SigmaWay Blog

The big data has penetrated into healthcare. But merely collecting the patient records and keeping them in the form of electronic health records (EHR) is not enough. It starts with using programming software languages like R to sort through the huge database. R is an open source programing software used for statistical computation and data mining. In this case, R can be used to interpret the EHR for different purposes like side effects associate with a particular drug among a typical set of patients with similar records and could also help identify what causes the side effect. R can be customized according to the needs of the patient and the physician and thus can be helpful in providing personalized healthcare .Also, since R is open source software, it involves no cost and a large amount of documentation is available to learn the language. Read more at:http://www.healthcareitnews.com/blog/radical-potential-open-source-programming-healthcare 

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/

Data mining sounds like a monotonous activity on a pile of information, requiring little oversight. It is however, in the words of Professor Uwe Aickelin, University of Nottingham, "A discipline that blurs the lines between artificial intelligence, machine learning, statistics and other cutting-edge disciplines to unearth the golden nuggets that lurk within data." He explains how data mining is the effort to extract valuable information from unstructured or 'messy' data. Statistics fails to recognize patterns and it is here where Evolutionary Computation and Machine Learning is required. Industries have begun to understand the need to make sense of the large amounts of data out there and Data Mining is more important than ever now. Read more at: http://www.gizmodo.com.au/2015/05/why-data-mining-is-so-important/

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/

Self-learning machines need data to learn. So one possible way to get the data that is being generated from all the devices being used across the world is IoT-Internet of Things. Devices communicate with each other and are interconnected through various networks. This could help advance many fields especially medical field. But a standard protocol has to be developed for devices to communicate with each other. It was proposed that the protocol should take care of safety of humans, security of IT infrastructure.

To read more, follow: http://www.v3.co.uk/v3-uk/analysis/2410409/big-data-and-iot-powered-self-learning-machines-wont-take-over-the-world

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/

 A key role for HR administrators is talent management. When counselors have the HR support, it enables them to work effectively with clients and find the support they need themselves. Often challenges like staffing, talent management and employee relation are encountered by organizations. HR decision relates to employee welfare and engagement. Now-a-days, organizations include payroll costs, benefits administration, HR administration, talent management and time and preference. Although at midsize companies, many HR leaders cannot identify these areas and when midsize companies start to identify them, it will enable their workforce to achieve broader company goals.  Read more at:                                                 http://blog.adp.com/2015/05/07/the-real-human-impact-of-human-capital-management/

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

Each human is unique in his own ways. For treatments to be effective, it can't be based on what the averages are. "Biotech has progressed to the point where we're actually able to edit or code DNA …," said Andi Karaboutis, Executive VP of Biogen, who was detected with Greaves disease a decade ago and undergoing iodine ablation treatment. The doctors then did some stratification to the normal range to suit her body needs and within three weeks she was back to normal. "Internet of me", the combination of computing power and health research, can lead to treatments being personalized improving the healthcare benefits. Read more at: : http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/the-internet-of-me-is-getting-real-in-healthcare/d/d-id/1320348?

Micro lending is a way through which micro finance institutions (MFIs) provide micro credit to support entrepreneurship in rural areas. According to a Wall Street Journal report, experts have come up with ways that would make micro lending more effective. Instead of providing loans solely for investment purposes, MFIs could also lend for personal expenses if the borrower has the ability to pay back the loan with interest. Also, lenders should employ research to measure the success of the financial programs, making sure that the client not only returns the debt but does so while making a profit and not by selling his assets. Another way to make micro lending effective is to take up a holistic perspective. Quoting Iskenderian “Micro lending will be more effective if there are other safety-net and asset-building products in place—like insurance, savings and pensions—so that families can be secure and repay that loan.” Last but not the least is to make use technology for money transfer and big data analysis of mobile phone usage thereby easing out the whole process and saving time. Read more at: http://online.wsj.com/ad/article/mlf-5-ideas-to-make-microlending-more-effective

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/

In a saturated market, even the powerful brands have a hard time achieving growth targets. Big companies can do so by entering into new categories, which would increase the brand strength across markets. An established brand name would endorse itself in the new market. A classic example is National Geographic Society's shift from magazines to television channels, expeditions, and retail stores that sells books, clothes, and travel gear. The first step to go about brand extension is to look out for the categories that can be entered into, the target markets. The next step is to define how well your brand would fit into those markets, which would require assessing a new market like a start-up would. The third step is to design a prototype and test it against failures. Finally, prepare your response to the competitor's reactions; make sure the organization in ready to go. Successful brand innovation would harness the potential of the brand, all the while serving consumers better and bigger. Read more at :http://www.mckinsey.com/insights/marketing_sales/incumbents_as_attackers_brand-driven_innovation

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

The mission of Matt Morgan, the vice-president of global product marketing of Hortonworks is to establish Hadoop as the foundational technology of modern enterprise data architecture. Hortonworks Data Platform (HDP 2.3) is the only enterprise Hadoop-based platform that is made up of 100% Apache open source components. Enhanced security and data governance have been added to HDP 2.3 including new encryption of data, and the extension of the data governance initiative with Apache Atlas. But many doubt that skill shortage is one of the barrier to Hadoop adoption. Read more about this article at:  http://www.cmswire.com/big-data/is-hortonworks-paving-the-way-for-pervasive-hadoop-adoption/

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/

Lean Manufacturing is a business improvement philosophy which focuses on the needs of customers. Lean Manufacturing originated from the Toyota Production System. When a company reduces wastes, many other benefits are achieved. One of the basic advantages of implementing lean is that it helps in getting more work done with less people.
Lean manufacturing helps to reduce costs, eliminate waste, increase productivity, maintain high levels of quality and thus increase profit.
Read more at: http://www.processexcellencenetwork.com/lean-six-sigma-business-transformation/articles/why-does-your-business-need-lean/

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/

The sale of insurance and other similar products through a bank to help it customers at situations is known as Bancassurance. Development of Bancassurance in India began to improve the channels of insurance policy so as to reach the hands of the common, to widen the area of working of banking sector, to improve the services of insurance. The Reserve Bank of India and the insurance development and regulatory authority have a set of guidelines for companies to form Bancassurance. It basically encourages people to buy insurance policies increasing the number of providers. It also has demerits such as interest conflict of bank and insurance policies, compromising on data security, etc. Read more about this article at: http://tips.thinkrupee.com/articles/bancassurance-in-india.php

The success of the startup is not dependent on the idea, but it depends on getting the customers. As a startup, getting customers is one of the biggest challenges. 
Jeff Foster (Co-Founder and CEO of Tomoson), discussed about few tips to get your first 1000 customers. Some of them are:
• Setup a waiting list
• Use personal network
• Target online publications
• Get bloggers on your side
• Build suspense
• Work with early adopters
• Create high quality content
• Offer a free product option
• Incentivize customers to sell your product
• Online advertising
Read more at: http://www.entrepreneur.com/article/247049