Innovation, Future of Work, What We Do
We thrive on innovation and turning ideas into action. Anyone can be an inventor and an innovator.
When Roberto S. joined ADP, he never imagined how far he’d “Roll.”
He started his ADP journey by working as a Machine Learning Engineer. In May 2022, he moved from the Brazil Labs to the Innovation Lab in Roseland and was awarded the ADP 2022 Inventor of the Year.
ADP’s Inventor of the Year recognizes an associate who develops products with great features. Tech associates submit a summary of the invention to the ADP Patient Program, providing a unique solution to a challenge.
Roll is the first digital AI/ML HCM solution for small businesses, offering payroll, time and attendance, and more. Everything a small business needs for running HR & payroll in a simple chat-based mobile application. Roberto’s patents have driven Roll from an idea on a whiteboard to a real in-market offering.
“Roberto’s contributions to ADP and, specifically, Roll, has been invaluable, and how he focuses on driving technology forward and innovating to create new technology makes him so successful,” Roberto Masiero, SVP of Innovation, said. “It’s no surprise he’s been named the Inventor of the Year!”
ADP recognizes the hard work and innovative efforts that go into filing a patent application. Every inventor named on a patent application receives a monetary award for each utility and each design. Roberto was chosen based on his contributions, providing technically detailed and sound documentation.
Machine Learning in Roll
The machine learning models Roberto designed for Roll use a chat interface to interact with clients. In the process, Roberto and his team developed a variety of NLPS (Natural Language Processing) technologies for Roll in the intent classification, questioning, and answering domains.
On a weekly basis, the team meets to discuss strategic and tactical developmental ideas for Roll, including a technical paper reading session, in which they collectively brainstorm ideas to help make a better application.
“Developing technologies for Roll is a never-ending process of asking questions and learning,” Roberto said. “This is a team effort. I’m only the messenger and sometimes the guy poking everybody with links and technical articles.” On the team, he gives kudos to Guilherme G., Roberto C., Carlos N., and Juliano V.
The Team’s Patent Process
Roberto sees the patent process as a method to transform ideas into a formal document that will increase ADP’s innovative power on the market. “There is always a great team working behind the scenes to help engineers describe a solution and ensure this initial description will make it to a patent, with all the legal aspects covered,” Roberto said.
As the Inventor of the Year, Roberto encourages other inventors to keep in touch with the patent team to understand the process and give their ideas a try.
Advice for Technologists
“My career journey has been a remarkable, fun 5-year ride at ADP,” Roberto said. “If you’re considering a tech career, I’d encourage you to apply to ADP because this is where you can bring your ideas forward, receive feedback, and try new things.”
Transformation is at the heart of what makes ADP unique. With innovation rooted in our values, ADP continues to provide opportunities such as our patent program, showcasing ideas from associates at all levels.
“It is always important to ask yourself how the idea will benefit ADP,” Roberto said. “Keep your minds open and study new areas and domains. Sometimes the innovation happens in the intersection of domains of expertise!”
#MachineLearning #MachineLearning #HCM #Technologists #Roll #Inventor
Click here to search for your next move, and visit Who We Hire.
Innovation, Tech Trends, Career Insights
As a leader in the industry that collects a wide range of data from employees, we ensure the information is safe with us.
Say you met a technologist at a hackathon and want to connect with the person more. Instead of exchanging business cards like before, you’ll likely pull out your phone and exchange information digitally.
From LinkedIn profiles, Instagram usernames, hometown, and family relationships to mentions in articles from years ago, the internet and digital world do not erase one’s footprints in most cases.
With all information and data becoming digitalized in the 21st century, it’s time to utilize them in a way that’s never been done before. Data is not just your social media photo or where you went for vacation; it can be numbers and confidential information from financial to hospital records.
We recently had the opportunity to speak with Xiaojing W., our Distinguished Engineer who advocates for data privacy and user-respectful interactions. She shared with us some ways she keeps applications safe and secured at ADP.
Why Data Privacy is important
By Xiaojing W., Distinguished Engineer
On September 7, 2017, a consumer credit reporting agency announced that it had breached the data of approximately 143 million U.S. consumers, including customers’ names, dates of birth, social security, driver’s license, and credit card numbers. These incidents resulted in a loss of consumer trust, therefore, future business opportunities.
ADP takes pride in building applications that put customers’ privacy first with holistic security and privacy practices. In fact, our Chief Data Officer developed a holistic privacy framework instilling the privacy culture and centrally managing the practices in daily data operations.
Here are some of our methods:
When it comes to creating a trusting experience for users, we have five best practices to share:
With over 1M clients (about the population of Delaware in the United States), ADP pays more than 38M workers worldwide (about the population of California in the United States), and just in the US alone, we reach nearly 20% of the private US workforce.
As a leader in the industry that collects a wide range of data from employees, we make sure the information is safe with us. At the same time, we pay attention to the design process, ensuring a safe, user-friendly experience for everyone involved.
Here are five design patterns for creating user-respectful and privacy-aware interactions:
Tech Trend: All about Data
Data is always changing, which means more people want ways to keep their information private. This has led to the development of new techniques that preserve user information in large datasets.
Here are four types of technologies that are getting attention in the industry:
You may ask, how does the new landscape in data privacy change our product design thinking?
To better understand our clients and the needs of their employees, we must have a comprehensive view of who they are (i.e., profile data) and what they do, and how that impacts their day-to-day (i.e. behavior).
By following HBR‘s new data privacy rules, our products will empower users with trustworthy technology solutions.
Our private permissioned blockchain also safeguards highly sensitive personal data while simultaneously allowing individuals complete control. This innovative technology enables ADP to craft new products and services that benefit employees and clients.
Data privacy isn’t the Privacy Officers’ job; it’s a collective responsibility. As engineers who are often tasked with the technical aspects of securing sensitive data, we must understand the landscape of privacy-enhancing tools and technologies.
Keep in mind that we must stay up to date with the changes in the data industry as our users trust us with their information. Taking care of the trust and protecting the data should be everyone’s top priority.
#Data #DataPrivacy #WomeninStem #Automation #UserExperience
Click here to search for your next move, and visit Who We Hire.
When faced with decisions to make — no matter the topic or implication — it’s human nature to seek data. We all want information to help us make the right choice, to prove our assumptions, to validate the courses of action we’re about to take. In business, data is driving important decisions in marketing, operations, logistics and other essential business functions. We’ve seen that the insights drawn from data can provide a reliable path to better outcomes.
But data about people has perhaps never been valued like it is today. People data is propelling better assessments about the workforce and the global economy. From hiring to compensation to promotion and everything in between, each data point reveals a truth that can help business leaders and human capital management (HCM) professionals make better choices when it comes to their workforce. Collectively, such data-driven decisioning can unlock the doors to a more diverse, equitable and inclusive world of work.
With the technological tools we have today, we can mine and use real-time data to track important HR metrics, but more importantly, we can proactively help solve HR issues like turnover and retention. Through aggregated and anonymized real-time data, we can start to see trends emerge and even predict the likelihood. Data detailing how long people stay at a job, how much they earn and how often they get promoted can help businesses get a clearer picture of where they stand against the backdrop of the global economy. For example, analyzing their people data enabled one company to discover the reasons for involuntary turnover in their organization. Using these insights, they changed processes, procedures, and policies, which resulted in a 20% reduction in turnover.
Benchmarking data – knowing what other businesses in your industry or geography are paying – can also mean the difference between attracting talent to your organization or losing them to a competitor. Today’s labor marketplace has more jobs than candidates and is in constant flux. Companies need to know how they compare to others on compensation, benefits, and other key employment factors. In this environment, having up-to-date HR intelligence is crucial.
There’s no question that having access to this level of detail in your people data can help make your organization more competitive in the talent marketplace. But perhaps more importantly, this transparency into your people analytics can help you identify gaps in representation and equity and take meaningful steps to close them. There’s a need in society to continue to push forward with creating an inclusive environment for everybody, and the first way to advance that goal is by measuring progress. If you can’t measure progress, then you can’t adequately assess whether you’re making improvements to people’s situations.
Examining a critical DEI challenge, let’s consider pay equity. At the end of the day, there’s nothing more important than making sure that people are paid correctly and fairly for their contributions. In the past, it’s been difficult to accurately assess differences in compensation. We’ve known for some time about gender pay inequities but they’re often too high-level for companies to tangibly action against. The resulting discussions around the root of the issue and how to fix it also become too high-level in response. This doesn’t help leaders and HR professionals who want to reduce pay inequity in their organizations. By analyzing internal HR data and then comparing it to benchmarks across industry, demographic, geography, function and job titles, companies can now pinpoint where their organization is missing the mark.
One misconception is that hiring people at a better rate of pay will help close the gap. If you bring people in, you’re not actually creating upward mobility inside of the organization. By examining compensation across a wide range of job titles and companies and evaluating what it really means for somebody to move up, organizations can better understand where they might need to adjust course.
Pay transparency is another important and often forgotten element to closing pay gaps. Data can empower and giving employees more information about the pay of their colleagues and for similar roles in their industries can help workers across underrepresented groups gain negotiating leverage.
Data can help organizations resolve these inequities proactively, resulting in higher employee retention and better talent acquisition. Data helps you see around corners and acts as a flashlight into dark places on your path forward. We can use data to identify when people aren’t paid to the level that they should be paid. We can create tools to plan and budget to adjust for those pay gaps. Ultimately, the goal is to turn real-time data into actionable insights and workplace solutions that help businesses and people thrive. By February 2022, 75% of clients using the solution have shown improvement in pay equity, making a $1.1B impact on communities in the US.
It’s important for organizations to reflect on what’s visible within their people analytics, looking for the context and connections that create uneven effects. When patterns emerge, examine what happened earlier to understand potential causes and tailor proposed solutions. When it comes to creating a better, more equitable world of work, focus on removing barriers to progress and building programs and policies into your workplace culture that allow your employees to show up as their best selves. By using data to channel your efforts, you can effect meaningful change and become part of the benchmark that challenges others to follow suit.
Innovation, Tech Trends, Machine Learning
If Picasso were to be alive in 2022, would he use Artificial Intelligence technology to make art?
AI Art: Will it Disrupt the World as We Know it?
By Amy H. Chiu, Tech Brand Content Developer
I can’t help but wonder, if Picasso were to be alive in 2022, would he use Artificial Intelligence (AI) technology to make art?
With a background in visual arts, I spent sleepless nights in the art studio, sketching and studying every brushstroke. Every step in the art creation was filled with unexpected beauty. A small drop of black ink could alter the entire canvas. In traditional art forms, there was no control + z key to undo changes.
I remember Adobe visited my art community years ago and showcased a variety of digital tools from Creative Cloud. The tutorials broadened my horizon and challenged my definition of art. I experienced the power of switching pen tools and colors on the screen, including the accuracy and consistency of texture in design. The techniques would have taken hours and days in a hands-on studio, considering mixing colors, cleaning the tools, and using multiple mediums come at a cost.
Little did I know, that was just the beginning.
Fast forward to 2022 – all it takes is a few keywords and programming languages to create art.
Several weeks ago, a Colorado-based artist sparked controversy when they submitted a piece created using artificial intelligence (AI) and brought home a $300 First Prize.
By harnessing the power of machine learning algorithms, artists can now create works that would have taken hours and years to complete with traditional mediums. That said, what are the pros and cons of relying on algorithms? Let’s look at what we know about AI art and its impact.
What Defines AI Art?
AI art is any artwork created partially or entirely by artificial intelligence. In most cases, AI art is generated by algorithms, meaning artists write code or use software for the machines to learn. The algorithm then captures the style and aesthetic the artists want by reviewing thousands of existing paintings before generating one.
One of the most famous examples is “The Painting Fool,” a software that generates artwork digitally and paints in various styles. It was created by Simon Colton of Imperial College, London. Further reading: Painting Fool’s portfolio reveals artificial artist.
The Algorithm to Make AI Art
When you make AI Art, you will encounter a class of algorithms called Generative adversarial networks, or GANs. They are composed of a generator and a discriminator. The generator creates images from scratch while the discriminator evaluates them and determines whether they’re real. Both the generator and discriminator get better at their respective tasks, resulting in increasingly realistic fake images.
In other words, one may generate photographs of human faces and realistic images of animals that don’t exist in the world. GANs also translate images from sketches to color photographs and texts to images. For example, users may put in: “a small bird is purple with green and has a very long beak,” and get realistic photographs that match the description in the output. Read more examples here.
If you want to try GANs, here are a few steps. Step one is selecting several authentic images for training. Next, generate a few fake images using the generator. Step three is training the discriminator to use both real and fake ones. Lastly, generate more fake images and train the full GAN model using only counterfeit images. You may find detailed instructions and working python code here.
The Scary Side of AI Art
Technologies are evolving. They are convenient yet dangerous.
My biggest concern as a creator is to see people lose their respect and appreciation for artists. Although one may romanticize and say art is about the process and the original ideas behind it, the result matters, especially for agencies that hire graphic designers and advertising experts.
“Art? I can do that in 20 seconds with a detailed description in AI.” Hearing comments like this has impacted the motivation and the reality of artists. That’s when I think about the cost and effort art students pay to attend art schools.
What will the Dean tell future art students on their graduation day? ‘Good luck finding an art job out there and doing better than AI’? Although this may sound a little extreme, the concern remains as there are already limited career opportunities in the field.
My best friend attended the Otis College of Art and Design to become a fashion designer. The annual tuition on a full-time basis for 2020/2021 is $69,532. She always drew fashion illustrations on tablets and paper. Every shade and every detail mattered. Handing in the illustration collection late could result in a lost opportunity in a competitive internship.
If AI could do what she learned in four years and at a much faster speed with more pattern selections, was it worth it for her to pay the tuition and go through the training?
The Cost of AI Art
With AI Art in place, how does one price the work? Is it based on the artist’s fame, artwork’s material, time spent, or simply how “good” the art looks?
In 2018, an algorithm-generated painting sold for $432,000 at Christie’s, one of the world’s largest auction houses. The ‘painting’ was created by a designer using a computer. The news sure sparked intense conversations in the art communities. How should AI impact the value of the art generated? Should it be worth less? Then again, look at the price of NFTs (Non-Fungible Tokens). Need we say more?
AI-generated art challenges the definition of what we call ‘art.’ Consider how NFTs and AI art are created and sold. Both use algorithms, which are a set of rules. How they are applied can produce different and unique results, sparking inspiration and controversial debates. Only time will tell what else AI can do in the realm of art, but one thing is for sure: it has brought us closer to the future.
AI Art Continues to Evolve
AI art is still relatively new, and there’s much we don’t yet know about it. However, AI is profoundly impacting the art world—creating new types of artwork and how experts judge artwork in competitions.
“I see the power in AI Art, and that makes me want to support and protect traditional artists even more,” Srinivas P., the Sr. Mainframe Developer, said. “There could be a different category for AI-generated artwork in future competitions.”
Srinivas and I also connected with Sangeetha G., an artist specializing in character drawing. “Live art competitions would be great opportunities for people to see the value of traditional art. Creating-in-progress is something computers do not show.”
Computers didn’t develop the painting concept solely on their own. AI still requires human involvement before generating the result. The algorism can take a photo of a seascape and apply the style of van Gogh’s “Starry Night.” If the user is unhappy with the result, edit the input by changing a few words and generating the “perfect” one.
It’s fair to ask: are we creating art or playing a puzzle game?
For now, the ability to produce something entirely new from scratch separates us from machines. In the future? Maybe not so much.
Life @ ADP, Voice of Our People, What We Do
A podcast episode for applicants interested in the scale ADP operates at, including the leadership teams’ strategies and their focus on data security.
Life @ ADP S2EP4: Let’s Talk #ADPTech
Our hosts, Ingrid and Kate, invited Lohit Sarma, a Senior Vice President of Product Development, to the show to chat about what’s happening in #ADPTech.
Lohit’s ADP journey began in 2014 when he helped build our Next Gen team, Lifion, in New York City and scaled up the organization to about 700 associates.
“I can’t believe it’s been eight and a half years,” Lohit said. “It’s been an incredibly humbling learning experience, and I’m super excited for what’s ahead.”
The episode is great for associates and applicants interested in the scale ADP operates at, including the leadership teams’ strategies and their focus on data security. Lohit spoke about various areas in #ADPTech, from User Experience (UX), Security Engineering, to Site Reliability Engineering.
“Our clients trust us with some of the most sensitive information in the world,” Lohit said. “Security engineering is a huge focus for our products. Reliability DevOps is just across the board.”
You wouldn’t want to miss out on the episode, especially if you are interested in learning more about ADP’s Next Gen products and ADP’s role in the US financial system. From launching the iHCM, a cloud-based platform that simplifies Payroll and HR management in one scalable, compliant solution, to our next-generation time and payroll products, ADP has transformed into a technology company.
“We attract talents based on our adaptation of modern software engineering, product management, and UX practices,” Lohit said. “We’re able to not only hire but also retain and contribute back to the industry.”
From sponsoring the Grace Hopper Celebration to hiring female engineers and managers, ADP’s leadership team is building a culture that welcomes and nurtures tech talent. Further reading: Seramount Names ADP One of the Best Companies for Multicultural Women.
In addition, ADP is continually enhancing and evolving the way we do things. “We’ve been heavily investing across the board in pure engineering and management practices,” Lohit said. “That’s reflecting the quality of our products.”
Life @ ADP is available on iTunes, Spotify, Google, iHeartRadio, and Amazon Music. Listen to the full episode here or on your preferred podcast player!
Learn more about what it’s like working for ADP here and our current openings.
HCM Technologies, Women in STEM, User Experience, Product Management, DataCloud
Innovation, Tech Trends, Machine Learning
If buying an NFT does not give you the right to reproduce and sell copies, what exactly do you own?
NFTs: The Price of Bragging Rights
Why would someone spend $2.5 million on a Link to a JPEG?
You might have already seen examples of NFTs like funny ape drawings or celebrity avatars used as an account holder’s picture on Twitter. So, who would buy a personalized digital token of a dancing bear in a tutu? Is it worth $2.5 million dollars? What value are you really getting?
First, a quick definition of an NFT:
Non-fungible token (NFT)
Units of data that are stored on a blockchain. People can buy and sell NFTs; they can be associated with unique digital files such as photos, videos, and audio.
What is the difference between buying an oil painting at a gallery and buying a bunch of 2D digital pixels?
Here’s the definition of ownership.
If you purchase a painting from a gallery, you get to take it home and hang it up in the physical world we live in. You OWN the original painting. All others may have photos or even reproductions, but they will never have that one piece of unique physical canvas. For example, Picasso’s original artwork will always be Picasso. People cannot recreate the same exact painting.
If you purchase an NFT, which could be anything from JPEG to a screenshot of a tweet, it does NOT make you the owner of the “art,” it only gives you the right to claim partial ownership. Buying an NFT does not give you the right to reproduce it and sell copies. Buyers showcase immutable public transactions on the blockchain to prove ownership. Read more: NFTs – what exactly do I own?
It’s worth pointing out that although the owner has the right to use the NFT EXCLUSIVELY, a copy of the digital art can literally ‘look’ as good as the original when people take screenshots to copy and paste the images. With a right-click to save, the copies of digital files are precisely the same as the original NFT. It comes down to the owner bragging about whether they own the original NFT.
For NFT creators, you have the right to reproduce, distribute copies, and display the work in public. However, the NFT royalties work differently. Creators earn royalties through subsequent sales in the secondary market. The transaction occurs without the need for any intermediaries. Remember, not every NFT generates royalties. Everything needs to be written on the smart contract; otherwise, the creator has no claim. Read more: What are NFT royalties?
Why do people go crazy over these?
Let’s break it down.
An NFT gives you a token of ownership on the blockchain. Rather than supporting an artist by donating to them on PayPal or BuyMeACoffee, you can support them by purchasing their NFTs in exchange for documenting your purchasing record on a public, visible ledger. A second benefit, buying an NFT may appeal to collectors who gain pleasure from owning rare, digital goods. A third benefit is that each NFT has a market value, and anyone can buy/sell NFTs. For starters, it is more accessible than investing in the housing market. New to NFTs? Here are some options to store them.
Risks in NFTs
But before you dive right in, consider the risks of buying and selling NFTs. If you want to purchase one to support an artist, ask if the value you derive from ownership aligns with what it means to own an NFT. There are business opportunists who create NFTs from written codes, disregarding the meaning of art creation. For example, the 10000 Lazy Lions NFTs with different combinations of eyes, clothing, and mane are made from randomly generated codes instead of careful craftsmanship from artists.
Another danger is the way we are using NFTs. Before the pandemic, everyone from organizations to influencers jumps on trends trying to chase the cash. For example, agents have produced NFT from past photographs and artwork of the famous deceased to “celebrate” their legacy using them in the NFT market.
Many are predicting this could be the next housing bubble. Has it started to crash? What do you think? Something to consider before purchasing that dancing bear in a tutu.
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We’re hiring! Learn more about what it’s like working for ADP here and our current openings.
Voice of Our People, Career Advice, Career Insights
“To me, ADP is a tech-first company where innovations are always welcomed and are prioritized first.”
To Boomerang or not to Boomerang: How to Determine if Returning to a Company is the Right Choice?
According to a recent article, The Rise Of Boomerang Employees During 2022, published by Forbes, experts noticed a rising number of boomerang workers—meaning people who left their jobs and are returning to the same company.
We recently met David C., Senior Director of Application Development, at our tech New Hire training and discovered his boomerang story and learned more about his career journey. With more than 20 years of experience in a wide range of technologies, including DevOps Solutions, Datacenter Architecture, Product Architecture, Storage Architecture, Cloud Architecture, virtualization technologies, Active/Active, and Standard Disaster Recovery Solutions, David shares key elements to consider before returning to a company.
Coming to ADP
David’s ADP career began in 2000 when he worked as a consultant in product engineering, installing web-based applications into the hosting center. He had different roles throughout his career and landed in Development, leading MyADP/Mobile DevOps teams.
“I went from analyzing products for installing, building, and testing Disaster Recovery Sites to working for client support, infrastructure, deployment delivery, automating process, and moving to AWS,” David said. “It’s always fulfilling to grow with different teams at ADP!”
His team worked to support production clients and development groups for deployments, delivery, performance, and monitoring, where they tracked the daily health of all environments residing in the hosting centers.
Migrating all our data center from Roseland to Bridgewater in 2002 was a memorable milestone in his career. “I was so proud to receive the President’s Award for growing our data centers to support our products,” David said.
Over the next few years, he worked with more teams and helped launch the MyADP product. David was extremely honored and proud to be nominated for ADP President’s Club hosted in Spain, where they celebrated Sales and IT’s achievement in 2014.
Taking a Turn
David’s career journey took a turn in 2019 when he left ADP to work in DevOps for a bank, supporting more than 150,000 users. The new environment was a growth experience for him.
“I’ve learned about supporting structure, especially crisis management and reliability-related topics in the banking industry,” David said.
A significant difference he noticed between working for a bank and ADP was our environment and emphasis on tech. “I value our focus on tech. To me, ADP is a tech-first company where innovations are always welcomed and are prioritized first,” David said.
It was difficult for him to leave ADP after 19 years, and he’s so glad to be back. “I came back after two years at the bank. The leadership teams at ADP always make me feel included. Friendships and the culture were the biggest reasons I decided to come back,” David said. “The bonds you build at work are irreplaceable.”
Boomerang Self-Assessment Questions
We were curious about David’s decision-making process before he returned and asked him to share some insights.
He gave us these five questions to ask before returning to a previous employer:
1) Why did you leave the company?
2) Has the direction of the company changed since you left?
3) Were you concerned about the company’s previous direction? What were the concerns?
4) What role are you taking when you return? Are you moving to a position you previously couldn’t?
5) Do you see yourself growing in the new position? Does the path lead you to the future you envision?
You might be interested in exploring other good reasons for returning to your former employer. Recommended reading: What to Do When You’re Returning to a Company You Used to Work For by Harvard Business Review.
Returning to ADP
David took a big step by returning, and he’s happy to grow his career within DevOps as they build the infrastructure for automation. When we asked for details on why he returned, he shared with us how amazing it was to see the teams expanding in a great direction. During his two years away, the team continued building a solid support system for clients. Every day was a learning experience through virtual, in-person networking and mentorship.
“As an associate, I enjoy working at an organization where they value each employee, providing guidance and support programs,” David said. “I was especially grateful for ADP’s support in my education. I worked full-time while taking classes online and graduated with an Associate Degree in Business Administration.” The balance between family, work, and personal growth is the foundation for David’s passion as a Senior Director of Application Development at ADP.
Welcome back, David!
Learn more about working at ADP here and our current openings.
Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer. My guest today is Joe Kleinwaechter, the vice president of Global UX for ADP. Among other things, it’s his job to make data accessible and useful. So he’ll tell us about those efforts, about how you make use of tens of millions of records and whether analytics and HR deserves all the attention it gets, on this edition of People Tech.
Hey Joe, it’s good to see you again. Could you tell me what you’re working on right now? ADP’s a big company, deal with a lot of data. You are basically in charge of helping people get access to the information. So what’s that translate to on the ground right now?
On the ground, my job is a lot of questions. Asking lots of questions and trying to really understand. One of the greatest challenges with us as human beings is that we think we have a really good understanding of others and we only understand it through our lens. And so trying to dismiss that and constantly realize that people do things a lot differently than I do on a daily basis. So my job is to figure out when they need data, when they need access to something, why do they need it? What are they ultimately trying to do? Not necessarily, yeah, maybe they’re trying to get their pay slip, but why are they doing that? What’s the bigger picture?
Because it’s in that understanding of what they’re actually trying to do and those emotional states they have, that I can maybe get them there quicker to the end, rather than through a series of steps such as this is the way you always get your pay slip. So I really focus a lot on trying to listen for things that don’t make sense to me or are cognitive dissonance to the way we think about the world.
Do you have an example of that cognitive dissonance?
Yeah. It’s funny. You think that, listen, if I wanted to pay in the old days, if I wanted to pay somebody, I would have to go to my wallet, give them money because that’s where the money was, in my wallet. And it was only until you realize later that the money was just there because that’s all we had. People didn’t want to have a wallet. People didn’t want to have money. They wanted to ultimately give something in exchange for something else. They didn’t even want to spend money. They wanted to go get a cup of coffee at Starbucks.
And then Starbucks figures out guess what? If I have a card for you, I can keep on file for you, or I can know about you. I can maybe help you get there better, not just in paying for the money, but maybe there’s something else. Your favorites, your history. How you operate. Things that make you happier as a customer that maybe you didn’t think about when you pull out your wallet with your Starbucks case, $20 for my coffee. But at the same time, what were the other things along the way that maybe could have been easier for you? So in my job, it’s not just about how do I go and look at my pay. I got to figure out what are they really trying to do? Are they trying to figure out if they have enough money to pay something? Or better yet, maybe they have some ambitious goals to try to accomplish and I can help them along that way. And that’s exactly what we’re doing in wisely right now, in our wisely product line.
Now, obviously ADP has a ton of data and that’s kind of factored into your work, I would think. How does it factor in? How do you approach making all of this data digestible and useful?
By ignoring most of it. I know that sounds kind of contrarian, but you could get absolutely awash in all of the data. Data’s a really fascinating thing. They say from a mathematical sense, data never lies, no, but, reading it does, right? Somebody could say something perfectly legitimate, but you can interpret it a lot of different ways. So the danger you have with lots of data is that the more you read, the more you make it confused. And what you have to do is take the data and figure out, okay, what can I start with as a hypothesis? Does the data support this? Does it not? And if it’s not, how do I change and pivot on my hypothesis? Those pivots often come by taking that hypothesis and trying it out with people. Seeing does it resonate?
Okay, this says, this says this about the great resignation. This is what we know about it. Is that really what’s happening down there? And that’s where UX comes into play. Because we then go out and say, okay, we have this hypothesis, the data says this, what really is it? Is it really true or not? Maybe there’s other ways to interpret that data. And that’s probably one of our biggest challenges, there’s many ways to look at data and you can make data loo, however you want, right? The old statistics line, right? You’ve got to figure out a way that it’s unequivocally true for the people that you serve and localized to their needs. That’s the hard part about data.
Okay. Can you tell me a little bit about the technology that’s behind all this? What’s going on under the hood?
You mean gathering the data?
Not just gathering the data, but putting it together and presenting it in a way that’s usable.
Yeah, I think the biggest thing we have to focus on really is what are people actually doing versus what does the data? So the data gives us a starting spot, but really the really good data comes from what they’re actually doing as they’re using your software, for instance. How are they using it? What are they doing? So the best data is the one that actually follows them doing what they’re trying to do, rather than maybe some larger data set that gives you great demographics and breakdowns, but doesn’t really get personal enough. So what we typically do, I’ll give you a great example. In one of our latest products here called Intelligent Self-Service, we actually go back and look at all of our calls that come into the service center. And we find out which ones are the most plentiful, because those are the ones that are probably, our hypothesis is, nobody wants to call into a service center. Nobody wants to call cam Comcast, right? Or call Google. They don’t want to do that. So therefore, how can we subvert those calls ahead of time?
Well, okay, we go and look at the top 10, and this is what they’re calling in for. It doesn’t really tell me why or what their circumstance was, or other characteristics like do they really need a human, maybe they need some confidence. We then take that data and apply it in, let’s say hypothesis. We say, listen, people want to know, for instance, who their HR benefits person is whenever they do this. We then watch the way they behave using our software and say, okay, at this time we think they want this. And that combination will help them not call. So it’s a series of hypothesis driven design along the way that takes the data that we see in the call center, combined with the demographics of what we know from our products and how they use our products. Combined with what the user did at that moment, that triggers us wonderful little in, we use the AIML phrase, this black magic that happens with AIML that causes us to say, oh, these things when together have a high degree of confidence that what he’s trying to do is this. Give him this.
Now 20 years ago, I mean, we’ve been try AI for a real long time, right? For a very long time. And what makes it really good today is that the models have gotten so good that we’re right far more than we are wrong. Remember the old days of Clippy trying to figure out what you were trying to do? Hey, it looks like you’re trying to write a resume. Irritating as all get out. But now we know what you’re actually trying to do with some high degree of confidence, because we have so much data that built that model so great that we actually have a good idea that maybe not only can we tell you what you need, but maybe we can actually do it on your behalf if you want us to.
And that’s really where the state of experience is going to, can we be predictive? Can we be insightful? Can we be intuitive to what they’re trying to do and then be bold enough to offer to do it. And then when we find out that we’ve got really high degree of confidence, that we can do it every time, maybe recommend doing it on their behalf, without them knowing about it, if that’s what they want. That’s the model that the experience is going to.
Well, how does this all fit into ADP’s efforts overall?
In which respect? In terms of the UX, the experience model, this intuitive model?
So I would say right now we recognize that the big position that ADP has different than a lot of others in the industry and competitors, is that data, is the wealth of data. It would not be wise for us to ignore the fact that’s a competitive differentiator. So we use that data all over the place. So what’s really key? Our data sciences inside of ADP are pretty, pretty high level. And I say that with the great degree of confidence, because I’ve seen it operate on myself. Our AIML models that we have out there for telling where you’re going to go separates from everybody else. Now, since we have all that data, now the question is what’s the right thing to do with that data? What is the proper thing to do with the data?
And our view is really simple. If it helps our clients, our customers, our users out there to do something that they wanted to do or to make them aware of something that they want they should know, then that’s good. Right? So it’s the alignment of that big data through a good model to get into the data at the right time. That’s across the whole product line. That’s across everything ADP is trying to do. We’re trying to become, a little bit like a barista at Starbucks where we know you enough that maybe we have your coffee ready for you because you always do that. You come and say the usual. Okay, good. Here’s the thing that took you half an hour to spout out before, now happens as you get in line. And that’s what ADP is really trying to do, is to be there before even you are there.
I mean, obviously there’s a lot of technology behind this and that makes me wonder, how has the technology evolved over the last 10 years say. Which as the technology was evolving, it seemed also that the use of data was spreading. And I’m curious about, first how the technology became more of a foundation. But also how did the growing demand for it influence the technology and vice versa?
Yeah, there’s a couple of things. It’s funny having been in many industries that relied on data. There’s a good natural checks and balances with the using of data as we know. There’s good ways to use data. There’s bad ways to use data. And it’s different for every person. I used to, and I still do, refer to something called the creep factor. Something is creepy. Back in 2002, if somebody told you that you need to get in your car because your flight is going to leave in a half an hour and the roads are blocked, you’d be kind of like, well, that’s kind of creepy. How did it know all this stuff, right? And you go, well, that’s creepy. But there’s a point at which you say but that’s useful. Okay.
In the early days, we didn’t expect people to have all of that data. Now we’ve come to the point where we are growing up with societies where our kids and all others just assume you have that data, just assume that data is out there. It’s a different world about what we assume the data. Right or wrong, or whether you have that data, they make an assumption that data is there. Therefore, why wouldn’t you use it for me? How dare you not use it to help me become better? And that’s a far cry from where we were in the early 2000s, where how dare you use that data, to the point we said that data’s actually pretty useful. I kind of like the fact that you can do this for me. And then you start allowing a little bit more data, a little bit more data. And next thing we have data fields all over the place that are being mined for lots of different reasons.
First, it was just concrete data, physical data. Now it’s behavioral data. How you operate, where you move, where you go. And to the point that it’s useful, great. But there’s always this paranoia that it’s not being used in the right way. And that’s something that I think is really healthy. I think that’s a really healthy check on making sure that we are good ambassadors of that data.
What do you mean by paranoia around the data?
Well, I think anytime somebody knows something about you that you either A, didn’t want them to know or didn’t know that they know, there’s a natural paranoia in us that asks how are you going to use that? What are you going to do with that, right? And knowing that if this were a benevolent world where everybody was going to use it, right, we’d have no problem with it probably. Not everyone, but a higher majority. But now we’re in the place where we have to be very careful about those that want to use the data to harm us or to use it in a way that annoys us at the very least, right? The scam calls that you get all the time, all the phishing techniques that are being used, things like that. There’s a whole black science of UX out there to trick you to go do things because they have some data, right?
There’s reason HIPAA was set up, right? There’s a very valid reason why HIPAA was set up and needs to be needs to be respected and done because of the bad that you could do with that data they aren’t governed correctly. So we treat governance with data incredibly, incredibly important. It’s at the top of what we do in all of that governance. We know we have an ethics board. We have our chief data officers constantly making sure that we are using data in an ethical way. And that it really truly not only is just ethical, it’s got to be valuable. It’s got to be something valuable for our clients and our customers. Otherwise, it’s just data.
I’d like to shift gears a little bit for the last few questions. Delivering data in the flow of work, the whole notion of in the flow of work is gaining a lot of traction. A lot more vendors are exploring ways to present their products that way. Does that pose any particular challenges for a data service or is it better? What’s your response to it?
Yeah. There’s a fascinating thing that I learned, again back in the early 2000s, I worked at a company that we decided at the time Google had come up with Appliance, right? That you could put inside your internet and all of sudden you could use as a search engine localized to your internet. We put the Appliance up there and it didn’t perform well at all. We let it run. We let it run for a couple of months and it kept getting data. It could never, the finds were just not good. They weren’t even close to what you would get on the internet. And what we learned from the Google data scientist at the time was the reason that the internet is so valuable as a search tool and so accurate, is because it has so much heterogeneous dat. Data that doesn’t appear related but in a way is, and that heterogeneous data gives us a much greater chance of finding that needle in the haystack that you’re looking for.
Whereas inside of a company, it all looks like the same thing, give me the latest dev report, give me the latest financial report. It’s more of a monocosm of stuff, and therefore you couldn’t find things. As we start meeting people on the go, where they are, we now have the chances for other types of data to improve that. Now depending upon where you land on the privacy of knowing where you are, geofencing and things like that, there’s a lot that can be done by knowing where you are. The question is by knowing where you are could you also use that for nefarious means? Yes, I guess so. Sure.
So you’ve got, I think the real challenge is, as we learn all this new data, what’s right to keep and what’s right not? And that’s not necessarily our choice, right? That needs to be our client’s choice of what’s valuable because again, going back to the creep line, if I know where you are and I can offer you this new service, it should be your choice, whether you want to exchange that data for that service. Not we’re going to take this all from you.
Companies have gotten in trouble in the past. We’re going to take this data. We’re going to read where you are and not tell you, and we’re going to give you a great product. Even if it may benefit you, the fact that you took that without my knowledge makes me suspicious that you may take it to do something else. And I think we’ve got to be really, really careful that having an honest conversation, a full disclosure and a strong ethics policy behind your data is really going to make the difference. Now with that in place, now I can meet people where they are. I can see where they are. I can get a lot more information.
A great example. One of our customers has a lot of field workers, right? And they have their phones on, they got GPS on their phones. If they want to transmit their GPS information, great, they’ll be great. They can do it. We can tell when they’re going to clock in, when they’re going to clock out and maybe even clock them in automatically. So we get rid of the single biggest call, to most HR departments, is I forgot to clock in. Can you clock me in? I forgot to clock out. Can you clock me out? Something as simple as that, just by turning on GPS location. Is that valuable or not? Well, that’s kind of a client thing, isn’t it? You tell me. Is it something you want to exchange for that? Then I have put governance about what I’m not going to do with that data. That’s just as important. And maybe I’d say is even more important. Because just because I have the data doesn’t mean I can use it however I want. I’ve got to use it in a prescriptive way,
Joe. Thanks very much. Really appreciate it.
My pleasure, Mark. Thank you.
My guest today has been Joe Kleinwaechter, the vice president of global UX for ADP. And this has been PeopleTech, the podcast of the HCM Technology Report, a publication of RecruitingDaily. We’re also a part of Evergreen podcasts. To see all of their programs visit www.evergreenpodcasts.com. And to keep up with HR Technology, visit the HCM Technology Report every day. We’re the most trusted source of news in the HR tech industry. Find us at www.hcmtechnologyreport.com. I’m Mark Feffer.
Voice of Our People, Career Advice, Career Insights
Data Science is perfect for you if you enjoy storytelling and solving complex problems with data.
Is Data Science the Right Career for You?
By Mark P., Lead Data Scientist, Product Development DataCloud
As a Data Scientist at ADP, I use workforce data to tell stories, using curiosity to analyze and display the data. In this blog, I’ll share my observations of experiences and trends in the growing field of data science.
According to the U.S. Bureau of Labor Statistics, data science will continue to grow, and the number of jobs is estimated to increase by 28% through 2026. In other words, data scientists are in demand, and our role will continue to impact many industries.
What comes to mind when you hear “data science”? Numbers and graphs? Machine learning and big data?
Let’s dive into a quick definition.
What is Data Science?
My perspective on data science was shaped years ago. People started referring to themselves as data scientists and posting jobs for “data scientists” around the same time that machine learning with big data was spreading to industries and companies beyond tech.
I view data science as the methodical analysis of an extensive dataset to understand a subject of interest. Machine learning is a powerful means of such analysis, but not the only one. I focus on a different area, writing query code and dynamic calculations to produce interactive visualizations. To me, the significance of big data is more of a spectrum than a boundary. Science is a systematic study for understanding, and we can understand things with smaller amounts of data too. But big data like ADP has made the insights and applications deeper and more reliable.
Pragmatically speaking, data science can be whatever an employer considers it and communicates through the specific skills they seek. No definition of data science can replace an employer’s expectations, the candidate’s expression of their experience, and conversations about career fit and advancement. With evolving technologies and models, there are a growing number of opportunities in this career. As a Data Scientist at ADP, it is certainly rewarding to have occupational, organizational, and demographic facts on over 30 million US workers to explore – anonymized of course!
Top Trends in Data Science
Currently, two of the most visible trends in data science are cloud-based development and the advanced application of natural language processing (NLP).
Cloud-based platforms and services such as Amazon Web Services and Databricks make it easier to source data, develop analyses and models, collaborate with colleagues, and deploy products. We work closely with these partners and have often spurred innovation in their products as we expand our capabilities.
NLP has many current and potential applications in human capital management, including client support, occupation and skill classification, job posting development, and candidate recruitment. Since jobs are diverse, overlapping, and constantly evolving, building and maintaining comprehensive, systematic knowledge can be challenging. NLP can make our solutions more scalable and data-driven than classifications created by human experts alone.
Day in the Life as a Data Scientist
My research on restaurant employment and wages during the COVID-19 pandemic represents many common day-to-day components of data science work. While it is well-known restaurants were one of the most heavily impacted industries, ADP data shows some cities fared better than others. You can see this in the 18-month employment trends for 3 of the largest 50 US metros.
Visualizations like these are the tip of the iceberg: the most visible part of the work requires much more underneath. In addition to conceiving and developing metrics, models, and graphics to create knowledge, data scientists need to find good data sources and write code to retrieve and process their information. They need to understand the limitations of their sources – things like sample bias, predictive labels, outright errors – and communicate and correct them.
And data scientists need to query people as well as data! For example, interviewing local restaurant association executives for their expert perspectives and calling US Bureau of Labor Statistics economists to discuss statistical methods.
How can I gain experience in Data Science?
If you are interested in data science, you can find a ton of resources, including boot camps, online courses, Medium articles, and YouTube videos. If you look up #datascience on TikTok, it has 89 million views! Of course, classes are a great way to acquire vital education, but they can be a significant investment in time and money. You may wish to test your interest with a project that involves either a question you’d like to answer or a problem you’d like to solve. You’ll gain not only motivation but also a proof point to share with potential employers.
As an example, when 2020 presidential candidate Andrew Yang proposed a universal basic income, I was curious to know who might benefit from $1k a month and how to quantify the benefits objectively. I searched for household spending data, turned up relevant data and code from the Bureau of Labor Statistics, and then used free versions of SAS and Tableau to create a public dashboard to answer that question.
I’d advise anyone interested in data science to follow their curiosity and search the web for public data and free tools. You’ll face technical challenges along the way, but sites like W3 Schools and Stack Overflow can help you tackle them as they arise. Of course, many people prefer the structure of classes to an open-ended, “many-options-no-right-answer” type of project. The former is fine – but if you can take the leap and try the latter, you’ll gain a good experience of what real-world work is often like!
Data Science is a great option if you can:
Three self-examination questions for Data Scientists interested in ADP:
Interested in a career in Data Science? Let’s work together!
Learn more about working at ADP here and our current openings.
Innovation, Voice of Our People, Future of Work
The future of learning will involve more personalization and customization based on learning styles, competencies, and preferences.
How Artificial Intelligence and Machine Learning are Driving Innovation and Opportunities at ADP
Julio Hartmann joined ADP as a software development manager in 2004. Seventeen years later, he is now the Vice President/General Manager, head of ADP’s global software product development center and innovation lab in Porto Alegre, Brazil. His team works across the global product and technology portfolio, always looking for new opportunities. Julio leads product innovation and research, exploring growing technologies and evolving trends. He and his team aim to create the next generation of human capital management applications that drive learning and training in the workforce.
How it Started: Human Capital Management (HCM) Software
Steve Jobs said, “Things happen fairly slowly. These waves of technology, you can see them way before they happen, and you just have to choose wisely which ones you’re going to surf. It takes years.”
People tend to assume technology evolves linearly—growing at the same rate over time—but it develops exponentially instead. Some examples of exponential technologies include 5G networks, 3D printing, robotics, and blockchain. As the speed of technological innovation increases, it creates frustration in product development. People perceive a gap between expectations and performance, then quickly learn the products are not the problem. We inflate our expectations beyond what technology delivers. Despite uncertainties in the environment, the emerging tech follows an exponential growth and improves until it reaches a pivotal moment of breakthroughs.
For many, the pivot point may be challenging to foresee, and companies are caught unprepared. With market research observation, we know breakthroughs happen for a number of reasons. The moment is often tied to technology becoming cheap enough to reach mass consumption. In other words, a breakthrough occurs when a component becomes more viable with a combination of factors, creating the perfect environment to throw the innovation into disrupter status.
The phenomenon played out clearly in smartphone market. When the iPhone arrived, that changed everything. We live in a time when anything and everything is possible. Modern technologies drive the future and bring endless learning opportunities to the next generations. To prepare ADP for the next move in the industry, my team continues to develop, recognizing the power of artificial intelligence and machine learning.
The Future of Learning: HCM Systems
The future of learning will involve more personalization and customization based on learning styles, competencies, and preferences. In other words, artificial intelligence (AI) and adaptive learning are the future. These powerful technologies will affect both humans and machines in the coming years. Our goal at ADP is to develop a combination of tools that harness the power of AI and facilitate learning, ensuring companies and employees grow at a fast, steady pace.
The job market is shifting due to the broad impact of AI, automation, and robotics. There is a reduced demand for specific jobs, such as factory roles that can be automated. On the other hand, there is an increasing demand for particular jobs that belong in the future. According to the report by the Institute for the Future, 85% of the jobs in 2030 do not exist yet. It’s time for leaders to identify skills gaps based on current trends to prepare organizations and professionals.
In fact, we might be heading towards a disruptive breakthrough in artificial intelligence and data usage in human capital management (HCM). We are not far from a pivotal point, meaning we can expect many advancements with the power of AI and data information in HCM for the upcoming years.
As an industry leader, ADP looks forward to the future. My team supports innovation through our mantra — always designing for people. HCM solutions provide opportunities for companies and workers to grapple with the demands of a futuristic workplace. AI helps companies manage their workforce while anticipating changes and preparing their employees for upcoming challenges. Specifically, my team is working on technology that allows companies and employees to navigate a variety of scenarios. It combines traditional training and cutting-edge tools that connect people with mentors and experts in various communities.
We can’t talk about the future without understanding users’ needs. The good news is human capital management systems and training tools have become more predictive with ground-breaking developments in event-based systems, meaning they carry on as usual until they require inputs. For instance, a system can recognize users changing their addresses and further instigating necessary documents and paperwork. Another example is for the system to alert managers of a potential alarming pattern that shows an employee has not filled out a timecard.
AI’s Applications in Real Life
AI’s applications in real life are everywhere. Companies like Walmart hire a significant number of workers every month, experimenting with augmented reality (AR) and other technologies in new hire trainings. Wouldn’t it be more efficient for new employees to see the procedures before joining the company? The new hires at Walmart could see the supermarket’s organization in a virtual environment through a peer-to-peer reality before their first day at work.
Human resources (HR) managers may also benefit from using AI. From recruitment to employee experience and talent management, AI can automate routine HR tasks, deliver personalized experiences, and gain actionable insights from HR data. For example, AI may serve as a helpful tool to help track the workforce and notify managers that they need to hire more data scientists.
Another scenario is using AI as a user interface (UI) through natural language processing for seamless interactions between humans and technology, for example, using chatbots as the user interface. AI can be a powerful ally to promote diversity, inclusion, and equity among employees if leveraged carefully.
These are all opportunities and concepts that will change the future of jobs.
Challenges in AI Technology
“With greater power comes with greater responsibilities.” There are risks with using the tools. At ADP, we have an ethical committee that looks at privacy issues and built-in biases. The technologies are developing quickly, which makes predicting outcomes challenging. Nevertheless, we always try our best to watch for violations and learn as we go. The teams at ADP are investing in a well-detailed approach to monitor how the machine learns and develops, ensuring all technologies evolve in the direction we expect.
Looking Forward: ADP’s Future
Technology development plays a huge role in ADP’s transformation into a technology company. There is more capital available than ever before, and the cost of building innovative products has become lower. In other words, we have more funding to experiment which leads to more breakthroughs. We are on the cusp of seeing more efficiencies on a massive scale through AL and ML.
The possibilities of using AR and VR during the company’s onboarding training are exciting! I can imagine applying AR and VR in digital workplaces for associates who work from home. The technologies bring efficiencies, save costs, and improve learning. Workers will have the ability to see the office and understand procedures even before joining the team in person. The implications are astronomical for national and global companies.
As we research more possibilities in tech, humans will benefit from using technologies in the workforce. The foundational trends include faster computing power, increasing data volume, low-cost communications for everyone and everywhere. These opportunities are life-changing, and we’ll see this come to fruition soon. I look forward to how the industry creates unique jobs in the workforce and breakthroughs. In the future, technologies at ADP will continue to help companies and workers adjust to changes, improving their job performances and making tasks easier.
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