SigmaWay Blog

Location-based tools can be easily imagined to be used for transportation purposes, but this technology has also recently been linked to the healthcare sector. With modern location data, you can easily find where all the physicians are and they can be easily called to the facility in a case of emergency. Similarly, an injured patient can be sent to the exact place in the facility that is not that busy as compared to others. Recently, a hospital in Wisconsin saved $850,000 in overtime staffing costs by placing a similar system. Find out more at: 

https://www.smartdatacollective.com/real-time-location-data-revolutionizing-healthcare-industry/

There are many levels to an influencer marketing campaign, just like any other marketing strategy. The basics of these marketing strategy are vital i.e. find a fit for your brand, establishing a budget, and be thoughtful and thorough while you engage your influencers. But, it can be difficult to fine tune your campaign and catch how to quantify your investment, both for your team and for the people who approve your budget. So, this article link by Kristen Matthews (specialist in creating content) gives us some hints to get your numbers and strategies on point. Read more at : 

https://www.business2community.com/digital-marketing/next-level-influencer-marketing-roi-answers-for-your-brand-02061232

Location-based tools can be easily imagined to be used for transportation purposes, but this technology has also recently been linked to the healthcare sector. With modern location data, you can easily find where all the physicians are and they can be easily called to the facility in a case of emergency. Similarly, an injured patient can be sent to the exact place in the facility that is not that busy as compared to others. Recently, a hospital saved $850,000 in overtime staffing costs by placing a similar system. Find out more at: 

https://www.smartdatacollective.com/real-time-location-data-revolutionizing-healthcare-industry/

As the data in the ‘datasphere’ is about to increase a thousand percent by 2025, data giants like Amazon and Google are looking forward to Object Storage, making File and Block storage methods the news of yesterday. But, even that is not enough, billions of dollars are being drained annually to improve the data centre around the world. Therefore, it’s time that we go to the next step, taking data storage out, into the space. Read more at: 

https://www.smartdatacollective.com/data-storage-space-works/

The latest version of Deep Learning Application (Bayou) will help humans in programming. With the help of just a little information and some keywords, Bayou can almost predict the programmers brain. The researchers have used method of Neural Sketch Learning to train its Neural Network.  It can easily take over forums like Stack-Overflow because of its instant reply to programming problems. Read more at: 

https://www.futurity.org/artificial-intelligence-bayou-coding-1740702/

Nowadays, we can hear about lot of myths surrounding AI and ML. This article link has busted those myths and allowed the truth to rise to its deserved place in the informational hierarchy. The basic misconception is that most of the people link AI and ML directly to robot, which is not true. Other than this, there is a whole other discussion of AI taking over humans, to which even Stephen Hawking and Elon Musk agreed. In some economical respects, these threats seem to be real, like self-driving cars replacing human drivers. But a new trend is on rise, in which the AI is included in the business structure by shifting the human staff to a new position. AI/ML is still an infant and hence we shouldn’t fear AI takeover anytime soon. Continue at: 

https://www.smartdatacollective.com/busting-myths-ai-machine-learning/

Proper call centers must have an efficient balance between customer experience (CX) and user experience (UX). UX is the experience with the product, while CX is the review of the interaction with the brand. UX is a subset of CX. CX envelopes a wider scope of experiences. A bad UX with a good CX can be seen as a good experience overall, but a bad CX and a good UX is seen as a bad experience. Despite covering wider area, CX has some dependence on UX as well. Firms should aim at optimum CX, involving best UX via its services. Read more at: https://www.ameyo.com/blog/building-a-modern-day-contact-center-where-ux-meets-cx

Many executives in the healthcare industry suggest that predictive analytics will cut cost in their organization. A survey conducted in February 2017 forecasted that predictive analytics processes will reduce 15% of the cost for more than five years. A majority of healthcare industries already use them. Lack of budget is a biggest challenge for the implementation of predictive analytics. Lack of skilled employees, too much data, lack of confidence in the accuracy of data and lack of support of technology and executives are some of the important challenges healthcare industry faces of implementing predictive analytics. Read more at :

https://www.information-management.com/news/predictive-analytics-seen-as-cost-cutter-by-healthcare-execs?feed=0000015a-13e1-deb4-ab5e-9bfd65d50000

There are many ways in which a first-time customer will feel enticed to be a full-time customer and maintain a long relationship with the company. One should understand the profile of the typical customer of the company and segment the group both geographically and demographically. Giving customer's a positive experience by accurately replying to their queries goes a long way. Companies should understand that customer retention is much more than the cost incurred in the advertising campaigns. While offering subscription information, companies should keep in mind not to cost their customers anything. Rather, moving subscriptions to social media can reach thousands customers within minutes. Hence social media posts should be updated according to the customers interests. Read more at :

https://www.smartdatacollective.com/customer-feedback-data-analysis-keys-good-customer-retention-rate/

Data is an important aspect of decision making and for valuable insights over the data, BI has emerged crucial in organizations. Some useful ways to manage your data and team are : 1.Invest in a robust BI service like Google Analytics or CoolaData so as to get better insights on behavioral analytics and better collaboration. 2.Break down silos - when organizations go through silo effect and information stops flowing, BI can prevent this through transparency. 3.Teams should acquire data analysis skills and grow as they work. 4.One should relate to the context, not depend solely on the numbers but on the why, how. 5.Decide on how to decide for better data implementation. 6.Drive action through insights, study how trends correlate and act accordingly. Read more at :

http://www.networkworld.com/article/3197753/big-data-business-intelligence/6-ways-to-manage-your-data-and-team.html

 In the recent days, prevention of fraud is a very difficult task and banks should keep in mind some non-financial signals that might prevent a fraud. Fraudsters take advantage of the growing technology where a simple code interception can lead a criminal to hack a person's credentials. It is difficult to suspect any change in contact details as people change phone numbers very frequently and so the bank managers should put this down as a sign of potential threat. Bank managers should have the correct  solutions for these fraudulent actions happening. Fraud managers should have the latest fraud-preventing programs in order to receive the signals of a fraud before hand and can prevent it from causing severe damage. Read more at :

http://banking.com/analysis/non-financial-events-must-play-role-fraud-prevention-program/

Internet has opened up new possibilities for communicating, from emailing and blogging to mobile video chatting. Most businesses now communicate with their customers through multiple online channels. As brands prioritize this digital experience, they often overlook communicating by voice is faster, easier, and more effective than typing messages. According to the advertising and marketing advisory firm BIA/Kelsey, calls to businesses are expected to exceed 169 billion per year by 2020. These calls are 10-15 times more likely to generate a successful sale or follow-up activity than digital form submissions, which means they are more efficient in generating revenue. Voice is central to communication, and because it’s not strictly an online channel, it presents a challenge for brands that treat digital transformation as a matter of generating clicks and website views. The challenge is particularly acute for businesses that operate in categories such as insurance, lending, travel, or healthcare. In these “considered purchase” categories, consumers typically reach out to a brand after conducting preliminary research online, and are thus well qualified to buy. Read more at https://hbr.org/2017/07/your-customers-still-want-to-talk-to-a-human-being

To all voracious readers, here is an ideal list of books to have in bookshelf of every data scientist and analyst. These books cover a wide range of topics and perspective, which would help to become a well-rounded data scientist and analyst. Here are a few:

• R Cookbook by Paul Teetor

• Python for Data Analysis by Wes McKinney 

• Web Analytics: An Hour a Day by Avinash Kaushik 

• Web Analytics 2.0 by Avinash Kaushik 

• How to Measure Social Media: A Step-By-Step Guide to Developing and Assessing Social Media ROI by Nichole Kelly

• The Visual Display of Quantitative Information by Edward R. Tufte

• Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics by Nathan Yau

• The Wall Street Journal Guide to Information Graphics: The Dos & Don’ts of Presenting Data, Facts, & Figures by Dona M. Wong

• Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few

Read more at https://www.analyticsvidhya.com/blog/2015/06/infographic-read-books-analytics-data-science/

Every business wants to improve its customer experiences and they want to turn its ordinary customers into a loyal customer. In order to make a fabulous customer experience there are 4 ways to attract customers and foster lifelong loyalty. 1) Use Technology to provide Top-Notch Customer support- Customer is needed to provide with mobile customer support, live chat, self-service management, social media support and Omni channel support.  2) Create Personalized Experiences-Customers are needed to be treated in the more personalized manner, or by catering messaging, offers and creating more communication. 3) Leverage Big Data to Get to Know your customer on an Individual level- Understanding the customer on different levels- behavioral, contextual, service-interaction ,social and consumption  4) Reward Customer Loyalty to Foster Brand Ambassadorship- Customers are needed to be given loyalty awards through brand leveraging loyalty programs. These all over enhance the customer experience and the business will able to get loyal customers. Read more at: https://www.ngdata.com/ways-to-improve-customer-experience/

Nowadays companies are on the hunt for analytics executives and organizations in the industry have created hundreds of executive roles in analytics. Employers are hunt for leaders who understand the data and threats and opportunities related to it. In order to identify for the analytics initiatives the males and females are divided according to age groups and experiences which lead them to their roles. The executives landed into their current positions by one of their career paths- 1) Linear- It’s an upward movement of the analysis function who has experience in computer experience, statistics and analytics and ability to gain customer attraction 2) Nonlinear- it’s a movement between and within analysis function and are experience in IT, marketing and accounting.  3) Parachute- there is no previous work experience needed in engineering and technology and should have knowledge of project management, telecommunications, mobile systems, project management and security. Read more at: http://www.cio.com/article/3182718/analytics/3-paths-to-analytics-leadership-roles.html

Both terms web analytics and digital analytics are interchangeable. But there is a difference between two of them. When Web analytics association changed its name to the digital analytics association then the word digital analytics came up. During the early days of the internet, Web analytics were analyzing the website data, such as users, visitors, links and many more alike. When other forms of online came like emails, search, social, etc., then a new term called digital analytics came into being where all these channels were analyzed. Now all the online channels have been transformed from web analytics tools to digital analytics tools. Web analytics is the analysis of website data, whereas digital analytics is an analysis of all data from digital channels that includes websites also. But till now web analytics are still searched more than digital analytics according to Google Trends chart. Read more at: http://webanalysis.blogspot.in/#axzz4hutH4lMG

Few challenges being faced in translating the lessons of predicting analytics from other verticals in manufacturing. The objective of this predictive analytics is to get the correct business decisions and it will impact the design and service of the product. The data is being updated continuously through their supply chain. The predictive models are used to connect the real world data to digital twin models of the virtual world. This helps in better understanding and working of their business plus with the on the factory work. Predictive analytics help to find the issues related with the product quality, performance and its features. These helps in better designing the product features and make it to optimum use of it. The predictive model is quite accurate in giving information about the risk failure, improving the machines to put in a better use as well as it gives the best correlation between job characteristics and job failure. Models are being trained through environmental data and IoT data and few factors which affect such data too such as environmental hazards, weather and many more. Its benefit for the business to take predictive analytics into consideration. https://www.ngdata.com/ways-to-improve-customer-experience/

According to authors, innovations in the areas of Artificial Intelligence(AI),Machine Learning and Deep Learning are beginning to dominate the technology landscape but the actual user experience and concerns about privacy and security are obstacles that can prevent AI from reaching its potential. Despite it’s negative implications, a Consumer Intelligence Report by PwC, revealed, that most consumers believe AI will help humankind, by solving complex problems, like fatal diseases, city traffic, cybercrime, infrastructure and fraud, that plague modern societies , thereby helping people to live more fulfilling lives. AI could revolutionize  personalized healthcare by analyzing data thus helping in maximizing life expectancy and enhancing wellbeing.AI will expand access to financial, medical, legal and transportation services to those with lower incomes thus emerging  both as a process and a product that instills trust and transparency in consumers. Read more at https://aitrends.com/security/consumers-welcome-ai-despite-lingering-privacy-concerns/

According to authors, modern-day marvels are the result of machine learning, which are programs that combine through millions of pieces of data and start making correlations and predictions about the world , by using machines that uses cold hard data to make decisions that are sometimes  more accurate than a human’s, thereby reducing biases. Computers don’t hold any inherent biases, as machine  knows only one approach i.e. the objective analysis and are capable of analyzing massive amounts of information, thus having  a distinct advantage over humans. Utilizing the data for machine learning, can uncover contradictory and surprising results by making data more accessible, more understandable and  enabling the organization to achieve a new level of business intelligence, thus empowering decision makers at all levels with a powerful tool. Read more at https://aitrends.com/machine-learning/machine-learning-cure-decision-bias/

According to authors, the implication of big data is the quantity is paramount, the returns generated do not match the quantity of data generated. Experts point out, it is not per se the data that should be big, but the primary factor that counts is the diversity of data, the amount of richness they provide and the focus on accelerating human understanding of data , which has the potential to create output subject to increasing returns. More data retards innovation, the speed of experimentation and iteration. However IT teams helps in bringing order to chaos, in data and analytics, by managing data infrastructure, such as data warehouses and production processes . Data scientists, who’re occupying the space between IT and business consumers , have made enormous strides in getting grip on their data, analyzing and acting on it, thereby avoiding imbalance. Read more at https://aitrends.com/big-data/three-big-data-developments-no-one-is-talking-about/