There are plenty of payroll solutions available in the marketing today. While deciding the payroll software companies look for low cost solution as they think that payroll software is a commodity with hundreds of payroll companies providing almost similar functionality hence the lower cost alternative is mostly preferred.
With the advent of emerging technologies like Data Science, Block Chain, Machine Learning that falls with in the ambit of Artificial Intelligence, the dynamics is going to change fast. In near future AI is going to become a very crucial assistant to the HR decision making and since AI is data hungry the choice of payroll solution will become critical. A low cost payroll solution might have solved the short time problem by providing an affordable solution but with these solutions unlikely to get around the AI anytime soon, companies will find themselves wanting while others using AI enabled Payroll solutions will be able to use the payroll data for critical decision making.
AI can help companies improve employee retention using Predictive Analytics and Payroll data forms a very crucial input for the effectiveness and accuracy of such predictive analytics.
It’s high time that companies start taking their payroll more seriously and look at it beyond just a salary processing tool. The data in the payroll is gold mine that the companies can utilize when embracing the emerging technologies.
You might have heard this phrase multiple times now that “Data is the new oil”. What it means is that data has become as precious as Oil was until a few years ago. Many wars were fought over Oil. Many economies used to (and still) run on Oil. Most of the current political problems revolve around Oil.
Compare Oil & Data
Data & Oil also have lots of differences. For instance Data is available in abundance whereas Oil is increasingly becoming scarce. Data is man-made and is increasing by the day and Oil is natural resource diminishing by the day. We can run out Oil but never run out of Data. Having said that there are a few similarities that call for such a comparison valid.
Dependence on Oil v/s Dependence on Data: This seems to be very similar. Economies now increasingly depend on Data to function and prosper as it has been dependent on the Oil for a long time.
Monopoly: Oil monopoly with a handful companies and a few countries that have the access to this natural resource is well understood. Possession of huge amount of Data with a handful of companies like Microsoft, Apple, Facebook, Amazon, and Google (also now called as the big 5) is giving them an unfair advantage over the competition
Oil leak v/s data leak. Both can cause significant damage. Oil leak can damage environment and economy whereas Data leak can cause damage to economy & even topple Governments (wikiLeaks has been able to stir many controversies by selectively leaking Data). Recent US Elections and current US administration is increasingly affected by the leaks.
Cause of Conflict: Oil has been the cause of various wars and Data is now becoming the tool for cyber warfare.
Data Hacks in US
Last year there were three separate instances of Data hacks in the US. The first one was the US personnel Data that hacked. The second was the medical Data (patient records, their ailments and the severity of their medical illness), and finally the banking Data (with account balance, income, and expenses).
Based on the investigations done by the cyber intelligence department of US, they found that three hacks were very well planned and internally sponsored by some hostile nations.
The motive was to get hold of critical classified information from the US. Earlier getting hold of classified or top secret information involved use of spying agencies who would try to find a mole in the government and try to obtain these secrets. This is something that most countries do to their enemy nations. This is risky, costly, time consuming and not as effective means to get hold of such classified documents.
Data if used properly can be a very potent weapon. This is what was proved in the series of cyber-attacks on the US systems. The motto was to get the classified information through a mole.
How they plotted was the first attack was done to get a list of people in the US with high level of security clearance specifically towards their objective. Once they were able to hack and obtain the HR personnel data which had the security clearance on record, they had the list of people who had the information. Their next target was to find who could be most vulnerable amongst those people.
The second attack was to get the medical data which revealed the health records containing information related to specifically the one with serious medical conditions. They were then able to cross reference the data and find how many had such serious ailments or had close relatives with such conditions. Now they were able to narrow down people who high security clearance and have a health crisis. The cost of healthcare in the US is very high and those suffering from serious and long term ailment end up spending a large amount of their earnings and savings towards the treatment.
The third and the final attack was done to identify the financial conditions of those with high security clearance and serious/long term medical conditions that needed expensive treatment.
Volla! They found some matches. Now this information could have been given to their agencies who contacted these potential targets to see if they could be bribed. They can easily offer an attractive sum of money to these people who had the means and who had the need and who were most vulnerable. Most effective way to find a mole and the mission accomplished. So quick, Inexpensive, Least risky and most effective. Used data as a weapon.
This is how effectively the Data can be used as a tool for proxy war. Today many countries including US, Russia, China, North Korea and Japan are preparing for the cyber warfare. Since most of the weapons are controlled through technology and by hacking the enemy systems it easiest way to paralyse and defeat the enemy. One can render the enemy missiles useless, ground their fighter planes that rely on the guidance systems if their systems are compromised. Cyber security has therefore become of the key areas of focus for the defence segments and is increasingly being strengthened.
Data Used by Companies
Data is also used by companies to compete against each other. Data has become so popular that companies have started employing statistical and qualitative analysis and predictive modelling as primary element of competition over the traditional factors.
Traditional Factors were pricing, branding, features, offers & discounts. New analytics & predictive modelling allows companies to use the existing data to identify key patterns and then apply those patterns to predict possible outcome of a strategy. For example, launching a new cologne or a new flavor based on the acceptability and taste of the target audience captured using their purchasing history, their browsing pattern and surveys/feedback on their usage experience.
Data is currently being exchanged between companies like Facebook, Google & Amazon who then offer this information to the companies trying to understand the preferences of the consumers. If you navigate to Facebook posts or search for a particular term using google or show interest in a particular product then these companies capture that information and are able to correlate your personal information with your preference and then apply these data analysis and prediction models to understand your behavioural patterns and your personal preferences.
So much information is being captured these days as every online activity (even offline activity) we do can be stored and then reused for further analysis. You go to amazon to search for any product, compare a few products and even if you don’t buy anything, amazon has got lot of new information about you and your tastes, your likes, your desires & your plans. This information can be further used to create customised offers. Everywhere you browse, you will be reminded of these offers. This is called re-marketing and is a very important tool used by the companies which is primarily based on your browsing history. Even if you don’t buy any product or services, simple search combined with your profile can be used to understand your demography and your choices.
Facebook might appear to be a social networking site where people share their experiences like places they visit, places where they like to eat and places they live. While to most this appears a network site to connect friends, for corporate like Facebook this much more than social networking. Facebook has for a long time been collecting the travel preferences and customer experience of their subscribers and has enough data now to become the most effective travel portal in the world. They possess the information that most travel portals don’t have. For instance, the best places to stay, most popular places to visit, most common activities you can do, places to avoid, things to do and so on. This is very precious information for any traveller and Facebook has it all. It can now use this information and provide useful data to the travel portals who can then design customised packages suited for individuals based on their browsing history on Facebook.
Try going to any travel portal search for a particular flight regularly for a few days. Over time you might get a different (may be higher) price. Through browser cookies these portals can easily collect information about your how frequently are you looking at a particular flight. If they can conclude that you have a concrete plan for a particular date range they might not give you a special deal which might be available to others. Have you ever noticed a deal on a flight that was visible for a few times and then disappears and only to reappear after a while? This is because of targeted pricing which is now being employed by companies. They use the information collected from your browsing pattern to decide whether you are likely to purchase or just checking prices.
When you install any app on your phone you are providing more refined Data about yourselves. Now they can easily get Data related to your friends. You might have seen companies like TrueCaller who displays the ID of the callers whenever your phone rings. TrueCaller has enough Data to not only give you name of the contact, it now holds details about your calling pattern, your close friends, associates, people that you most often interact with, last time when someone was active and even tell you if someone was already busy on phone without dialling their number.
One of the major reasons why Facebook bought WhatsApp was to collect this particular information as it can now let suggest and offer you to connect with friends that you might know. This is the information that they have now obtained from WhatsApp and your phone contacts and now they are able to share this data with Facebook and provide you with more desirable and personalized experience.
Data & Artificial Intelligence
The more comfortable you get with these systems, the more you are likely to interact and the more you interact, the more Data you end up sharing with these companies. The more Data these companies have the smarter their offerings can become using the machine learning and Artificial Intelligence.
When we talk about artificial intelligence there are some of the common applications that come to my mind. First is the Google Map. I am sure most of you have used google map at some point of time. Did you know that the more you use Google Map the more intelligence it has become as it is recording your travel coordinates? The time it is taking to travel from one place to another. It can based on the user experience also recommend the approximate travel time at any given point of time and their recommended time is factoring in your travel speed, the traffic and the time of the day. Google maps is now able to offer you alternative routes once you are about to stuck in traffic and its accuracy is improving by the day as the technology used to run the Google Map feasts on data. The more Data it gets the better the accuracy of the application.
Another example is Amazon Alexa, google home, IBM Watson or Apple Siri. These are bots that are designed to interact with humans. They are designed to understand the language and interpret the intent of the discussion. Based on the intent they are able to deliver a response to give a human like experience. Over the time these platforms have become better as they use Natural Language Processing and Machine Learning. The more it interacts the better it gets. Think of it has a baby that is just learning how to communicate. Baby is listening to every word that we speak in front of them. They use their cognitive learning skills to remember these words and then as they grow they are able understand and speak the language. Similarly these bots are learning every word in the dictionary. Some of these bots are now providing platforms to corporate who can use the NLP (Natural Language Processing) and ML (Machine Learning) and replace bots with the customer service representative.
Capital One bank is the one of the biggest bank in the US. They were evaluating whether they should implement a bot to handle customer services. Based on their data of the user queries they found that the most common query asked by their customer was “What time does the bank open”. This is one simple question for which they don’t need to have an expensive call centre representative answering the phone. This can easily be handled by a bot. So the phone banking can be handled to answer these common queries through these bots. The interaction is further stored to make this experience even better.
One of the reasons we make this comparison between Oil and Data is the emergence of big companies around both oil and data.
Big Oil or Super majors, a name commonly used to describe the world’s six or seven largest publicly owned oil and gas companies. These companies have to a great extent monopolised the Oil industry.
Now similar concerns are being raised by the giants that deal in data, the oil of the digital era. These companies are Google, Amazon, Apple, Facebook and Microsoft — look unstoppable. They are the five most valuable listed firms in the world. Their profits are surging. Amazon captures almost half of all money spent online in America. Google and Facebook have accounted for almost all the revenue growth in digital advertising in the US last year.
Such a monopoly prompted calls for the tech giants to be broken up, as Standard Oil was in the early 20th century.
Various uses of Data
Another similarity between data and the oil is to do with the high dependence of the two for the economy. Data has now become the key driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and — crucially — new economics. Digital information is unlike any previous resource; it is extracted, refined, valued, bought and sold in different ways. It changes the rules for markets and it demands new approaches from regulators. Many a battle will be fought over who should own, and benefit from, data.
Most important, the value of data is increasing. Facebook and Google initially used the data they collected from users to target advertising better. But in recent years they have discovered that data can be turned into any number of artificial-intelligence (AI) or “cognitive” services, some of which will generate new sources of revenue. These services include translation, visual recognition and assessing someone’s personality by sifting through their writings — all of which can be sold to other firms to use in their own products.
The majors pump from the most bountiful reservoirs. The more users write comments, “like” posts and otherwise engage with Facebook, for example, the more it learns about those users and the better targeted the ads on news feeds become. Similarly, the more people search on Google, the better its search results turn out.
These firms are always looking for new wells of information. Facebook gets its users to train some of its algorithms, for instance when they upload and tag pictures of friends. This explains why its computers can now recognize hundreds of millions of people with a high degree of accuracy.
Today several applications use or plan to use visual recognition as part of authentication. Even the Adhaar authentication (in India) is set to use visual recognition. This is also being increasing used in criminal justice systems in many parts of the world. Several law enforcement companies in US for instance are exploring the visual recognition technologies during crime patrol. The cops have body cams which can capture visuals of their interaction with a patron which is then uploaded on the server. These visual recognition systems then are able to search and return valuable information in real time back to the law enforcement officers. These technologies are able to help these officers take immediate actions based on the information retrieved from these systems.
Google’s digital butler, called “Assistant”, gets better at performing tasks and answering questions the more it is used.
Several applications with voice recognition features are now being developed that leverage these voice recognition, text to speech, speech to text features of Google Assistant.
Similarly google which has been working on the driver less car has been collecting data that is helping them design and further optimize its self-driving algorithms.
Data is the fuel for the machine learning and once the machines have sufficient data they can easily perform functions and research several times faster than humans.
Google’s AlphaGo last year defeated World’s champion Ke Jie in the Chinese ancient strategy game called Go. Once considered farfetched this became reality sooner than most anticipated. With enough data given to these platforms the results can be exponentially better than expected. AlphaGo was created by London based DeepMind which was later acquired by Google.
Future of Data
One of the reasons why the machines have a capability to deliver results sometime humans aren’t able to is due to the way humans think. We have a tendency to conclude and then research our way to prove our conclusions. Machines on the other hand do not have any empathy to cloud their judgement and are able to better interpret the data. Humans have a tendency to ignore some data due to the preconceived theories whereas the machines treats each data element equally.
There are several interesting use cases for data in the future:
Medical research: medical data from various medical facilities across the globe can provide useful information that can help cure and prevent several incurable diseases.
Astronomy: We have been collecting data about the various planets, galaxies & the existence of multiple universes.
Human psychology: So far we have been unable to predict or understand reasons why humans behave the way they behave. With so much of data related to the human behaviour available there is a lot of potential to uncover the mystery behind the human behaviour.
Data is everywhere. We are collecting data at every stage of our life, in every activity we perform, with every gadget we use. While watching TV, travelling, on the phone, on the internet, attending events, while voting, buying, transacting, playing sports & even sleeping.
Data is the new oil that we have in abundance. We are never going to run out of data. On the contrary the data will only grow and that too exponentially. Learning to deal with the data and using it effectively will be the most sought after skill of this century.
HR has traditionally been treated as a second cousin when it comes to technology. HR automation projects are very often put on a lower priority and given below par budgets. The reason is that the HR automation is considered more of an expenditure and taken out of a very limited budget allocated for employee engagement. Even the HR is unable to explain the ROI of HR automation convincingly to their CEO. HR is very often seen shopping for the least expensive solution that meets their needs and end up finalising solutions more on the budgets then on the merits.
It is neither the fault of the HR nor of the top management as the major benefits of HR automation has been to automate the workflows which may save costs. When the focus of automation is primarily to save costs, the quality often takes a back seat and companies end up going for the lowest bid option. You cannot blame them as the solutions look pretty much the same on the paper. It is when the implementation is completed that the HR department realizes that by saving a few extra pennies they have taken a system that is causing more trouble than solving. Start-up products are cheaper but come with a long stabilizing phase. So what is the solution?
Solution to the problem is to find a system that adds value and contributes to the growth of the organisation. Solution is in a system like Mentis that using its disruptive new technology helps HR become a value add and not a cost center to the company any more. Role of HR is changed from finding ways to reduce cost and control attrition but to help management with ideas on how to improve the productivity. Using the features like Data Science & Artificial Intelligence, Mentis offers HR the edge that they were craving for all their life. Now the HR can proudly stand in front of the CEO and demand an investment in the HR automation as it is not a cost to make their lives easier but it is an investment that will help companies push their top lines up.
Predictive intelligence of Mentis can be used to correlate events in the employee life cycle to the productivity loss or find a way to correlate how to push the productivity up. A simple use case: Using Mentis a company realized that number of breaks taken by the employees was significantly high causing the loss of productivity. Upon further analysis it was found that employees were taking break for coffee & snacks. By putting a snack bar within the company premises, the breaks were reduced by 70% and the output increase by 10%. The cost of the snack bar was paid up within 4 weeks and the cost of solution was paid by this step alone within a quarter.
AI can be used in several ways including predicting, identify & controlling unwanted exit, candidate back out, dip in performance, unexpected loss of productivity, unplanned manpower shortage, star performers, projected liability, correlation between performance & pay, candidate profiling, employee profiling, punctuality index, potential leadership traits, risky managers & many more.
Biometric based attendance has long been the go to solution for the companies. They have been the most reliable source of input as it cannot be tampered with and eliminates the chances of proxy attendance. Enforcing attendance has been one of the top concerns for the employers. Employers have implemented several procedural and technical solutions to ensure that the employee attendance is accurate. With attendance being the input for the salary the accuracy of attendance is directly related to the accuracy of the payroll cost.
Employees on the other hand have found ways to subvert the system and have been playing the cat & mouse game with the companies. Especially companies where the manpower is distributed across multiple branches and even more so in case of field workforce this problem is far from solved by the biometric. The key reasons why biometric systems have failed include:
Expensive: Implementing biometric attendance across number of branches with low manpower count is not practical due to heavy CapEx.
Can Easily be Tampered: Employees have found ways to tamper the biometric machines by putting oil, abrasives or other ways to damage the devices. This not only fails the system but also puts a heavy cost on replacing those devices. Some companies end up putting cameras to deter employees from damaging the devices adding further to the cost of solution
Not practical in many cases: Workers in the hotel industry, manufacturing industries or some having light finger prints find it difficult to register their impressions. In such cases either these employees have to be given alternative systems or companies end up buying even higher end devices thereby increasing the cost
Regularization: In the event a company transport is delayed, or machine is not working or employee forgets to input the attendance the companies allow process of regularization. This process is another loophole that the employees use to circumvent the late coming penalties.
Infrastructure: There are instances where due to lack of reliable connectivity or power backup the inputs are not captured thereby increasing the dependence on manual attendance.
Effective: Even after all the cost and handling the challenges, the biometric systems are only able to solve one part of the problem which the in and out impression. It doesn’t and cannot handle whether the employee went back home after marking attendance and then came late to work or came inside the factory but never reported to the line. Field workforce, branch, outlets & factories the major challenge is to see if the manpower actually reported to their workplace or not. For instance a nurse walks into a hospital registers attendance on biometric device but never reported to the nurse station there is no way for hospitals to rely on the inputs of only biometric device to give salary.
So what is the solution? After spending so much money and going through all this hassle the input is still not reliable and the real problem has not been addressed. The solution lies in the latest IOT based field workforce solution.
The IOT based field workforce solution not only captures the attendance of the employees, it also gives you an entire heat map of the employee location throughout the day. You can find out when the employee reaches a particular location and how much time the employee has spent on that particular location.
This solution is perfect for field workforce where the employers can accurately track where the employees have been throughout the day. The solution also works perfectly for the outlets where the employers can accurately see how much time the employee has actually spent inside the outlet or in case of a manufacturing unit how much time the worker was actually on the shop floor.
To know more contact us and we will showcase how you can revolutionize the attendance capturing and move away from the expensive and less reliable bio-metric attendance solution. Instead of focusing on just attendance these new IOT/Mobile based solutions can help you get full visibility of the workforce.
Implementing Artificial Intelligence in the HR tech is an area where we have gained strong expertise. During our initial discussions we study the quality and quantity of transactions to understand how well and how quickly can AI be implemented in HR. A company that has no HR system will not be able to give useful transactions for the machine learning. All AI systems work efficiently if they are fed ample transactions. Any decent HRIS (not just a payroll solution) implemented for over 2-3 years will be a start for feeding any useful data for machine learning. Make sure you opt for a single tenant cloud based system or an on-premise system rather than a multi-tenant cloud system.
You can always run your AI with less data but the accuracy of the results and the real benefits start only when you feed these systems ample data. In coming years, AI will become almost mandatory to stay ahead of the competition and companies that have been sliding by without any HR systems will find themselves lagging behind. By the time the HR systems are fully implemented and ample amount of transactions are captured the competition will have a fully trained AI system that will grow exponentially making it impossible for the newbies to catch up.
Using Artificial Intelligence to help in decision making and in running various simulations to predict threats & opportunities will very soon become a norm. Within next 2-3 years all leading companies in their respective space will either have implemented or would be in process to implement the machine learning in their companies. The main problem will be faced by those companies who would find themselves without any HR system and would like to catch up only to find that they lack the feed to grow their systems.
Don’t miss out on this Golden Opportunity
It is the right time to catch the last Train to the AI Platform and adopt a good HR system that is capable of capturing the transactions that would provide quality data input to these AI systems.
Machine Learning is like teaching a toddler who is unable to differentiate between what may be right or wrong. A toddler needs to be told what is positive behavior and what is considered negative or dangerous behavior. Similarly the machine learning requires feeding useful information and letting it know what information is useful and what to ignore.
Just like a toddler learns by observation and the teachings, the Artificial Intelligence learns from the feedback given and the built-in Deep Learning Algorithms.
We have taken the same concepts of AI and applied this on the HR strategic thinking. As the system will grow it will automatically understand which information to ignore and which information to focus on. Our system has taken the concepts of Data Science and Machine Learning and given it sufficient data points to help start thinking and assist in decision making.
It may start with sales then production before it gets into HR or may be sooner than you anticipate. It might take another 3-4 years for the technology to mature or it may happen sooner. You may like it and embrace it or stay in denial but its going to happen.
Whether you like it or not, Artificial Intelligence will make inroads into every technical space within an organization.