Why Machine Learning Services are getting Maximum Attention?

Machine learning is one the trending topic these days. Right now, it is a catchword in the field of technology. It defines major representations that how computer can learn in future. Basically Machine learning algorithms are trained with the help of “training set” data.  By using this machine learning algorithm, it gives answer to the questions. For example, in training dataset you will have given pictures of dog to the computer. Some pictures will say, “This is a dog” or some will say, “This is not a dog”. Then you can show a number of new pictures and it would start searching which pictures were of dogs. Every picture which is identify as a correctly or incorrectly gets added to the training set. In this way program efficiently gets “smarter” and better by completing its task over time.

A couple of years back; it was the time of social, mobile, cloud and analytics etc. Although these technologies are still important as they have very good place in digital strategies.  Nowadays big hype is Artificial Intelligence, Internet of Things, Big Data, Machine learning services etc.  It is observed from the various surveys that Artificial Intelligence is the future of Growth.  There are some Artificial Intelligence Consulting companies available which provide number of services in this field. Machine learning is a part of Artificial Intelligence.

Let’s discuss Machine Learning Services which are attention seeker. These services are as follow:

  1. Fraud Detection: In numerous fields, machine learning is much better to spot fraud detection cases. Fraud management has been very aching for the commercial and banking sectors. Due to plethora of payment channel such as credit/debits cards, kiosks, smartphones etc., this menace is increasing day by day. In the similar way, criminals have found out loopholes in these channels. So, it is becoming difficult for the businessman to confirm transactions. However, Data Scientists have been successful in solving this problem with the help of machine learning. For instance, PayPal is using machine learning to fight with these raiders. This company has tools which compare all the transactions and easily distinguish between legitimate and non-legitimate transactions between sellers and customers.
  1. Recommendations: If you are regular user of Netflix and Amazon, then you must be familiar with use of this term. Intelligent algorithms of machine learning are monitoring  your all the activities and compare it with the other number of users and as a result show you which thing you would like to buy. These product recommendation systems are getting smarter every time. Suppose you want to buy light shade jeans of a particular brand, during your search it will also recommend you light shades jeans of some other brands. It will definitely make your shopping better with number of choices.
  1. Natural Language Processing (NLP): NLP is an emerging trend which is almost used in every field. Natural Language with Machine learning algorithms can stand for agents of customer service and in fast way route customers to the information they need. Mostly it is used to translate incomprehensible legalese in contracts into basic language and help prosecutors to handle large volumes of evidence to prepare for a case.
  1. Healthcare: Machine Learning algorithms can practice more information and find out more patterns than the humans can do. Machine learning can recognize risk factors in a better way for sickness in a large population area. A company build Disease Prediction System name “www.AIvaid.com” which based on machine learning algorithm that is capable to diagnose the human health conditions and predict the disease report. There are plenty of symptoms and diseases dataset has been added and working it wonderful. Along with that, Personalized medicine is also one of the effective treatments which relay on the health data of the individual combined with predictive analytics is a latest research and diligently correlated to better disease assessment. For this purpose, supervised learning is used which permits physicians to list out  from more limited sets of diagnoses based on the  symptoms and genetic information.
  1. Smart Cars: IBM recently surveyed top executives and it is concluded that we would see smart cars on road by 2025. It will learn about its owner as well as its environment and integrate it with Internet of Things.  Thus it will automatically adjust its internal setting such as audio, temperature, seat position automatically based on the driver and itself fix problems, drive itself and also give advice about traffic and conditions of the road.

If you want to start your career with Machine Learning, then you can start online and offline course with Dummybyte, here are plenty of course available you can opt with Python.

In the nutshell, we can say that Machine Learning is a buzzword in the world of technology. Machine learning services are wooing more customers due to its smart learning techniques. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers are getting massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. For many organizations, providing machine learning services can be challenging. When machines and human solve problems together and learn from each other, AI full prospective can be achieved.

Advertisements

Why Businesses Should Embrace Machine Learning

If you’re still unsure of machine learning and it’s benefits, consider these scenarios. 

In 2016, Google’s net worth was reported to be $336 billion, and this is largely due to the advanced learning algorithms the company employs.

Google was the first company to realize the importance of incorporating machine learning in business processes. And the technology powerhouse doesn’t stop at any given point; it keeps modifying its algorithms to better suit the needs of its users continuously.

And how does it accomplish the difficult task of observing the browsing pattern of thousands (or millions) of its users?

The answer is simple. By analysing the data, which it has accumulated by introducing machine learning to its business operating model.

This is just one example of how machine learning processes in the recording and processing of data can help businesses grow.

Here are three more ways in which machine learning can help various business sectors:

1. It removes physical restrictions

If we have accomplished one thing by automating and digitalizing business processes, it is that we have removed the physical limitations that restrict growth.

Before the technological age, what was the biggest problem faced by businesses? Operating within a limited space accessible only by a limited number of people. For a designer, it was necessary to completely clear out the previous inventory before utilising the shelf space for new designs. By embracing machine learning and diving into the world of e-commerce, you don’t ever have to worry about running out of shelves.

2. It provides a deeper understanding of your consumers

With the introduction of automated processes, businesses have become increasingly consumer-centric. To be able to survive the competition of catering to your customers’ needs, you as a business owner have to understand the needs of your consumers.

If you do not deliver what consumers are looking for, there is a high probability that you will lose potential customers to competition. Machine learning plays an important part in solving the mystery of consumer preferences. All required information is hidden behind the data accumulated by the business. You just have to crunch the code, and voila—you know what your customers are actively searching for.

3. It automated processes, boosting efficiency

Imagine standing in line in a supermarket; someone with a long haul is standing ahead of you and the cashier’s machine suddenly breaks. It’s a nightmare, right? You could be spending those precious minutes watching your favourite series on Netflix.

Now imagine that the machine is not broken but in fact was never invented. The cashier has to manually enter every purchase and tally it with the existing stock. Incorporating automated processes to record inventory stock and purchase order data is not a luxury, it’s a necessity in today’s world. Machine learning has increased the efficiency of businesses and minimized the room for error.

Want to learn more about how to incorporate Big Data analytics to propel your business towards growth? Visit Simplilearn for useful insights into the subject. For more interesting content about data analytics follow me on Twitter and LinkedIn.


Source: Why Businesses Should Embrace Machine Learning | Ronald van Loon | Pulse | LinkedIn

Artificial Intelligence

Trends Shaping Machine Learning in 2017

Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world of the tech world is Artificial Intelligence (AI). The two technological concepts, Machine Learning and Artificial Intelligence, are often used as interchangeable terms. However, it is important to understand that both technologies supplement each other and are essentially different in terms of their core functions.

It is often predicted by technology enthusiasts and social scientists that human beings in the workforce will soon be replaced by self-learning robots. It is yet to be seen whether there lies any truth in these predictions or not but for 2017, the following trends have been prominent in the development of Machine Learning.

Giant companies will develop Machine Learning based Artificial Intelligence systems

In 2016, we saw many prominent developments in the domain of Machine Learning, and numerous artificial intelligence applications found a way to our phone screens and caught our attention. In the previous year, companies just touched the tip of the iceberg and in 2017, we will continue to see more developments in the field of machine learning. Big names such as Amazon, Google, Facebook and IBM are already fighting a development war. Google and Amazon launched successful apps, which include Amazon Echo and Google Home, at the beginning of the year, and we have yet to see what these tech giants have in stores for their customers.

Algorithm Economy will be on the Rise

Businesses greatly value data to take the appropriate actions, whether it is to understand the consumer demand or comprehend a company’s financial standing. However, it is not the data alone they should value because without an appropriate algorithm, that data is worth nothing. Peter Sondergaard, Senior Vice President of Gartner Research, says that, “Data is inherently dumb and the real value lies in the algorithms which deduce meaningful results from a cluster of meaningless data”.

Algorithm Economy has taken center stage for the past couple of years, and the trend is expected to follow as we expect to see further developments in machine learning tools. The use of algorithm economy will distinguish small players from the market dominators in 2017. Small businesses that have just entered the transitional phase of embedding machine learning processes in their business models will be using canned algorithms in tools such as BI, CRM and predictive analysis. On the contrary, large enterprises will use proprietary ML algorithms.

Expect more Interaction between Machine and Humans

Google Home and Amazon Echo received an exceedingly positive response from the audience which made it evident that consumers perceive human-machine interaction positively. Innovative technologies embedded with machine learning processes prove to be helpful under various circumstances; for example, helping people with eyesight issues to navigate. But will they completely replace human-human interaction? Maybe 25 years down the road, but we do not see that happening anytime soon. Machine learning has made it increasingly possible for machines to learn new skills, such as to sort, analyze and comprehend. But nevertheless, there are certain limitations to it. Automated cars have frequently been tested, and even with modified algorithms and advanced technologies, the chance of an error is still present. This example alone is enough to convince that machines will not completely replace humans, at least not anytime soon.

Conclusion

Machine Learning and Artificial Intelligence is a promising field with much potential for growth. We have seen some recent developments in the sector which, not long ago, people believed were not possible. Therefore, we cannot give a definite verdict regarding the industry’s potential for growth. However for now, intelligent machines are only capable of handling the repetitive tasks and can follow a predetermined pattern. It lacks the skill to figure out things which are out of the ordinary, and we still require human intervention for keeping the chaos at bay in such situations.

Ronald van Loon is Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He will contribute his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read Ronald van Loon future posts then please click ‘Follow‘ and feel free to also connect on LinkedIn and Twitter to learn more about the possibilities of Big Data .

This article was originally posted on SimpliLearn

Source: Trends Shaping Machine Learning in 2017 | Ronald van Loon | Pulse | LinkedIn

Securing Competitive Advantage with Machine Learning | The HR Tech Weekly®

Securing Competitive Advantage with Machine Learning

How to Secure Your Competitive Advantage with Machine Learning | The HR Tech Weekly®

Business dynamics are evolving with every passing second. There is no doubt that the competition in today’s business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages.

Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Every idea that has completely transformed our lives was initially met with criticism. Acceptance is always followed by skepticism, and only when the idea becomes reality does the mainstream truly accept it. At first, data integration, data visualization and data analytics were no different.

Incorporating data structures into business processes to reach a valuable conclusion is not a new practice. The methods, however, have continuously improved. Initially, such data was only available to the government, where they used it to make defense strategies. Ever heard of Enigma?

In the modern day, continuous development and improvement in data structures, along with the introduction of open source cloud-based platforms, has made it possible for everyone to access data. The commercialization of data has minimized public criticism and skepticism.

Companies now realize that data is knowledge and knowledge is power. Data is probably the most important asset a company owns. Businesses go to great lengths to obtain more information, improve the processes of data analytics and protect that data from potential theft. This is because nearly anything about a business can be revealed by crunching the right data.

It is impossible to reap the maximum benefit from data integration without incorporating the right kind of data structure. The foundation of a data-driven organization is laid on four pillars. It becomes increasingly difficult for any organization to thrive if it lacks any of the following features.

Here are the four key elements of a comprehensive data management system:

  • Hybrid data management
  • Unified governance
  • Data science and machine learning
  • Data analytics and visualization

Hybrid data management refers to the accessibility and repeated usage of the data. The primary step for incorporating a data-driven structure in your organization is to ensure that the data is available. Then you proceed by bringing all the departments within the business on board. The primary data structure unifies all the individual departments in a company and streamlines the flow of information between those departments.

If there is a communication gap between the departments, it will hinder the flow of information. Mismanagement of communication will result in chaos and havoc instead of increasing the efficiency of business operations.

Initially, strict rules and regulations governed data and restricted people from accessing it. The new form of data governance makes data accessible, but it also ensures security and protection. You can learn more about the new European Union General Data Protection Regulation (GDPR) law and unified data governance over here in Rob Thomas’ GDPR session.

The other two aspects of data management are concerned with data engineering. A spreadsheet full of numbers is of no use if it cannot be tailored to deduce some useful insights about business operations. This requires analytical skills to filter out irrelevant information. There are various visualization technologies that make it possible and easier for people to handle and comprehend data.

Want to learn more about the topic? Watch replay of the live session with Hilary Mason, Dez Blanchfield, Rob Thomas, Kate Silverton, Seth Dobrin and Marc Altshuller.

Follow me on Twitter and LinkedIn for more interesting updates about machine learning and data integration.


Source: Securing Competitive Advantage with Machine Learning | Ronald van Loon | Pulse | LinkedIn

5 Machine Learning Startups To Improve Your Recruiting Workflow

5 Machine Learning Startups To Improve Your Recruiting Workflow

Screen Shot 2017-06-14 at 7.20.58 PM
This list was originally published on Product Hunt here. Below is an abbreviated version.

Sam DeBrule co-founder of Journal and voice of the Machine Learnings Newsletter has curated a list of top startups using Machine Learning to automate work-related tasks. I’ve pared this down to my favorites for simplifying recruiting and team building efforts.

1. Textio | Spell checker for gender bias and more

Job descriptions are often vague and unintentionally biased, which affects the quality and diversity of applicants applying to your jobs. By generating insights from your job posts, Textio teaches you how to better message an open job role in a way that is both non-discriminatory and eye-catching to applicants.

2. Slack | Real-time messaging, archiving & search

Slack facilitates quick, real-time communication using ML-powered search, allowing you to chat with your team and candidates without the lag-time between emails. It automates many internal status updates and meetings regarding candidates as they move through the pipeline. Additionally, with hundreds of groups, it’s a great place to source candidates and learn tactics and best practices from other recruiters.

3. Wade & Wendy | AI chatbot for engaging & interviewing candidates

Wade & Wendy has developed an Applicant Experience Chatbot, Wendy. She serves as a first-round interviewer and candidate engagement tool. By chatting with applicants at the top of the funnel, recruiters and hiring teams can spend more time building relationships with candidates and sourcing hard-to-fill positions.

Disclaimer: I work at Wade & Wendy! 😎

4. Grammarly | Clear, effective, mistake-free writing everywhere you type

With nearly 1 in 3 employees searching for new opportunities, many often communicate with recruiters when a few spare minutes arise while at their current job. When candidates have a small window of time, recruiters need to move fast with their communication. Grammarly is a seamless way to side-step embarrassing typos when quickly emailing (or Slack-ing) back and forth.

5. X.aiAn AI personal assistant who schedules meetings for you

Between, texting, calling, emailing and messaging candidates, it’s tough to keep your calendar straight. X.ai uses AI scheduling assistants to automate this process. Cc’ing Amy to emails eliminates the time-consuming task of scheduling phone calls, interviews and coffees with candidates.

Any other tools keeping your recruiting efforts on track? Drop them in the comments section.👇

About the Author:

Bailey Newlan is the Content & Growth Marketer at Wade & Wendy, a New York City-based startup on a mission to make hiring more human. Wade & Wendy is a conversational engagement platform for recruitment automation. To connect, reach out to Bailey via LinkedIn, Twitter or Medium and join the private beta list.


If you want to share this article the reference to Bailey NewlanWade & Wendy and The HR Tech Weekly® is obligatory.

Current and future state of HR and employee appreciation – Interview with William Tincup

Written by João Duarte, Content Director at Tap My Back.

WT-thin--football-clubs1

William is the President of RecruitingDaily. At the intersection of HR and technology, he’s a Writer, Speaker, Advisor, Consultant, Investor, Storyteller & Teacher. He’s been writing about HR related issues for over a decade. William serves on the Board of Advisors / Board of Directors for 18 HR technology startups. Many say his words dictates and predicts the future of managing people and teams.

Tap My Back, an employee appraisal software, recently managed to have an interview with Tincup about the current and future state of HR focusing topics such as performance reviews and the use of AI. This article is sort of a compilation of the main ideas he went through on this interview.

One of the most interesting topics Tincup spoke was about the way he feels HR managers currently should have more responsibilities than ever before. Following his thoughts we’re moving from era where employee engagement was the main worry of HR managers onto one where there’s the need to manage the full experience staff go through on the workplace.

He even says that engagement is the same as recycling, everyone already recognizes the value it provides but still many prefer to ignore it.

According to William, the reason why performance reviews stopped producing the outcome they used to is related with the fact that many times managers who conduct those are not honest with the employees about whose interest this process serves. As society currently values highly aspects such as transparency, HR staff conducting performance reviews should be clear to people and say something “hey, this actually for us, so that we do better, so that we make sure that we’re on the right track and we get the most out of you because we want the best version of you while you’re with us. We’re going to train you, we’re going to help you, we’re going to throw some stuff in but at the end of the day we want the best version of you while you’re with us”.

Regarding AI and Machine Learning, William provides an interesting opinion, stating that these tools will make insights that used to be remarkable to become commonplace, a commodity. Following his reasoning these tools will turn dump databases into something capable of providing insightful conclusions, sparing human brain of analyzing raw data.

William, with his typical charismatic way of being, finishes the interview with an advice for every entrepreneur, “Grow, comma grow the right way” referring to the fact that the ambition to grow should never overlap the way managers treat people

End note: You can hear and read the full interview here.


If you want to share this article the reference to João DuarteTap My Back and The HR Tech Weekly® is obligatory.

How Machine Learning is Revolutionizing Digital Enterprises

How Machine Learning is Revolutionizing Digital Enterprises

According to the prediction of IDC Futurescapes, two-thirds of Global 2000 Enterprises CEOs will center their corporate strategy on digital transformation. A major part of the strategy should include machine-learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model today.

If you want to stay ahead of the game, then you cannot afford to wait for that to happen. Your digital business needs to move towards automation now while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.

In short, ML will connect intelligently people, business and things. It will enable completely new interaction scenarios between customers and companies and eventually allow a true intelligent enterprise. To realize the applications that are possible due to ML fully, we need to build a modern business environment. However, this will only be achieved, if businesses can understand the distinction between Artificial Intelligence (AI) and Machine Learning (ML).

Understanding the Distinction Between ML and AI

Machines that could fully replicate or even surpass all humans’ cognitive functions are still a dream of Science Fiction stories, Machine Learning is the reality behind AI and it is available today. ML mimics how the human cognitive system functions and solves problems based on that functioning. It can analyze data that is beyond human capabilities. The ML data analysis is based on the patterns it can identity in Big Data. It can make UX immersive and efficient while also being able to respond with human-like emotions. By learning from data instead of being programmed explicitly, computers can now deal with challenges previously reserved to the human. They now beat us at games like chess, go and poker; they can recognize images more accurately, transcribe spoken words more precisely, and are capable of translating over a hundred languages.

ML Technology and Applications for Life and Business

In order for us to comprehend the range of applications that will be possible due to ML technology, let us look at some examples available currently:

  • Amazon Echo, Google Home:
  • Digital assistants: Apple’s Siri, SAP’s upcoming Copilot

Both types of devices provide an interactive experience for the users due to Natural Language Processing technology. With ML in the picture, this experience might be taken to new heights, i.e., chatbots. Initially, they will be a part of the apps mentioned above but it is predicted that they could make text and GUI interfaces obsolete!

ML technology does not force the user to learn how it can be operated but adapts itself to the user. It will become much more than give birth to a new interface; it will lead to the formation of enterprise AI.

The limitless ways in which ML can be applied include provision of completely customized healthcare. It will be able to anticipate the customer’s needs due to their shopping history. It can make it possible for the HR to recruit the right candidate for each job without bias and automate payments in the finance sector.

Unprecedented Business Benefits via ML

Business processes will become automated and evolve with the increasing use of ML due to the benefits associated with it. Customers can use the technology to pick the best results and thus, reach decisions faster. As the business environment changes, so will the advanced machines as they constantly update and adapt themselves. ML will also help businesses arrive on innovations and keep growing by providing the right kind of business products/services and basing their decisions on a business model with the best outcome.

ML technology is able to develop insights that are beyond human capabilities based on the patterns it derives from Big Data. As a result, businesses would be able to act at the right time and take advantage of sales opportunities, converting them into closed deals. With the whole operation optimized and automated, the rate at which a business grows will accelerate. Moreover, the business process will achieve more at a lesser cost. ML will lead businesses into environs with minimal human error and stronger cybersecurity.

ML Use Cases

The following three examples show how ML can be applied to an enterprise model that utilizes Natural Language Processing:

  • Support Ticket Classification

Consider the case where tickets from different media channels (email, social websites etc.) needs to be forwarded to the right specialist for the topic. The immense volume of support tickets makes the task lengthy and time consuming. If ML were to be applied to this situation, it could be useful in classifying them into different categories.

API and micro-service integration could mean that the ticket could be automatically categorized. If the number of correctly categorized tickets is high enough, a ML algorithm can route the ticket directly to the next service agent without the need of a support agent.

  •  Recruiting

The job of prioritizing incoming applications for positions with hundreds of applicants can also be slow and time consuming. If automated via ML, the HR can let the machine predict candidate suitability by providing it with a job description and the candidate’s CV. A definite pattern would be visible in the CVs of suitable candidates, such as the right length, experience, absence of typos, etc. Automation of the process will be more likely to provide the right candidate for the job.

  • Marketing 

ML will help build logo and brand recognition for businesses in the following two ways:

  1. With the use of a brand intelligence app, the identification of logos in event sponsorship videos or TV can lead to marketing ROI calculations.
  2. Stay up to date on the customer’s transactions and use that behavior to predict how to maintain customer loyalty and find the best way to retain them.

How Enterprises Can Get Started Implementing Machine Learning

Businesses can step into the new age of ML and begin implementing the technique by letting the machines use Big Data derived from various sources, e.g. images, documents, IoT devices etc to learn. While these machines can automate lengthy and repetitive tasks, they can also be used to predict the outcome for new data. The first step in implementation of ML for a business should be to educate themselves about its nature and the range of its applications. A free openSAP course can help make that possible.

Another step that can bring a business closer to ML implementation is data preparation in complex landscapes. The era of information silos is over and there is an imperative need for businesses to gather data from various sources, such as customers, partners, and suppliers. The algorithms must then be provided open access to that data so they can learn and evolve. The Chief Data Officer of the company can oversee the ML integration process.

To start with completely new use cases for Machine Learning is not easy and requires a good understanding of the subject and having the right level of expertise in the company. A better starting point for many companies would be to rely on ML solutions already integrated into standard software. By that it will connect seamless with the existing business process and immediately start to create value.

Lastly, businesses should start gathering the components necessary for building AI products. Among the requirements would be a cloud platform capable of handling high data volume that is derived from multiple sources. The relevant people are as important to this step as are the technology and processes. After all, they would be the ones who will be testing the latest digital and ML technologies.

If you want more information on SAP Machine Learning, then go here to subscribe to the webinar on Enabling the intelligent Enterprise with Machine Learning.

The presenters include Dr. Markus Noga: VP Machine Learning Innovation Center Network, SAP SE. You can follow him on Twitter. Ronald van Loon is the other presenter for the webinar. Mr. van Loon is counted among the Top 10 Big Data expert and is an IoT Influencer. You can also follow him on Twitter.


Source: How Machine Learning is Revolutionizing Digital Enterprises | Ronald van Loon | Pulse | LinkedIn

3 Ways HR Will Evolve in the Future

3 Ways HR Will Evolve in the Future

Believe it or not, automation is changing our entire lives, the way we live, think and work. As quoted by Mr. Abhijit Bhaduri, author of Digital Tsunami, “Humans resist change, machines don’t.” We are at a age where we no longer can nor should resist change. That said, technology is also massively impacting the HR functions. Most of the traditional support functions of HR, such as payroll, attendance etc. are being automated. Adding to the progression, chatbots are further driving more engagement with its personalized attributes, and is further adding up to redefining the HR role.

Ripples in the Water

One might wonder, will the rapid pace of digitization re-define HR? Of course yes, with millennials making up more than half of the current workforce — and predicted to make up 75 percent by 2020 — HR has to embrace technologies to keep at par with employee and business demands.

The Effect of Big Data

A lot of work in HR used to be related to adherence to compliances and therefore, huge amount of work related to paperworks was involved. But, now things have changed. Online portals and platforms provide HR with all the information that they need. Today’s technology gives HR professionals access to the power of Big Data and changes the way businesses understand their customers, build their own brands and communicate to prospective employees.

One of the boons of Big Data is Predictive Analytics. In big corporations, it is very difficult to keep a track of each employee. Predictive analytics enables HR to understand which employee needs an additional training.

High Up in the Clouds

Another technology which is impacting HR in a big way. Gathering and storing of information has always been a major function of the HR department, and the stack of files not only waste office space but are very difficult to trace as well. Can you even imagine, a millennial, who is always glued to his smartphone will have the patience to go through all the piles of paper?

High Up in the Clouds

Thanks to cloud technology, all of this information can instead be stored in the cloud. No longer does an employee need to tick the boxes while filling up a feedback form which again runs the risk of getting lost. All the employee information like tax documents, payroll, feedback etc can be stored online securely.

Cloud-based systems and Big Data go hand in hand. With Machine Learning emerging steadily, all these data will make a lot of sense few years down the line, it all depends how well can one derive relevant information out of it.

Chat with the Bots

There are some information which are very subjective in nature, like how to fill the Form 19 or file for the income tax returns. It makes no sense if the employee walks up to the HR managers for day-to-day queries or any concerns they might have regarding their pay, leaves, performance etc. To narrow down the gap of communication between the employees and the HR, PeopleStrong recently launched India’s first HR chatbot ‘Jinie’. From a transactional interface with employees to a conversational interface, Jinie the India’s first HR Chatbot will be able to provide the next level of experience to its employees.

In the era of smartphones, this will be a great boost in employee engagement.

These are few of the many ways in which the HR domain will change and adapt itself to digitization. With the burden of a lot of paperwork gone from the shoulders and with new data in hand, HR department will be fully equipped to make the employees life much easier and will add more value in business.


If you want to share this article the reference to Bangabdi Roy Chowdhury and The HR Tech Weekly® is obligatory.

Emerging Talent: The Trends, Challenges and Opportunities at TLCon

Emerging Talent. Our Speakers

Emerging Talent is not void of the many changes taken place over the past year. Plugging the skill gaps with EU workers is under threat from Brexit however does the apprenticeships levy pose to fix that issue? Are graduate schemes at risk? As young people look for more than just a good salary from work, is retention becoming more difficult? Can we do anything about it?

On the 27th April, talentleadersconnect. will be hosting for the second time, TLCon: Emerging Talent that will give 70 Head of early careers, HRD’s, graduate recruitment, apprenticeships and talent acquisition professionals the opportunity to learn, share and network around a theme that is getting more and more important each year. The agenda will have case studies, research and thought leadership from the likes of L’Oreal, Cognizant, Centrica, LaunchPad, The Chemistry Group plus more.

We’ll be starting off the day with research from the Graduate Recruitment team from L’Oreal on the graduate talent population and their expectations and career priorities. Within this same event, Bright Network have carried out research of their own on this topic which should provide a good comparison of the results. Furthermore they’ve been implementing some really great initiatives to gamify how they attract and retain candidates. They’ve told us a little bit on this and we were stoked at the ideas. We’re sure you will be too once you hear it!

We then dive deep into AI and Machine Learning. Don’t worry, there won’t be a whole bunch of code put on slides however we will be looking at an overview of the trends and how this affects your work in recruitment and HR. Will Hamilton from LaunchPad will guide us through the latest innovations in what will be a very futuristic presentation.

We continue to trailblaze into the future by looking at Apprenticeships in the New World. It’s no doubt, there’s a lot of division between viewing the apprenticeship levy as a tax or investment and Erica Farmer, Apprenticeships and L&D programme lead for Centrica is best placed to fill us in on the benefits; she represents Centrica at the National Apprenticeships Service’s lead employer Apprenticeship Ambassador Network.

After our time travel into the future, we go back to student recruitment 101. You may be excited about all the new tools and changes that will affect how you go about graduate recruitment but “You can’t harvest fruit from the trees you haven’t planted yet.” Brian Sinclair, Head of Student Recruitment for Cognizant will give you practical advice on everything, from requirements gathering to pipeline reporting with some useful templates and tools to help explain and position best practice with key internal stakeholders.

There’ll be a buffet lunch and plenty of time to network with your peers around all these topics so join us on the 27th April with your complimentary ticket at TLCon: Emerging Talent.

Useful Information:

Date: 27th April 2017, 8:15am to 1:30pm

Venue: Foyles Bookstore, 107 Charing Cross Road, WC2H 0DT

Theme: Emerging Talent

Contacts: Edie Kalman, Events Manager, edie@talentleadersconnect.com

Twitter: @TLCon_

Hashtag: #TLCon

talentleadersconnect. is the largest Talent Acquisition & HR event series in the UK & Europe. The events combine industry leading keynote talks, interactive discussion sessions and relaxed social networking opportunities.

Enhance Engagement and Retention with People Analytics

Enhance Engagement and Retention with People Analytics

Employee Group

An organization that provides top wages and benefits loses a great employee to a competitor for no apparent reason. We can’t stop employees from leaving unless we have a plan to make them stay.

“Retention is the single most important thing for growth” – Alex Shultz (VP Growth, Facebook)

What is the biggest and most intractable restraint to growth faced by companies doing business today? For many organizations, it’s the lack of appropriate talent. The reason: As more organizations have expanded their operations, the need for talent has skyrocketed. But there isn’t enough skilled labor to fill the demand. As a result, one risks losing the talent to other organizations. And with so many companies drawing on a limited talent pool, the competition is fierce.

Glassdoor’s statistical analysis reveals top three factors that matter most for employee retention.

  • Company culture
  • Employee salary
  • Stagnating for long periods of time in the same job

By examining the survey responses of more than 100,000 employees in numerous organizations, Gallup also discovered common themes among the reasons employees chose to remain with a company or to leave it. The reasons employees chose to stay with a company included the following:

  • I feel my job is important to the company.
  • My supervisor cares about me and gives me regular feedback.
  • I know my job expectations.
  • My opinions count.
  • I have opportunity to do my best work every day.
  • My career development is encouraged.

All the above reasons are part of what is often known is “engagement”. Organizations, or teams with high levels of employee engagement score high in most if not all of these. Higher engagement levels not only significantly affect employee retention, productivity and loyalty, but are also a key link to customer satisfaction, company reputation and overall stakeholder value.

OWEN Analytics, who is are providing AI-based people solutions have developed a robust and comprehensive methodology to measure and enhance retention. They run quick pulse surveys that are a combination of “ME” questions (My opinions count), and “WE” questions (I would like to appreciate the following individuals for helping me in my day-to-day work). Open feedback questions are interspersed as well to understand sentiment and key issues.

This helps understand engagement drivers not only from an individual employee perspective, but also from a team dynamics perspective. After all, our engagement with the organization is actually our engagement with the people in the organization – hence understanding those relationships is critical in better understanding attrition. This is the science of ONA (Organization Network Analysis). The example below illustrates how ONA can be used to understand team dynamics in a pharmaceutical sales organization.

01

02

Clearly, the more cohesive teams have better performance and lower attrition.

Now that we have looked at engagement comprehensively, we need to look at what other factors drive employee turnover, as shown below:

03

As per Deloitte, moving beyond the analysis of employee engagement and retention, analytics and AI have come together, giving companies a much more detailed view of management and operational issues to improve operational performance.

Exploring People Analytics

People Analytics, a discipline that started as a small technical group that analyzed engagement and retention, has now gone mainstream as per Deloitte. Organizations are redesigning their technical analytics groups to build out digitally powered enterprise analytics solutions.

OWEN Analytics specializes in helping organizations improve retention using AI driven techniques. As per OWEN, “Machine learning predictions can be sufficiently accurate and thus very effective in enabling targeted interventions for retaining high risk employees. However, using such techniques requires significant expertise in developing predictive models and experience in interpreting the outputs.

HR leaders and aspiring analysts needn’t be disheartened though. One can start with some very simple analyses using nothing more than basic Excel and develop reasonably good retention strategies” Read their blog here: Manage attrition using simple analytics.

OWEN uses a systematic retention approach to understand, predict and drive necessary actions.

04

Predictive models are developed using various Machine Learning algorithms (e.g. Decision Trees, Random Forests, Logistic Regression, Support Vector Machines and Artificial Neural Networks) and best fit algorithm based on the accuracy and business context selected to predict flight risk.

Once the predictions are drivers are available, simple action planning templates to develop and track interventions are used to retain high potential employees.

Retention Challenge

The retention challenge is the result of increasing job mobility in the global knowledge economy where workers average six employers over the course of a career, coupled with the baby boomer retirement “brain drain” and a smaller generation of workers entering their prime working age during this time. It is occurring in all types of organizations across all management levels. This study empirically investigates whether the impact of an organization’s strategic orientation toward knowledge management, the learning culture it supports, and specific human resource practices impact knowledge worker retention and organization performance.

The Eight Elements of the High-Retention Organization as per SAS Institute

  • Clear Sense of Direction and Purpose
  • Caring Management
  • Flexible Benefits and Schedule Adapted to the Needs of the Individual
  • Open Communication
  • A Charged Work Environment
  • Performance Management
  • Recognition and Reward
  • Training and Development

As per Asia – Pacific Journal of Research, preventing turnover is a wise step to implement because it saves money, time, and effort. The company should spend a considerable effort and time to prevent turnover. It is better for an organization to keep experienced and productive employees than to hire new ones. It should invest in its employees through training programs, creating a good hiring process, and engrain them with strong organizational vision. To effectively solve turnover problems, every company needs to address the causes of the turnover. The causes of turnover might not be the same for every company. Below are the most common and affecting factors for preventing turnover.

It’s no more a secret that People Analytics plays a vital role for organizations in dealing with challenges of employee engagement and retention.

About the Authors:

Soumyasanto Sen — Blogger, Speaker and Evangelist in HR Technologies. Engaging with OWEN Analytics.

Professional Advisor, Consultant, Investor in HR Tech. Having 12+ years of experience focusing on Strategies, People Analytics, Cloud, UX, Security, Integration and Entrepreneurship in Digital HR Transformation.

Tej Mehta — Founder & CEO of OWEN Analytics.

Entrepreneur, advisor, student of social sciences. Founded i-Cube as an intersection of analytics and social sciences. Previously, as Vice President with Seabury Group, led strategy and operational transformation programs across several clients in the airline and aerospace industries. Aeronautical engineer, MBA from University of Southern California.


If you want to share this article the reference to Soumyasanto SenTej Mehta and The HR Tech Weekly® is obligatory.