in the ascending order were Amazon, Facebook, Microsoft and Apple. This particular study was conducted by Piazza, a discussion platform used by several students and teachers around the world. Pizza has been doing a great work in the market for recruiters and is an attractive tool. Piazza has also stated that women and men have different opinions in choosing their workplace. Hence, such a software helps the companies know how students see the world right now.

Read more at : http://techcrunch.com/2015/06/29/where-computer-science-engineering-and-math-majors-want-to-work/?ncid=rss&cps=gravity_1730_-7001745546600563025#.mzgrdo:U5OR

Amalgamation of data, technology and marketing helps the manufacturing industry acquire new opportunities for earning profits. Data obtained from the customers are stored in various different systems like inventory, billing etc. And normally the manufacturers have to gather information from a common source thus having no competitive advantage in the market.
Industrial manufacturers are now gradually shifting from traditional marketing methods to data-driven ones so as to gain more prospects. Solutions such as Data-as-a-Service (DaaS) are influencing the Big Data ecosystem increasingly. It mines the appropriate data required from the Big Data sets. Patented Web Mining is also an effective and efficient way to find new prospects. Social media also actively helps in promoting the manufacturers.  Thus industrial manufacturers can increase their market share to a great extent by shifting to data-driven strategies.
Read more at: http://www.smartdatacollective.com/lbedgood/327457/how-manufacturers-can-use-big-data-acquire-new-customers

Many business have been extracting the benefits that come from applying big data as years go by. But there are still a few start-ups left who haven’t yet reaped its benefits. It’s crucial to know that now is the correct time to get into big data as it has considerably improved in the past few years. The reasons why big data should be employed now are:

• It keeps the data secure and all the sensitive data stay well protected in the system.

• It opens up brand new revenue sources and gives us a clear perception about the market which is very valuable.

• Big data provides an advantage of supplying better data which helps compete against others in the market when trying to innovate.

• It provides better visuals as well as it is very easy to set up big data in the business of any size.

• It helps the business expand and can efficiently cut down on maintenance costs.

• With big data employment, it’s easier to analyse the risk and create better improved products in the market.

• Lastly, big data improves company-customer relationships helping the business in every possible way.

Read more at: http://www.smartdatacollective.com/rick-delgado/323981/10-reasons-why-now-time-get-big-data

Banks, when adopting a mobile-first strategy, should look beyond mobile apps. To achieve a successful mobile-first strategy, banking sector should deploy data analytics. Although several tech giants are threatening traditional banks with their mobile enabled financial services, banks possess certain advantages. They are a trusted source for financial services and have maintained long-term relationships with millions of customers. Banks possess a wealth of customer data, transactional data to be specific, which can be leveraged to gain insights on customers. This will help banks to understand customer behavior and target their behavior. Read more at: https://channels.theinnovationenterprise.com/articles/7649-banks-are-pioneering-the-mobile-revolution-but-can-they-see-beyond-the-app

In order to achieve accuracy of understanding customer’s experience the company executives have to come up with varied surveys and several tricky questions. We can gain a more empathetic experience of customers experience with the appropriate amalgamation of observations, sensors, data and designs. Being aware of the users experience is very critical for the technology design.
There are certain emotions which cannot be explained by words. Sensors can pick up these stress signals, combine them with the available textual data and thus identify the emotional triggers of a customer’s experience.
The comprehensive data on the experience of the user can be gathered by observing the way user interacts with the product or application. It exactly determines how the design is performing and whether it needs improvement or not.
Read more at: http://www.informationweek.com/it-life/can-data-teach-us-empathy/a/d-id/1321022?

Manufacturers, logistics companies, pharmaceutical companies and other energy companies have been using supercomputers to push the limits of research and discovery and answer questions that were not possible using other means. Companies are using cloud to solve problems which were once solved by supercomputers. Cloud computing has encircled high performance computing or HPC and supercomputers along with cloud solutions continue to advance. Companies in the auto industry, airlines, energy are willing to work with supercomputers to solve their big questions. Along with wide range of hardware, optimised software’s run efficiently on supercomputers. Such computing also helps companies to improve their state of art. The six different reasons which encourage companies to use supercomputers are that they have amazing computational capacity, the datasets fit properly into the memory, the interconnect is analytical, advanced modelling power, expansion, and speeds discovery.

Read more at: http://www.informationweek.com/it-life/supercomputers-unleash-big-datas-power/d/d-id/1321013

Data is the most important thing for marketing today and most marketers understand this pretty well. Simply collecting data won't help. It should be able to tell a story. Insights derived from data are very important. Data should be able to help marketers identify their customers and the appropriate offers for them. In an organization, every department holds some data on customers. The first task of the marketer is to get rid of data silos and gather all the marketing data at one place. Once this is done, robust analytics tools can be applied to the data to generate a singular customer view. Read more at: http://www.business2community.com/big-data/marketers-big-data-worth-01263023

Big Data’s prospects are tremendous and will keep growing but only if we can keep up with it. Bangalore now monitors traffic in real time. It is only then that Data Analytics steps in, helping take calculated decisions for the strategic spots for sign boards and road markings. Thereby reducing congestion and allowing safer travel. We see how crucial the hardware, software, networking and data center are in the analysis of such data. Analytical data mining seems to be the majority of the effort now. Smarter data centers, networking practices and rapid storage technologies will be the start of a cycle of innovation that may be a few decade long. Ironically enough, in-depth analytics generates data that has greater need for computation and large storage systems that will allow rapid testing and deployment. Read more at: http://www.firstpost.com/business/big-data-paradox-2323460.html

Imagine your treadmill turning into a wonder machine which knows your fitness goals, takes inputs from you every morning and then suggests the best workout for you. Seems too good to be true. But not that dreamy for Sunil Koduri CEO of Zsolutionz, who believes that fuzzy logic will lie at the helm of realizing this dream. In simple words, fuzzy logic deals with approximate rather than exact reasoning and is most useful where computers are required to make decisions like human. Mr. Koduri, in his special guest feature in Inside Big data talks about how fuzzy logic technology can combine with the huge data generated by wearables to provide personalized fitness experience. No stopping here, all this data from wearables, health history information, and cloud data can be combined to create a huge ecosystem which is user centric to enhance user experience and also keep the healthcare providers in loop. To read the original piece of article, follow the link http://insidebigdata.com/2015/01/12/fuzzy-logic-key-connected-health/

We often struggle to extract real benefits from the large pool of data we have and volume of information is only going to swell in the future. What we need is to "learn to listen to the data".

1. Focus should not be on goal setting but on learning.


2. Many new voices- big data allows us to hear the voices of the silent. Like the customer voice and Workers voice. We can understand our team’s habits in real world by seeing their working behaviors. We can get the employees to tell us how to make the business model smarter and we can use this knowledge to design systems and incentives that make your business better.


3. Addition of sensor data enriches our understanding even further.

4. Wearables will open up whole new sources of data on human behavior and we will also hear from machines, buildings, even animals and oceans.


To read more - http://www.webanalyticsworld.net/2015/06/listen-to-the-voices-of-your-data-for-great-analytics.html