At Paris Air Show 2015, Bombardier plans to bring its CSeries jetliner that carries Pratt & Whitney’s Geared Turbo Fan (GTF) engine which uses great swathes of data. But data created by industrial equipment such as jet engines, gas turbines and MRI machines has more potential business value on a size-adjusted basis which makes it valuable than other types of big data being generated from the social web, consumer Internet and other sources. Finally, the Internet of Things is helping manufacturers manage their Pay by Hour engagement models and long term maintenance contracts at lower costs. With rapid advancements in the Internet of Things in aircrafts combined with data analytics, it’s truly exciting to work in the aerospace industry. Read more about this interesting article at:  http://channels.theinnovationenterprise.com/articles/7319-analytics-and-the-internet-of-things-in-aerospace

In the healthcare industry, big data and business intelligence work as complements which have helped the industry to achieve a remarkable growth. In today’s world data collection has its own advantages but we need instruments to refine them and make sense out of them. Herein lies the importance of clinical and business intelligence. More and more organizations are resorting to business analytics. It helps to make better clinical decisions and organizations stand to benefit from it in the transition from a fee based industry to a value-based one. Organizations have targeted their focus on population health and are implementing C&BI solutions accordingly. These help in deciding on the best strategy for the industry as the care models are undergoing a change. Read more at: http://www.dataversity.net/infographic-business-intelligence-in-healthcare/

Since 19th century, Artificial Intelligence always made everyone very curious but only recently we started to see its applications. Even now its applications are very narrowly limited to natural language processing and image processing. Some people are scared that AI may one day replace humans in many industries and cause unemployment. Let's see what two pioneers in the industry of AI think. Read more at: http://www.technologyreview.com/news/537941/emtech-digital-where-is-ai-taking-us/

Content marketers are delving more into analytics to give interesting insights about their past content. With some careful analysis of this content they are able to come up with new content to meet the needs of their customers. This exercise of tracking the past performance data is important for the content marketers in many ways. Even without analytics, most rigorous analysis and tracking of the data can give information about what to do next. The usage of analytics is giving better pay offs and is the new trend among Content marketers. With the new technologies emerging the usage of analytics tools is expected to make valuable recommendations to help identify the right strategies. 

Read more at: http://www.acrolinx.com/blog/predictive-analytics-content-marketers/

Need a big data solution for your project? The 3 V's of big data will bail you out. In an article by Paul Holland (Consultant), he talks about the 3 V dimensions of big data initially discussed in 2001 by META group (now Gartner) analyst Laney Douglas in a research report. Fourteen years hence, big data challenges and opportunities have acquired 8 V dimensions. The author exclusively focuses on the 3 V's:

• Volume
• Velocity
• Variety 

Volume talks about zettabytes of data generated every second. For example, if we look at airplanes they generate 2.5 billion terabytes of data every year. Velocity is about the speed with which data moves around. Unlike earlier times, data is created in real time or near real time. Variety is basically the different types of data. Gone are the days when data used to fit neatly in tables. Today 80% of world data is unstructured (text, images, videos, voice etc.). Read more at:http://makingdatameaningful.com/2015/05/26/characteristics-of-big-data-part-one/?utm_source=rss&utm_medium=rss&utm_campaign=characteristics-of-big-data-part-one

Digital media publishers have taken their time to incorporate big data tools into their businesses. There have been marked improvements in clarity on use cases for big data and toolsets have also grown accordingly. Big data helps in the content creation side catering to the readers and the advertisers. Keeping the target audience in mind,big data tools optimizes decisions and effectively monetizes generated traffic. Relying on editorial expertise and sound journalistic judgements are fast becoming things of the past. However web analytics softwares like Google analytics, Webtrends also help in tracking content consumption patterns.To know more, please follow:

http://www.dataversity.net/big-data-tools-for-publishers/

Most companies don’t get rid of the currently unused data, with the expectation it may be required to forecast trends in the future. This data becomes ‘dark’ data when the company completely forgets its existence and it becomes difficult to locate it and interpret it or interact with it. However, this ‘dark’ data can be restored or ‘lightened’ up by the following steps:
1. Data filtration, that can isolate the required information in the means of filtering the data feeds, to prevent the accumulation of excess unnecessary data, but is only useful when the data center manager can identify the data, expected to be valued in the present or future times.
2. In case of any future requirement, it is always better to export the data (instead of deleting it) to a cloud-based storage system, from where it is possible to import the data into the company’s data system, whenever required.
3. It is important to properly define, data withholding policies in order to facilitate data center management.

Read more at:http://www.techrepublic.com/article/clear-out-dark-data-to-make-room-for-useful-big-data/

With increasing complexity in the sales environment, improvement in growth and retention of customers is required. Prescriptive guidance can provide answers to these. It is predicted that implementation of analytics tools and strategies will increase. So, B2B companies will reinforce a position for Chief Growth Office (CGO) who will require analytics tools to implement their ideas.
Thus, a step towards Prescriptive analysis and effective implementation plan will help B2B companies to improve their financial performance.
To know more about prescriptive analytics, follow the article written by Javier Aldrete (senior director of product management at Zilliant) at: http://www.inddist.com/articles/2015/05/rise-prescriptive-analytics-b2b-environment

Providing good customer service is a challenge for most businesses and the trends are ever changing. Customers embark on an online journey whenever they buy something. The customer data - a string of emails, conversations across the web and social channels, leave a large digital footprint. Measuring and analyzing this information can help brands narrow down their marketing efforts and investments. Irrespective of the nature of the website, sentiment analysis is crucial. It can tell you whether your customers are frustrated, indifferent, or impressed and suggest measures to that accord. Monitoring customer engagement, referral traffic and time spent on pages can help in understanding the buyers’ journey. Thanks to Big Data, we can work based on intelligence rather than guesswork. Read more at: http://www.forbes.com/sites/danielnewman/2015/06/09/big-data-and-the-buyers-journey-measuring-the-invisible/

The factors currently stressed by predictive analytics, in talent management are:
1. Recognition of potential employees with a flight risk.
2. Recognition of selection factors which can predict the performance of the newly hired employees.
3. Prediction of the productivity initiation time of employee studies.
With the evolution of predictive analytics, new factors that can be expected to be focused on, in the near future include:
1. Projection of the revenue pay-off of a company’s talent programs.
2. Projection of the company’s corporate revenue and labor cost ratio.
3. Projection and comparison of performance indices of a company’s managers, teams and employees.
4. Projection of employee replacement costs.
5. Prediction of upcoming productivity issues and opportunities in the firm.
6. Prediction of the feasibility of new technology solutions.
7. Prediction of diversity bottlenecks.
To know more, visit:

http://www.ere.net/2015/03/09/the-future-of-predictive-analytics-the-next-generation-of-talent-metrics-to-consider-part-1-of-2/

Big Data and Predictive Analytics are booming and Indian recruitment agencies are jumping on board to hire more and more Data Analysts. According to a TeamLease report, the key trends that will dominate the recruitment industry over the next six months include Information Technology (IT), engineering. It is also predicted that startups will be key hirers and adoption of Recruitment Process Outsourcing (RPO) will increase. The nation is quickly realizing the importance of Big Data and Predictive Analytics and both large companies and start-ups are beginning to employ Data Analytics into their workings. The fields where Data Analytics will primarily be required include market risk analytics, facility management and mobile applications employing data sciences. Read more at: http://timesofindia.indiatimes.com/business/india-business/Big-data-and-predictive-analytics-likely-to-dominate-recruitment-TeamLease-report-says/articleshow/47588357.cms

Health insurance carriers are currently in a dilemma, whether to purchase an analytics platform, assemble a platform and then use it. Lack of proper infrastructure is a big hindrance for analytics in healthcare system, dominated by paper-based transactions and the dearth of their electronic imprints. Since health contracts are based on the payment of money, depending upon the health status of patients, health insurers have an incentive to build an analytics platform. Currently, the healthcare insurance companies, who are willing to purchase analytics platforms, have limited options to choose from. While options are being worked upon, healthcare insurance lags behind property and casualty carriers, who are not restricted by intricate security measures and government hindrance. Read more at :

http://www.insurancetech.com/2015-outlook-analytics-in-health-insurance/d/d-id/1318241?

Sigmaway consultants have worked with clients in benefits solutions workspace providing analytics on healthcare plans. For more details visit

http://www.sigmaway.us/

In coming years, Data generated by IOT devices are predicted to increase more than the available capacity to store, and all data that's generated is not useful for analysis. Only limited part of it is useful. 

According to Computer Business Review(CBR) magazine, there are some data generating areas. It is important to know what data from which area is precious and what is not. Following are the ten of the biggest data generating areas. To know more about these areas, read: 

http://www.cbronline.com/news/internet-of-things/10-of-the-biggest-iot-data-generators-4586937

There are variety of languages and modeling packages available at the dispense of any predictive modeler. In most surveys capturing the trends and usage of various packages and software tools, R and Python occupy the top positions. Surprisingly these are both command line languages. The reasons for this are many. But what are advantages of using command line languages like R and Python or GUI based packages? Which user interface is useful to what kind of programmers? What is the market share of usage of these packages? Which one among them is most useful for a job aspirant? To know answers read

http://www.predictiveanalyticsworld.com/patimes/what-programming-do-predictive-modelers-need-to-know-0408152-2/

Companies are collecting datasets to have a competitor advantage in the market. But what exactly are they doing with the collected data? And how are they dealing with the ever increasing data that is inrushing their servers or storage units? 

To answer any of above question, we need to know about a process called Data Mining. Data Mining is a process used to analyze raw information to try and find useful patterns and trends in it. Basically a data miner’s job is to make some sense out of the huge pile of data that is available. There are a lot many techniques available to do this. If you want to know more about data mining and these techniques go through: http://www.businessnewsdaily.com/5947-data-mining.html

Like everything else, businesses must also do yearly cleaning of their contents to remove redundant, obsolete and trivial data from their systems commonly known as ROT. This ROT makes it difficult to find data which are important and useful. A few tips to do this daunting tasks of data cleaning given by experts are: • Identification of relevant and critical data for the business from the identified repositories with the help of Subject Matter Experts (SME)

• Analyzing data in these repositories which are suspected to be important with the help of SMEs, automated processes and leveraging software.

• Establishing metadata for finding and retrieving documents, access control, privacy policies and potential business value

• Metadata needs to be classified for all documents in the repository. This classification process reveals the ROT.

These essential chores can lead to cost-effective information governance by eliminating ROT.

Read more at: http://www.cmswire.com/cms/information-management/do-your-chores-clean-out-your-data-029232.php

We are all very used to Power outages in our daily lives but little do we know about the impact it causes on a large scale. Power outages have the potential to put an economy on a downward trend. Addressing the issue of Power outages are being done lately through various technology advancements.One such advancement in this field is to use grids that are smart and automated. Smart grid technologies are enhanced with grid communication by employing grid radios, which are connected in mesh type of layout. They automatically switch lines and isolate faults in few seconds which otherwise take hours to restore power. The selection criterion in accepting these communication technologies is based on their reliability, performance and future proofing. 

In order to build a complete smart electrical network we have different technology suites employed at different stages, and these technology suites are may not n\be from the same company, which means the companies must ensure that their products are capable of running simultaneously side by side. 

Read more at http://www.clickgreen.org.uk/opinion/opinion/126074-how-smart-city-technology-will-reduce-the-impact-of-black-outs

Big Data is growing bigger and bigger every day.

But, as it is growing, it is also becoming more complex. And complexities usually lead to discrepancies in interpretation of same information. As it is said that, “Prevention is better than cure.” Similarly, identifying these discrepancies and the reasons behind them at an early stage is better than allowing them to become a bigger problem.

Lisa Morgan, Freelance Writer, in her presentation at Information Week, has pointed out six major causes behind big data discrepancies. They are:

• Same Data, Different Quality
• Data-Cleansing Issues
• Problems with the Algorithm
• Models Differ
• Model Complexity Differs
• Interpretations Differ

To understand them in detail, please visit the following link:

http://www.informationweek.com/big-data/big-data-analytics/6-causes-of-big-data-discrepancies/d/d-id/1320692

The global healthcare big data market is set to grow at 17 percent compounded annually according to the predictions of ResearchFox Consulting. Predictive and prescriptive analytics shall be the main area of focus in the United States. The Internet of Things (IoT) Industry is likely to get a big push from internet-enabled blood pressure monitors, mHealth apps, and wearable technologies. With the increasing need for interpolation of health data, improved healthcare coordination and robust big data analytics are becoming highly essential. Healthcare is in need of accurate data, real-time insights into patient care, and a better understanding of population health management, big data analytics is expected to gain importance. Read at: http://healthitanalytics.com/news/healthcare-big-data-analytics-driving-billions-in-market-growth

In the present scenario, the retailers who are combining customer analytics, internet of things (IOT) and data innovation to retrieve and analyze data of consumer preferences, are generating the maximum sales. Others are losing out. Customers now-a-days are well aware of their personal data being collected online by firms and hence expect better retail experience in return. That will only be possible when customers are segmented and served, which is done by analyzing their personal data, using data analytics. Big data can also be used in framing a product's optimal price system and inventory management, according to the prevailing or to be prevailed customer preference trends. Read more at:

http://channels.theinnovationenterprise.com/articles/grasping-the-value-of-data-analytics-in-retail