SigmaWay Blog

There are many challenges that are faced by marketing executives as well as compliance leaders in emerging markets with increased corruption and risks. Each country has unique sets of market opportunities and risks and cannot be transferable to separate market. Markets are also dynamic and applying the same outdated models might give inappropriate results. Strategic intelligence is the need of the hour, with updated market intelligence and multiple data sets. This updated market information would account for changing market conditions.There should be a right mixture of centralized and remote market intelligence and MNC's should extract information from various sources. Finally, there should be a mutual collaboration and sharing of responsibility between compliance and market intelligence leaders. Read more at : https://blogs.metricstream.com/can-marketing-compliance-share-playbook/

 IBM announced last week it has moved its cognitive computing system into the cloud to form the Watson Discovery Advisor, allowing researchers, academics and anyone else trying to leverage big data the ability to test programs and hypotheses at speeds never before seen.

"I think there have been a number of ways that we have improved the system since Jeopardy", said John Gordon, vice president of IBM Watson Systems.

IBM has been honing Watson's capabilities over the last three years, reducing its size and upping its power since its famous appearance on "Jeopardy!" in 2011.

"Part of Smarter Cities was working with different municipalities and governments to determine how technology could help them provide better services to their constituents", Gordon said.Read more at: https://www.fedscoop.com/watson-cloud/

Social media analytics or SMA, is the practice of gathering data from social media websites and analyzing that data to make business decisions. The most common use of social media analytics is to mine customer sentiment to support marketing and customer service activities and turns the vast amounts of semi-structured and unstructured social media data into actionable business insights. Depending on the business objectives, social media analytics can take four different forms. The first two are reactive in nature, while third and fourth are proactive in nature. First is descriptive analytics. Descriptive analytics gather and describe social media data in the form of reports, visualizations, and clustering to understand a well-defined business problem or opportunity. Second is diagnostic analytics, it can distill this data into a single view to see what worked in the past campaigns and what didn't. Third is predictive analytics, it involves analyzing large amounts of accumulated social media data to predict a future event. And the last one is prescriptive analytics, it suggests the best action to take when handling a scenario. Read more at: http://www.analyticbridge.com/profiles/blogs/4-types-of-social-media-analytics-explained

Career opportunities in the technology sector are increasing. Technological sector, like any other sector, needs employees in finance, marketing, sales and human resources. In case of a transition in career, an experienced field can be chosen. Knowledge and skill never go to waste. Four career transition tips are mentioned here. 1. Focus on soft skills because no matter what sector you work in, it is a key factor. 2. Use networks to make connections in the sector. 3. Do your homework and gain knowledge in your field. 4. Identify and prepare a narrative of your strengths that will be useful to the sector. Read more at: https://www.forbes.com/sites/davidkwilliams/2017/06/04/making-a-career-transition-to-technology-its-not-rocket-science/#98ef669474c7

All organizations face pressure to improve performance. This is difficult as there exists risks which reshapes the businesses. As the risks become more intertwined, managing them becomes difficult and leads to chaos. GRC helps the businesses to achieve task of managing everything under one umbrella. GRC helps simplify the complex and huge data. Most businesses are implementing AI systems to speed up the investment decisions. Systems will be able to automatically collect data from various data streams and channels. Also analyze it against the company’s existing datasets and operations, making suggestions regarding the changes. As technology evolves, algorithms improves and probability of errors reduce. Cyber risk is a new threat. As companies face greater pressure a more advanced GRC technology is to be adopted. Read more at: http://www.itproportal.com/features/the-road-ahead-the-coming-rise-of-artificial-intelligence-in-governance-risk-and-compliance/

The present business environment is volatile and full of uncertainties. Therefore, a need arises to improve efficiency and profitability. Though many organizations rely on traditional techniques, predictive analytics is the new trend of managing risks and monitoring frauds which eliminates all the guesswork. Predictive analytics help us in reaching the source of fraudulent transactions and in dealing with future plausible attacks. Lack of corporate transparency and missing public trust should be dealt with by using advanced tools for managing huge data and ensuring accountability. Predictive analytics helps in building the customer profile to know his credibility which is useful for banks. Read more at : https://blogs.metricstream.com/ready-predictive-analytics-revolution/

The data scientist focuses their efforts on developing analytics solutions that solve a specific and unique business problem. The primary reason for this declining demand, according to the author was that increased automation and operationalization of business processes will not require the technical skills of the data scientist. IT individuals steeped in the more traditional computer science discipline are trained and developed to focus their skills on developing solution that streamline business processes. Creating a technological environment which allows access to data and the potential for increased automation is the role of IT. This kind of scenario is simply going to increase the demand for both data scientists and IT in our Big Data world, regardless of certain opinions on the declining importance of data science. .Read more at: https://www.smartdatacollective.com/demise-data-scientist-heresy-or-fact/

From gyms to the front desks of Medical practice center, analytics are used everywhere. Most of the sectors have been semi-automated. Some small businesses, however, have failed to use analytics. Except these exceptions, most of the businesses have been successful in combining data science and cloud technology. Data analytics are essential for medium and small scale companies as well to be successful and data centric transformations are now trending. Read more at: http://www.zdnet.com/article/using-analytics-for-health-commerce-and-more/

Consumer Intelligence is largely focused on improving customer behaviour and enhancing consumer products which are tailor made to match consumer expectations. AI helps industries to introduce new product features by finding patterns in huge datasets. There are two types of categories in consumer AI : front end bots and AI assisted human agents. Chatbots take care of customer text queries. AI in enterprises has been useful in Enterprise Resource Planning. Enterprises are conducting predictive analytics in developing AI applications. Enterprise AI can be of two types- Applied AI and Artificial General Intelligence. Though comparing these two enterprise AI is complex and requires much more expertise  than consumer artificial intelligence. Read more at: http://analyticsindiamag.com/enterprise-ai-vs-consumer-ai-understanding-two-differ/

Random forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. It is called random because there are two levels of randomness; at row level and at the column level. In spite of it being such a convenient process to deal with large datasets it has a few disadvantages. In case of smaller datasets linear regression is a better method than this. Next is that any relationship between the response and independent variables can't be predicted. Also, this process is very cumbersome and can't take values from outside the datasets. Even then, random forest is advantageous because keeping the bias constant it can decrease the variance in the datasets and it helps us ignore most of the assumptions like linearity in datasets. Read more at: http://www.datasciencecentral.com/profiles/blogs/random-forests-explained-intuitively

It’s no secret that President Trump prefers tweeting over talking to the public — particularly when nearly everyone else in the country is fast asleep.

Trump’s Twitter archive shows that some of his angriest and most flamboyant accusations are issued early in the morning.

For most people, the middle of the night and the very early morning are not great times to make decisions, to say nothing of making policy pronouncements or political commentary.

At those times, you are likely to be close to so-called REM or dream sleep, which we all know brings about intense and often distorted emotions and thoughts, often about the events in our everyday lives.

These are times for reflection, not for social media.

A bit of unsolicited medical advice for President Trump: For the sake of the nation, stop tweeting and go back to bed.Read more at: https://www.nytimes.com/2017/06/05/opinion/mr-trump-stop-tweeting-sleep-deprivation.html?rref=collection%2Ftimestopic%2FSocial%20Media

Records were exposed globally, killing security running for support. A wannaCry attack leaves hints to the user on a network system. Moreover, how the security is protected or responsible by the fact of undercovering its data and information more strongly not be damaged to expose, also how the data miners are fighting against these cybercrimes. AI refining the threat of cybercrime and developing more ideas and innovation to counter such attacks. Not only that AI and quantum computing are challengers, but also a way with uncertainties. Read more at: https://www.ft.com/content/1b9bdc4c-2422-11e7-a34a-538b4cb30025

Price Transparency Tools provides many options to select the required services. It helps customers to reduce medical costs. Statistics say that the proportion of moderate income community group is more likely to use this tool as compared to higher income community. Thus, the aggregate spending is still high. Consumers who are good with internet mostly use this tool. Thus, it should be marketed properly so that it reaches out to a larger number of people. Sending out reminders is a great option to start with the process. Read more at : http://www.healthcarefinancenews.com/news/healthcare-price-transparency-tools-seldom-used-study-finds-more-reminders-marketing-needed

This year was good for SaaS startups in the country. Indian markets adopted newer technologies into its systems. More opportunities for SaaS players are expected from the BFSI sector. SaaS companies use business model that provides software solution over the internet and they charge the customers according to the usage of software since most of the financial solutions have gone online. The software uses around 2000 data points to evaluate the credit score and corresponding interest rate. Many big data analytics startups provide solutions to industries. Read more at: http://economictimes.indiatimes.com/small-biz/policy-trends/digitisation-push-makes-bfsi-sector-attractive-to-saas-players/articleshow/56325400.cms?from=mdr

Self-service analytics is an approach to data analytics that enables non-tech savvy users or business users to access data for more informed decision making.  For success in self-service analytics, employees should have the culture of using data to start, propagate or conclude every conversation. A few areas required to support this cultural change are, organizational readiness which will help in determining the type of self service tool required for the organization. Next is data readiness i.e. continuous feedback about data quality practices should be given. Third is data security readiness i.e. data security, compliance and data access should be carefully examined during making a transition to self-service analytics. Fourth is that users should be adaptable and willing to use new technology. And lastly, data shouldn’t be interpreted just by preparing charts instead it should be used to make theoretical interpretations. Read more at: https://www.blueoceanmi.com/blueblog/self-service-analytics-need-cultural-change/

With all the talk about predictive machine learning and deep learning applications, one can lose sight of the data engineering, some might call it data art that is needed to prepare the data to work on. Many questions go into the planning for deep learning applications like should the processing be disturbed; how much noise obscures the signal arriving from internet of things devices such as cell phones. In the case of mobile phone sensors, data preparation for deep learning applications can present unique problems, data preparation can involve considerable preprocessing. Insurance and other industries are entering the golden age of sensor data, but the data needs preprocessing because the data initially is very noisy. Given the Data, the algorithms will figure out the right transformations of the data. Read more at: http://searchdatamanagement.techtarget.com/news/450419925/Data-prep-for-deep-learning-applications-means-careful-planning

Going digital has directly hit demographics and technology of retail industry in a positive way. The economy of the retail industry is also shifting quickly, retail stores must do more to keep their stories intact without damaging their brand name. The retail industry is at a crucial moment with online sales resulting in periodical of all sales. Retailers must first examine and learn from leading physical and online retailers before implementing an e - commerce strategy. Retailers need to enhance their technology user base by collaborating with e - commerce giants of the market. The majority of e - commerce firms use social media like Facebook, Twitter and Instagram, to give a shot to great digital marketing tools, retailers should use these platforms more often to disclose their great offers and collection.  Read more at: https://www.entrepreneur.com/article/290131

BFSI is expected to pay out large this time, unlike the last time.  After the IT sector BFSI is on the top for its salary increment. Reactions are different for different sectors. There has been low supply and high demand for the blue-collar jobs, sophisticated profile jobs have also emerged. Variance between temporary and permanent salaries have narrowed sharply. E-commerce and educational services are both additions to the list, these focus on talent acquisition and pay high salaries to acquire it. Project managers and analysts are the roles that get the highest salaries. Certain new job profiles are taking away the focus from IT roles. Read more at : http://economictimes.indiatimes.com/jobs/bfsi-sector-edges-out-information-technology-to-become-top-paymaster/articleshow/58172956.cms?from=mdr

Many data analytics firms are working for the betterment of the farmers. These companies integrate satellite, weather, and IoT analytics with the agricultural sector. They use its proprietary machine learning and parallel computing techniques, to resolve complex relationships like crop growth and soil health. Using analytics farmers can opt for a smart sampling procedure using satellite – based crop clustering techniques, which reduces the time for identification of these plots and optimize their locations. While the former requires timely crop intelligence, crop insurance companies need highly accurate assessment of risk. The satellite imaging analytics serves two purposes: First, it ensures that the farmers receive a fair and immediate compensation for crop loss due to adverse climatic conditions. Second, it enables insurers to settle claims speedily due to the availability of data in near-real time without any manual intervention. Read more at: https://yourstory.com/2017/05/satsure/

Is Netflix about to set forth on a cancellation binge? A week after it axed “The Get Down,” its expensive single-season music drama, Netflix announced Thursday that it was canceling its sci-fi drama “Sense8” after two seasons.

After 23 episodes, 16 cities and 13 countries, the story of the Sense8 cluster is coming to an end.

Netflix has poured billions of dollars into original TV shows, and it has only canceled a small fraction of them.

But the company’s chief executive, Reed Hastings, recently suggested this was something they might be prone to do.

“I’m always pushing the content team, We have to take more risk, you have to try more crazy things, because we should have a higher cancel rate overall,” he said Wednesday in an interview with CNBC. Read more at: https://www.nytimes.com/2017/06/01/arts/television/sense8-netflix-canceled.html?rref=collection%2Fsectioncollection%2Ftechnology&_r=0