Posts
Tech & Innovation Blog
Women in STEM: Pioneering Product Management with Seema J.
Voice of Our People, Tech Trends, Career Insights
“Practice what you deliver in speech and use your products to better understand the user experience.”
Coming to ADP
Before joining ADP in August, Seema J. had worked in the technology space for over 25 years in different industries. She started with telecom and moved into media, health insurance, and information services. She has looked at various digital transformations and technology consolidations from a back-office perspective.
“Throughout my career journey, I’ve discovered my passion lies in using technology to create impactful customer experiences,” Seema said. “I ask myself: how do I use technology to solve problems? I also value mentorship and team growth, always moving forward with new ideas.”
When she came across the product management opportunity at ADP, she took it immediately. Seema’s service tech background led her to design systems from a global lens. She’s looking forward to building her career and focusing on client service.
Seema J., VP of Product Management
Power of People
Seema shared that her first two months at ADP were about absorption, understanding, and learning. She is now putting her knowledge and vision into forming a go-to-market strategy.
“Associates here always make time for each other, sharing updates and exploring tech interests,” Seema said. “I also connected with people outside my team to understand their work.”
When asked about her career journey as a woman in STEM, Seema explains she has always been interested in technology. She started with a bachelor’s degree in electrical engineering and pursued a master’s in computer science.
That was when Seema came to her first career intersection after college: pharma or telecom, and she chose the latter. She used her experience in the industry to grow in different areas, such as long-distance billing system analysis, insights, and tools.
“I enjoy the long tenure of associates at ADP and how many transitions into different roles within the organization,” Seema said. “This allows people to gain experience in multiple areas and apply that knowledge in any teams they join.”
Seema then went back into internet products and design, specifically in chip designs, product lines, and the portal space. Her career involves creating customer experience journeys and leveraging technology to solve problems.
“My career progresses by understanding what I can learn and always searching for forward-looking solutions,” Seema said.
Systems and Cloud Technologies
Seema focuses on improving the client’s experience and services. She looks at both ends of the spectrum from the client’s and associate’s perspectives.
“My goal is to have the knowledge readily available to associates to fulfill client inquiries on time,” Seema said. “I’m working with a mix of homegrown and cloud-based technologies that provide self-service capabilities.”
Seema is also exploring chat capabilities and predictive analytics to understand client sentiments and help associates better support them.
Three Product Management Trends in 2023
Seema believes that there are trends toward digitalization and a hybrid environment where people access information on their smartphones and other gadgets quickly and easily.
1) Personalization
Personalization is becoming increasingly crucial for end-users, and many products are leveraging data to provide a more personalized experience. For example, Siri and other customized tools show how products can leverage data to improve the user experience.
2) Low-code/No-code
Seema believes that low-code/no-code environment is becoming more popular as the industry moves towards software as a service (SaaS) products. The setting allows developers to prototype and test their products quickly, getting user feedback and improving the product promptly.
3) Data and Artificial Intelligence (AI)
Though the trend towards data is not new, Seema notes it is becoming more critical to apply artificial intelligence (AI) to gain valuable insights.
“I believe applying AI will become a standard practice in products soon,” Seema said. “I am enthusiastic about the possibilities in these trends, offering more in product management. They will continue to shape the industry for sure.”
Data-Driven, Innovative, and Fun
When it comes to the qualities of a good leader, Seema talked about the importance of innovation and adaptability, especially in the current environment where multiple workstyles exist, from hybrid to remote.
“My team comes from different backgrounds and experiences. There’s always more to learn,” Seema said. “We enjoy the team-building tool StandOut, which helps us understand each other’s strengths and how to leverage the traits.”
Seema advises technologists interested in Product Management to listen to clients and their feedback, which can be a significant input for product innovation.
“Practice what you deliver in speech and use your products to understand the user experience better,” Seema said. She describes ADP Tech as data-driven, innovative, and fun. Outside of the tech world, Seema enjoys gardening to relax.
#ProductManagement #WomeninSTEM #Data #AI #Technologies
We’re Hiring!
Click here to search for your next move, and visit Who We Hire.
Tech & Innovation Blog
Roberto S. Named ADP Inventor of the Year: This is How We Roll
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
We’re Hiring!
Click here to search for your next move, and visit Who We Hire.
Tech & Innovation Blog
Data Privacy: The Future is Now
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:
- Identification and classification of sensitive information as a part of data governance and management
- Implementation of enterprise-level and system-level data observability/monitoring
- Safeguarding information through various data security controls and advanced technologies such as privileged access management credential choices, secured APIs, file shares, and encryption through data’s entire lifecycle Implementation of Data Management Capability Assessment Model (DCAM) measurement processes to make privacy and security auditable
When it comes to creating a trusting experience for users, we have five best practices to share:
- Engage users in setting the privacy boundaries they want while getting the desired functionalities.
- Build a consistent consent model whenever we collect or use data
- Always include a system of consent receipts for auditing
- Bring full transparency and visibility into why we collect the data, how the data is processed/stored, and with whom we share the data
- Practice minimal data collection and store only the necessary data for future operations
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:
- Users must actively opt-in to having their data collected and used
- Users must give consent to every type of data processing activity
- Users must be allowed to withdraw their consent easily at any time
- Users should be able to check every organization and all third parties that will handle the data
- Use just-in-time data collection
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:
- De-identification: we remove or encrypt PII and CII. Multiple techniques, such as tokenization, K-anonymization, and pseudo-anonymization, are provisioned within a Single Global Data Platform (SGDP).
- Differential Privacy: The idea is to add a layer of privacy by adding noise to the original data sets. The new sets make it extremely difficult to infer information about a single individual, while the accuracy of the data analysis and machine learning tasks is not compromised too much.
- Synthetic Data: is algorithmically generated data that mirrors the patterns and composition of the original dataset. Synthetic data can be significant for engineers to test with production-like data. It is also widely used for training the machine learning models used in fraud detection systems and applications with no data or hard-to-get initial data.
- Zero-knowledge proof: Utilizing cryptographic technology, a claimant can validate assertions of confidential information to its verifier without disclosing the details. By leveraging blockchain-based decentralized identity ledger, individuals and companies can easily control who has access to their digital identities. This provides trustful proof of identity, making it easier for users to manage credentials across the web securely.
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.
Closing Thoughts
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
We’re Hiring!
Click here to search for your next move, and visit Who We Hire.
Data’s Power to Inform Decisions in Business and Work
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.
How Data Can Inform People Decisions
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.
Data as a Flashlight
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.
Driving Change through Data
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.
How Technology Is Evolving to Bridge the Gaps of the Dynamic Shifts in the Talent Landscape
JOBS & UNEMPLOYMENT
Bridging the Talent Gap With Data-Driven Technology
CONTRIBUTOR
ADP
PUBLISHED
OCT 20, 2022 1:53PM EDT
Sponsored Links
You Could Grow Your Money Without the Stress of Stocks
NerdWallet
By Don Weinstein, Corporate Vice President of Global Product and Technology at ADP
With their priorities shifted by the pandemic, today’s workforce wants more from their employers, including greater flexibility, better work-life integration and a heightened focus on diversity, equity and inclusion – and they are willing to make a change to get what they want. We’ve seen more workers re-evaluating their place of employment, with seven in 10 workers saying they’ve considered a career move in the past year. Despite anecdotes to the contrary, we remain in a tight labor market, and the best way to get in front of the ongoing hiring challenge is to start by holding onto your experienced workers. By leveraging new data-driven technologies to create engaging work environments, today’s business leaders can confidently bridge the talent gap and create a more engaged workforce.
In this age of the employee, it is critical HR leaders continually assess their employment brand to find ways to improve the worker experience. Is your workplace environment truly inclusive? Are you giving employees challenging work that leverages their strengths? Are you taking care of their health and welfare needs? Leaders need to ask themselves these questions, while deploying data-driven HR technologies that can help identify the right solutions. For example, personalized worker surveys can help employers better understand their workplace culture and predict potential retention challenges. Another important tool is skills mapping, which breaks down jobs into a set of inter-related skills, enabling employers to mine internal applicants for potential fits as well as career development opportunities. The same technology can also assist your external recruiting function, by broadening potential talent pools to look at all relevant candidates, including those from non-traditional backgrounds.
The evolution of HR tech accelerated when our ways of working were upended a couple years ago. But these changes have kept the industry dynamic and ignited new innovations. As we look to the future, we see a lot of promise in these areas of HR tech:
AI and machine learning for sourcing talent in hard-to-fill jobs: Algorithms are being deployed to find novel talent pools to source candidates through skills matching and retargeting. These algorithms also play a bigger role in upskilling tomorrow’s workforce, providing insights on skills-based learning and career pathing that can help guide and advance employees’ careers.
Technology-driven advancements for building more diverse and inclusive workforces: Skills matching can help uncover capable candidates from non-traditional backgrounds. Sentiment analysis can be used to assess employee perceptions on the overall level of inclusiveness in the workplace. And machine learning can help identify and correct workplace equity gaps.
Of course, these approaches will be effective only if companies remain agile during times of change. Leaders need to ensure that the right systems are in place to optimize their teams’ ability to deliver good work and to adapt as the environment shifts. Essentially, businesses need technology designed for how work gets done, so they can more easily adjust at the pace of change.
You can hear more about these emerging HR technology trends, what’s to come and how to stay agile in my Nasdaq TradeTalks interview below:
Tech & Innovation Blog
AI Art: Will it Disrupt the World as We Know it?
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.
An AI-generated work, “Théâtre D’opéra Spatial,” won first place in the digital category at the competition. Credits to Jason Allen
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.
For more emerging tech topics, subscribe to our blog and receive monthly newsletters.
Podcast: Using Data to Advance Diversity
Podcast: Using Data to Advance Diversity
Transcript
Mark:
Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer.
My guest today is Bob Lockett, chief diversity and talent officer at ADP. He’s responsible for the company’s diversity and talent strategy and oversees performance management, leadership development, engagement and culture, among other things.
We’re going to talk a lot about data and its relationship with DEI, from helping determine where a company’s at, to initiating new programs. That’s on this edition of PeopleTech. Bob, welcome. It’s great to meet you.
How does one attack the task of leading on diversity for a company the size of ADP?
Bob:
Well, Mark, the first thing I’ll tell you, it’s a very challenging task, because you have so many different constituents and everybody wants their own piece of the pie. What about us? What about us? What about us?
As you can imagine, DEI is a very emotional topic, for that reason. So, the approach that I’ve taken, that we’ve taken at ADP, is really tied to doing a couple of things.
Number one is using the scientific method. You know that thing, Mark, that we learned about back in middle school, that many of us did those experiments?
You would say, develop your hypothesis. Then from the hypothesis, you allow data to prove or disprove your beliefs. And then once you do that, then you really define the problem.
After you define that problem, then start to put plans in place to achieve the outcomes. You tweak as you go, as needed, based on feedback.
So what we’ve done is taking that exact approach and say, let’s take the emotion out of it as best we can. Let’s focus on the data. Let the data be our guiding light, to help us understand where we need to focus and what we need to do.
Now, this doesn’t just apply from a US standpoint. Think about it. This is a global opportunity that we’ve embarked upon. The way I view it is, there are needs everywhere, for people to feel like they are seen, valued and heard for all that they are.
So, not only do we think about diversity… You can measure diversity very easily. You can look at demographic data. How many of these do you have? How many of those do you have?
You can measure equity by looking at pay, but the key is also to measure inclusion. So, we take this holistic approach, all data driven.
The inclusion piece is all sentiment driven, but it’s really leveraging the scientific method and leveraging data, to help tell our story.
Mark:
Can you expand a bit on how data is used in DEI work? I mean, you mentioned that this is a pretty emotional subject. It always strikes me as interesting when you apply data to an emotional subject. How do they work together? So can you talk about that?
Bob:
Sure. I could tell you the stories of how we landed where we are, with some of our things.
The first thing that we did as an organization, when I took over the role, I wanted to understand how we looked, because I have a vision that our associate population in our company is reflective of the communities in which we operate and the clients that we serve. That’s very specific and very clear.
How do you test that, your hypothesis about that? How do you make it a realistic vision?
We looked at about three or four different datasets. One dataset was a census data. And as you know, the census data doesn’t mean that everybody’s working.
So, we looked at the census data and we say, “What’s the representation for African Americans, Hispanics, Asians, white women, everybody in our organization?” Let’s lay that out to understand it.
Then we looked at the Bureau of Labor statistics data. Of the people in the workforce, let’s take a look at how that compares and then let’s compare that against our information.
So, we compared it against our information, I’m talking specifically in the US and said, “Huh? Where do we have gaps?”
My hypothesis was that we didn’t look like the communities in America, but the reality of it was, we did. So, I was really impressed. I was like, wow, this is great news.
But as you look at the data, we also found that when you look up in the organization, you don’t have parity in representation for two populations in particular, which were African Americans and Hispanics.
We said, they represent 15% of the overall workforce in the US, for Hispanics. Let’s say it was 11% for African Americans.
Well, we noticed a gap in our company of about four percentage points each way, for African Americans and Hispanics.
We said, well, we should close that gap, because as you come to an organization, you also want to be able to see if there are opportunities for you to advance.
If you don’t see anyone that looks like you, in management level positions, then you start to wonder if you have a real future there. So, that was our quest.
This is how we use data to really understand and tell our story and to put plans in place to do it.
Now, notice the nuance here. Because again, if you go back to my original hypothesis, that we didn’t look like that, we did, but then we pivoted very quickly, because the data told us a different story. We said, that’s where we’re going to focus our efforts.
Now, some people use, Mark, data to try and boil the ocean. You can’t do everything. You can’t be all things to all people. That is a recipe for failure, particularly in DEI.
So, that’s why we have a very narrow focused approach. We have multiple initiatives that we work on, but suffice it to say, that was our main effort, for us to be able to say, we’re moving the needle when it comes to leadership representation in our company.
Mark:
Now, do you think your company is an outlier in that, or do you think that more corporations are starting to get on board with the idea of using data in this regard?
Bob:
Yeah. I think it’s a mixed bag, Mark, is probably the best way to describe it. Most organizations will take a look at their data. They’ll focus on where they think their opportunities are.
But it depends on where they are in their journey, their DEI journey, which I always talk about, that not everybody’s at the same place.
For us, I believe we’re an outlier. We’re an outlier because if you think about DEI, it’s one of our values. The things that really resonate in our organization, is that each person counts. In order for each person counts, by default, you have to have a DEI strategy.
Some organizations don’t put as much interest or effort into it, so there at varying stages.
It became a great corporate buzzword two years ago. Prior to that, many organizations weren’t making headway, with respect to that. So, my belief is, we’re certainly an outlier with our use of data.
Of course, Mark, that is our middle name. So, we use data to make sure that we can tell our story, to solve the problem, to understand all of those things. We’re all about measuring success. How do you measure the effectiveness of what you’re doing?
Having said that, I think we’re a bit of an outlier. I think there are other organizations that are doing great things, but I think there are some that are not doing anything because they don’t know where to start.
If that’s the challenge for them, then a great place to start is, understand your data at least. Then, think about where you want to have an impact.
Mark:
Can you think of any particularly surprising things that you’ve learned from data?
Bob:
I can give you a couple of examples of things that I think we’ve learned. Number one is that it’s never enough. Here’s what I mean. We had to put plans in place to do this.
I’ll just give you this example, Mark. We launched our talent task force. It was a specific focus on the African American and Hispanics/Latino community.
Well, as soon as we put that out, the first question that came was, hey, what about the Asian community? I said, “Huh? I’ve got a story for you. Asians represent 5% of our population, but yet they represent 8% of leadership.” So, there’s no problem there.
Then the next call came from the LGBTQ+ community. I said, “Huh? Tell me what the data says.”
The reason we couldn’t make a decision and put a plan in place to improve representation for that community, is because we didn’t have any data. So, that’s one of the things that will surprise you about that.
And when you don’t have enough of it, everyone wants to do these things, which is back to my point about, people get involved in this. They want to represent their constituents.
But at the same time, without the data, you can’t get involved and create corporate programs to improve something.
The second piece still ties to self-ID. If you take this to a global scale, so typically in numerous countries, they don’t collect the same data that we do in the US. They don’t collect it because their philosophies are different. It could vary, country to country.
However, there’s renewed emphasis on understanding your workforce and being inclusive. So, just imagine, you’re a multinational corporation and you don’t understand the dynamics that exist in operating in Tunisia or the dynamics that exist in operating in France or Italy and who the underrepresented groups are. So, we’re trying to capture new data.
That’s one of the surprising things, is that we’re beginning a journey globally, to do a self-ID approach.
It’s not just us, by the way. There are multiple companies now showing renewed interest in this, to say, how do we understand our workforce? How do we become more inclusive, so we can appeal to the needs of various communities where we operate?
Mark:
Are you satisfied with the kind of data that’s available to you today? What could be better?
Bob:
Yeah. I’m in a unique position, Mark. I tell people this all the time. At ADP, because we’re a data company… again, it’s in our middle name, I have the unique opportunity that we have our own department that does all of the analytics, pulls the data, does the comparative analysis, the sensitivity analysis to whatever we want to do.
Now, for companies that don’t have that, we do have a diversity dashboard, that gives them insights into their own information, that they may not have thought about before.
They may not have the luxury of having a large DEI department, like we do. They may not have the luxury of having the analytic capability, but we can provide them with some insights about how their organization looks, what their leadership makeup is. Oh, by the way, with pay equity too, we can take a look at that data as well.
So I think I’m in an enviable position. I’ve got all the data that I need. The key for me, is staying focused and executing, to ensure that we make a difference with our DEI efforts.
Mark:
What are your overall goals for your DEI efforts? I mean, what kind of changes are you hoping to enable or enact? What has to happen for you to be able to get there?
Bob:
Yeah, it’s a great question, Mark. I’ll go back to my vision. The vision that, we want our associate population to be reflective of the communities in which we operate and the clients that we serve.
That is the most important thing, because I believe that the efforts that we take to do that, will have a great cyclical impact on the environment.
Here’s what I mean. I’m not in the DEI business because I’m a social justice warrior. I’m in the DEI business because I believe that there are economic opportunities in a capitalistic society, that we can get everyone to participate in and grow the pie. I firmly believe that.
In many cases, it starts with employment. So, what do we do as part of our DEI, some of the work that we’re doing? Well, we want to hire in those various communities.
We have outreach efforts to every community, to make sure that we’re attracting the best and the brightest for our organization.
Then of course, once you get there, you have to walk the talk. So, culture is really important, Mark, in this space, to ensure that if you said you’re going to do it, then you have to do it.
My saying is, don’t talk about it. You have to be about it. So, if you’re about what you said you are, by bringing everybody together and giving everybody an opportunity, so they can be their true authentic selves, then that makes a tremendous difference.
So, that’s the talent piece of it. Getting them in, giving them the opportunities to grow and develop, and then seeing them get promoted and being able to contribute.
Now, I also talk about DEI from a business practice standpoint. Oftentimes in the past, organizations that I’ve worked for, DEI was all about some of the HR practices, which I just talked about briefly. It was all about talent practices,
But I also incorporate business practices. Business practices are really about, well, how do we tap into the ecosystem of businesses and communities?
Oftentimes, you have underserved communities, that don’t have the same opportunities to understand things.
Give you an example. We have a company that we partner with. What the founder shared with us, was the fact that for many minority-owned businesses, they only have one way to finance their business. That’s through loans from family members or debt.
So, they don’t get the full spectrum of how to do revenue-based financing for their business, or how to think about the debt market very differently, that others have had exposure and access to.
So, giving them exposure and access to the full gamut is really important, but that also requires some education. So, we partner with organizations, to do that, just so businesses can finance it.
Now, selfishly, because I am a capitalist, I believe that we should be able to capture some of that market.
We should be able to say, we’ll help them. There’s no guarantee that they’re going to come back and nor is there an expectation, but just imagine if we’re the ones that help them understand how to run payroll.
I said, “We want you to focus on your business. If you make pizzas or if you have a restaurant, we want you to focus on what you do best. Let us do what we do best, which is run payroll, help you do time and attendance and help you with all of those other things. That’s what we do”
So, I think it’s important for us to extend our reach into the underserved communities, such that we can help raise the tide for all boats. That’s really the impetus here.
Say, if we do this the right way, DEI becomes much more holistic, so it’s focused on the economic empowerment.
If you do that by getting people great jobs, what do they do? Well, they go spend money in their communities. If they spend money in their communities, businesses grow. And if businesses grow, for us it’s a great thing, because that means you have more people to pay from your payroll systems and the like.
So, this ecosystem approach that I think is really critical and important, when we think about DEI.
Now, the other piece, Mark, that I’ll share with you about DEI is, I’ll share two other avenues of this.
One is the environment. Our environmental practices now, have become relevant in the DEI equation.
Let me back up and give you the broader view. Most companies talk about ESG, environmental, social and governance. The environmental piece is really critical. That’s where you have, what are you going to do for greenhouse gas emission reduction?
This S is all DEI. The G is board governance or governance of whatever programs that you take a look at. So, that’s something else you have to consider as you think about DEI.
We have practices to reduce greenhouse gas emissions. The good news for us is that, we don’t manufacture anything. Probably, our facilities and employees driving to work are our largest contributors to this. But what we also focus on is, what can we do to meet target? We put together plans to do that.
The last thing I’ll mention is what we’re doing as an organization, to make a difference, as we think about DEI and the like.
We have the ADP Foundation. We make contributions to a variety of 501(c)(3)’s nonprofits, to help support them in the communities in which they operate. So, there’s this holistic view that we have about, we can do well and do good at the same time.
Mark:
Bob, thanks very much. We appreciate your time today.
Bob:
Thank you.
Mark:
My guest today has been Bob Lockett, chief diversity and talent officer at ADP. This has been PeopleTech, the podcast of the HCM Technology Report.
We’re a publication recruiting daily. We’re also a part of the Evergreen Podcasts. To see all of their programs, visit www.EvergreenPodcasts.com.
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.
Image: iStock
Tech & Innovation Blog
NFTs: The Price of Bragging Rights
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)
noun
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.
NFT Buyers:
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.
NFT Creators:
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.
For more emerging tech topics, subscribe to our blog and receive monthly newsletters.
We’re hiring! Learn more about what it’s like working for ADP here and our current openings.
Podcast: ADP’s UX Leader on Data-Driven Design
Podcast: ADP’s UX Leader on Data-Driven Design
Transcript
Mark:
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.
Mark:
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?
Joe:
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?
Joe:
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.
Mark:
Do you have an example of that cognitive dissonance?
Joe:
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.
Joe:
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.
Mark:
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?
Joe:
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?
Joe:
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.
Mark:
Okay. Can you tell me a little bit about the technology that’s behind all this? What’s going on under the hood?
Joe:
You mean gathering the data?
Mark:
Not just gathering the data, but putting it together and presenting it in a way that’s usable.
Joe:
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?
Joe:
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.
Joe:
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.
Joe:
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.
Mark:
Well, how does this all fit into ADP’s efforts overall?
Joe:
In which respect? In terms of the UX, the experience model, this intuitive model?
Mark:
Yeah.
Joe:
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?
Joe:
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.
Mark:
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?
Joe:
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.
Joe:
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.
Joe:
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.
Mark:
What do you mean by paranoia around the data?
Joe:
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?
Joe:
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.
Mark:
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?
Joe:
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.
Joe:
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.
Joe:
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.
Joe:
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.
Joe:
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,
Mark:
Joe. Thanks very much. Really appreciate it.
Joe:
My pleasure, Mark. Thank you.
Mark:
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.
Tech & Innovation Blog
2022 Workforce Trends
Future of Work, Innovation, Culture
Accessible Video Controls
2022 Workforce Trends
[LOGO: ADP, Always Designing for People]
[TEXT] 2022 Workforce Trends
Diverse workers in a variety of settings.
[MUSIC]
[TEXT] Work is having its Moment
[DESCRIPTION] Workers in offices; a cluster of multi-rise buildings
[TEXT], What will work look like in 2022?
Employee Visibility Redefined.
[DESCRIPTION] A woman, a man.
[TEXT] Where and how people are working has changed
On-site, Remote, Hybrid
A man and a woman.
75% of global workforce changed how or where they live…
85% are among Gen Z
People data will replace physical proximity
Leaders will lean into trust-based approach
Workers who trust their team and leader are 7 times more likely to be strongly connected
People & Purpose Drive Culture
Connection will become a measurement of workforce culture
Strongly connected workers are 75 times more likely to be fully engaged
Diversity, equity and inclusion will evolve to drive measurable progress
More than 50% of companies with DEI analytics took action and realized positive impact – ADP DataCloud DEI Dashboard
Data & Expertise Power Resilience
Leaders will increasingly turn to data to identify gaps
Nearly 20% small-midsize U.S. companies report facing regulatory compliance challenges
Quality data will be key in providing confidence
Workers completed nearly 3 million health status surveys enabling a safer return to workplace – ADP DataCloud Return to Work Toolkit
Innovation Accelerates Growth
Global shifts will force new efficiencies, fuel productivity
Use of ADP Mobile Solutions increased more than 25% year-over-year
Skills based hiring surges transforming the talent landscape
28% workers report taking a new role since pandemic
Visibility
Culture
Data & Expertise
Innovation.
The Future of Work Starts
Now!
[LOGO: ADP, Always Designing for People]
Work is having its moment. Rapid changes have made way for a newly transformed workplace. What can businesses and workers expect in 2022? ADP identifies the top trends reshaping the future of work. For more insights, subscribe to the tech blog and receive monthly newsletters from us.
