A wealth of past data is available in years of medical records of ICU patients, which can be used to measure risk levels in ICU. In case of predictable risks, checklists can be created of obvious risks like infections caused by catheters or tubes, delirium caused by overseadation etc. In case of less predictive risk, a system can be generated that gives a running risk report of the ICU on the doctor’s hand held devices. Medical centers have developed an application that pulls out data from the EHRs and creates a visual dashboard of the patient that helps doctors in real time monitoring, thereby personalising patient care. Read more at:  http://www.wsj.com/articles/hospital-icus-mine-big-data-in-push-for-better-outcomes-1435249003

Big data can provide contextual intelligence, which would work as a catalyst in supply chain operations. While legacy ERP (Efficient Resource planning) and SCM (Supply Chain Management) are designed for order, shipment and transactional data, big data can create knowledge sharing supplier networks, revolutionizing the supply chain. Delivery networks can be optimized using geoanalytics, which fixes one of the major challenges in this business. Companies that have adopted big data analytics say that there is improvement in customer service and demand forecasting and optimization of inventory and asset productivity. Read more at: http://www.forbes.com/sites/louiscolumbus/2015/07/13/ten-ways-big-data-is-revolutionizing-supply-chain-management/

Sentiment Analysis is a text mining technique used to analyze the sentiment (unstructured data representing opinions, emotions or attitudes) about a particular product or topic, employing machine learning technique. Organizations across the world are extracting insights from the social data to understand how public feels about the product at a particular point in time. Sentiments are first categorized into negative or positive and then analyzed using various software like Hadoop. It can also be performed on excel. To know more, read the article at: https://www.brandwatch.com/2015/01/understanding-sentiment-analysis/

A recent survey on customer retention revealed that insures lose as much as 20% of their customers each year. For the insurance agency this corresponds to approximately £500,000 to £1 million in lost premiums for every 1,000 policies. Customer retention is thus an important issue facing the industry. Three key points can help insurance companies to eliminate customer churn.
• Firstly, they need to properly understand their customers, which mean determining overall market segmentation to understand cross sell and up sell opportunities at individual policyholder level.
• Secondly, insurance companies must be able to provide tailor made policies for each individual and also have a unique selling point in comparison to their rivals.
• Lastly, companies need to realize that they have to listen to their customer’s preferences rather than the customer having to choose from the list of policies the insurer has to offer.
Should the above be kept in mind in a company’s planning and operations customer churn can certainly be minimized if not completely eliminated.

For more information visit:
http://blogs.sap.com/banking/2015/06/19/preventing-customer-churn-with-better-data-analytics/

A loyalty program is less about points and rewards and while these perks might attract consumers, they don’t suggest a sense of loyalty. In this age of data, the focus of these programs should be collection of useful data to help maintain good relations which benefit both consumers and the brand. However a business that trusts consumer-provided data is a business making decisions based on what is untrustworthy, random information. From observation of customer activities and external data sources, advanced analytics can create a profile of the customer for precise segmentation of the customer base. Business analysts and data scientists agree that expanding the data for any given model will typically produce dramatic improvements in analysis. That’s why many organizations have turned to a Big Data solution. Read more at: http://www.smartdatacollective.com/davemendle/323701/redefining-loyalty-programs-big-data-hadoop

Up to 16 Exabytes of RAM can be supported by a 64-bit system. Machines with 128GB RAM or more are becoming common with this era of Cloud Computing and Big Data. The data sets for Big Data are getting too large for even heavily loaded machines with memory despite the best efforts, don't fit into the RAM even after clustering in some cases. Researchers at MIT created a cluster called BlueDBM using Solid-State Drives (SSDs) to get rid of the memory problem. They also moved some of the computational power off the servers and onto chips. By pre-processing known parts of the data onto the flash drives prior to passing it back to the servers, the chips made distributed computation much more efficient than before. They thus got rid of the overhead of running an operating system. Read more at: http://www.itworld.com/article/2947839/big-data/mit-comes-up-with-a-no-memory-solution-for-big-data.html

Uncontrolled urbanization is posing as a great threat as it puts immense pressure on the resources available. Some of the issues that will be faced in this scenario are clean water, clean air, power, waste management and living space.
Big Data and Internet of Things (IoT) are some of the technological forces which are pushing towards smart cities. With the help of Big Data a range of applications are being made available with the help of which smart cities can be made more livable. Applications like smart street lights are already being used in smart cities like Birmingham. Street lights are attached with sensors which monitor footfall and noise levels and based on this data, the light turns on or off. Cities like Bristol, England are installing an infrastructural network that will support the data generated through Big Data and IoT.
These examples are minor steps in building of Smart cities which can sustain growing population without hampering the quality of life. Read more at: https://icrunchdatanews.com/big-data-building-smart-cities/

Hiring a social media marketing consultant can help a company build long term relationships, brand awareness and lead to sustainable growth. Time is of the essence in any business venture. A consultant brings to the table the skill to assess business needs, competition in the market, the targeted audience and builds a smart and efficient digital marketing strategy. Social media is a field that operates 24/7. Keeping that in mind, using social media to promote businesses can lead to increased profit margins. On the other hand, a social media consultant adds to the business valuable information about the market. It helps in making a well informed and better business decision. Social media intelligence also makes it possible to reach out to customers faster making an impact in the business. To know more, please follow: http://www.business2community.com/social-media/3-ways-a-social-media-consultant-increases-your-revenues-01266289

The introduction of Hadoop has changed the outlook of businesses. It is a software system that has made processing large sets of data located on distributed servers an easy task. Hadoop is mainly used for analytical purposes boosting business performances and customer relationship management. It has worked wonders for small businesses as it has incorporated concepts of big data analytics. Data is much more accessible now and the cost of procurement has been drastically reduced. Some convenient features in Hadoop has made it much more attractive for small businesses. Hadoop has made effective use of big data possible to optimize businesses. With its value increasing day by day, more and more companies from various sectors are going in for Hadoop. Read more at: http://www.dataversity.net/an-introduction-to-hadoop-for-small-businesses/

Going digital has its own advantages. Along with flexibility, adaptability it allows for transparency and openness. With increasing openness and transparency, customers get to know the whole truth which helps them to trust better. Lack of trust is prominent among leading societies and economies nowadays and it has impacted them. Customers trust what they use and this is the whole point of digital transformation. It has its own culture that empowers us with digital tools to know more about the customers. Its span includes the big data surge. With more information on customers focus has been shifted to the effective use of these data. Again, while collecting personal data from customers there is often a lack of trust which hinders the whole process. To know more, please follow: http://www.cmswire.com/digital-experience/digital-change-is-cultural-change/

Permission marketing is now trending. Consumers don’t like to get interrupted often and so they take actions to block online ads, promotion emails. This new era of permission marketing can be broken down into “anticipated”, “personal” and “relevant”. A recently concluded study shows customers are willing to share information with trusted brands. Data privacy acts now protects consumers from personal data collection through websites and other online sources. Cookie law is such an example. Customer data can be explicit, implicit or structured by analytical tools. Thus data security is of utmost importance and it is the responsibility of organizations. Consumers are always looking for their needs to be met by companies and this gives the companies the chance to make the best use of data collected providing value to the organization. Read more at: http://www.cmswire.com/digital-marketing/tracking-customer-data-you-better-provide-value/

Data science is the most dynamic and multifaceted industry providing useful and interesting insights with the data. Recently due to more than exponential growth of data and digital revolution, it is becoming increasingly difficult for organizations to use this data efficiently. Sifting data has become cumbersome and getting to the right data has become a top priority. Thus most organizations are formulating strategies to use this data wisely without any hassles. From operational procedures to recruiting employees, right data can direct to right ways of doing it. Thus CEOs are showing more interest in this digitized environment. In short an overhaul in usage of data driven techniques are taking shape to have a leading edge. To  read more: http://www.predictiveanalyticsworld.com/patimes/plotting-your-data-science-strategy-0618152/

Data science with number of practical uses is becoming an indispensable tool for all businesses. Its pace has reached an exponential level in recent times due to many advancements. But many experts are now shifting the gears into next level, predictive analytics, to save the future of this invaluable science, but getting this accepted industry wide is going to be a rough ride. Getting a smooth change from data science to predictive analytics will need an industry wide trust. It also needs the barrier of welcoming the new entrant eliminated by showing the effectiveness of this new technology which will create a helping environment for employees and employers alike. But to many hardcore fans of data science it is tough time leaving it to accept a new technology, only the returns and competitive edge this provides will make the shift more pleasant. Read more at: 

https://icrunchdatanews.com/3-keys-smooth-migration-data-science-predictive-analytics/

Dubai Airports CEO, Paul Griffiths, is revolutionizing the airport experience and working. The Dubai Airport is handling 120 million annual passengers. This causes a need for something preventing the degradation of customer experience. Using in-dept. analytics to assign gates in a dynamic way is the first step. The system can assign gates in a far more efficient manner – such that if two flights have a significant numbers of passengers common between them, they will dynamically be assigned gate positions as close as possible. A new initiative allows retail stores in the airport to scan passengers' boarding passes and provide current updated about their departure gate, time, and the estimated time to move from the customer's current position to the gate. There are thus using data to improve the efficiency and reduce the cost structure of air industry operations. Read more at: http://www.networkworld.com/article/2944535/big-data-business-intelligence/big-data-enables-an-entirely-new-design-of-airport.html

According to a survey, big data analytics and informatics skills are the ‘prospective’ two most important resources, to be used by the healthcare providers in their decision making and data management processes. The health information sector is experiencing some major changes, like, for example, the conversion of paper health records into electronic health records (EHR). Health Information Management (HIM) is also evolving, to be utilizing big data in population health management, predictive analytics, clinical quality enhancements, etc. Health Information Management, needs to view data as an asset, in order to utilize the data, in the best possible way. Read more at: http://healthitanalytics.com/news/him-healthcare-big-data-analytics-skills-increase-in-demand

As the frequency of credit card fraud is increasing, so are the costs associated with these bad transactions that include, lost merchandise, lost profit, transportation costs, etc. Hence, enter fraud analysts, who are continuously updated through automated machine learning, about any sort of fraudulent activities, spotted by customers, online. Five ways to spot and prevent online fraud:
1. Users with no shopping history can be suspected, who may create new accounts only to test stolen credit card information.
2. Bulk orders of multiple costly items are to be suspected.
3. Hastening up a delivery process is to be suspected, as imposters try to get benefited as much as possible, before getting detected.
4. Multiple accounts or multiple shipping or billing addresses, with any one common factor, out of the three, should be cross-checked.
5. Accounts with multiple credit cards should also be cross-checked.
Read more at: http://www.business2community.com/big-data/5-telltale-signs-your-business-is-battling-credit-card-fraud-01271169

Banks lose money with small business loans because of high operating costs cannot make up profit from the net interest spread. Components determining costs of a $100,000 loan are origination, underwriting, loan review, operations, monitoring and collections, and compliance. Locking in a loss and reversing a loss are vital strategies, to reinvent internal cost structures. Management can effectively turn around the situation by:
1. Evaluating the profitability on more holistic basis
2. Increase cross sales
3. Process improvement, by simplifying processes.
4. Outsource or partner on lending, by allowing alternative financiers help reduce cost and increase productivity.
To know more: https://www.bai.org/bankingstrategies/Strategy/Small-Business-Banking/Making-Small-Business-Loans-Profitably?cclass=pointOfView

Choosing the right Hadoop distribution can be a tricky process. There are 4 basic categories that businesses should look at for specific qualifying criteria.
1. Performance
Hadoop is widely chosen as a data platform due to its high performance achieved by replacing the stock MapReduce by Apache Spark. However not all operations need such superior hardware and a business must choose its hardware on basis of the operations it hopes to perform.
2. Dependability
When looking for a distribution, dependability is a significant but rare feature. Only few implementations in Hadoop can guarantee a system availability of 99.999%. Look for a distribution that provides Self-Healing, No Downtime Upon Failure, Tolerance of Multiple Failure, 100% Commodity Hardware, No Additional Hardware Requirements, Ease of Use, Data Protection and Disaster Recovery.
3. Manageability
Look for a distribution that has intuitive administrative tools that assist in management, troubleshooting, job placement and monitoring.
4. Data Access
Gathering and storing data is just the beginning of the process. What really matters is that the stored data must me easily accessible for further processing. Look for a distribution that provides
• Full access to the Hadoop file system API
• Full POSIX read/write/update access to files
• Direct developer control over key resources
• Secure, enterprise grade search
• Comprehensive data access tooling
Hopefully these four specification along with your criterions will enable you to choose the best Hadoop distribution for you.

For more information visit:
http://www.smartdatacollective.com/davemendle/324791/four-considerations-when-choosing-hadoop-distribution

Big Data is a resource that helps organizations to understand and take control or corrective action to address problem such as customer centric service delivery, by accurately joining big data with Master Data Management (MDM). MDM helps to control the momentum of incoming data and tools that applied in MDM helps to verify facts about customers, vendors and products. Data definitions and rules previously established by the corporation are reflected in the MDM. Service Oriented Architecture (SOA), due to its agile nature, plays a key role in the corporation achieving its vision.
There is a symbiotic relationship between MDM and SOA. SOA provides the filtering needed for a steady stream of consistent data, which is an important aspect of data mining as the quality of data derived determines the accuracy of predictions.
Thus MDM is a valuable tool as it has the capacity to store and analyze large volumes of data to discover new patterns which leads to an increase in profitability.

For more information visit:
http://www.finextra.com/blogs/fullblog.aspx?blogid=11073

Forex, in terms of daily trading volume, is the largest market on the planet and should not be surprising that data mining has a lot to do with determining success in the Forex market. Algorithmic Trading is the automated execution of a trading algorithm. In our case, the algorithm is derived from data mining. Once the algorithm is in place, all it requires is some programming codes to allow it to run the algorithm. The most important part of this puzzle is the data used to set up the algorithm-the scope for extensive data mining.
Many open source platforms are available for this purpose and one can choose the desired one according to his/her preference of speed and accuracy with there being an obvious trade-off between the two, given the vast amount of data available to us.

For more information visit:
https://www.mql5.com/en/blogs/post/640795