Transcript of a discussion on how advances in infrastructure, data access, and AI combine to produce a step-change in human capital analytics and new business services.

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How Global HCM Provider ADP
Mines an Ocean of Employee Data for
New Talent Management Resources
Transcript of a discussion on how advances in infrastructure, data access, and AI
combine to produce a step-change in human capital analytics and new business
services.
Listen to the podcast. Find it on iTunes. Download the transcript.
Sponsor: Hewlett Packard Enterprise.
Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of
the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor
Solutions, your host and moderator for this ongoing discussion on digital transformation
success stories.
Our next big data analytics and artificial intelligence (AI) strategies discussion explores
how human capital management (HCM) services provider ADP unlocks new business
insights from vast data resources.
With more than 40 million employee records to both protect and mine, ADP is in a
unique position to leverage its business data network for unprecedented intelligence on
employee trends, risks, and productivity.
ADP is entering a bold new era in talent management by deploying advanced
infrastructure to support data assimilation and refinement of a vast, secure data lake as
foundations for machine learning (ML).
Stay with us now as we unpack how advances in
infrastructure, data access, and AI combine to produce a
step-change in human capital analytics. With that, please
join me in welcoming Marc Rind, Vice President of
Product Development and Chief Data Scientist at ADP
Analytics and Big Data. Welcome to BriefingsDirect,
Marc.
Marc Rind: Thank you, Dana.
Gardner: We’re also here with Dr. Eng Lim Goh, Vice
President and Chief Technology Officer for High
Performance Computing and Artificial Intelligence at
Hewlett Packard Enterprise (HPE). Welcome, Dr. Goh.
Rind

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Dr. Eng Lim Goh: Thank you for having me.
Gardner: Marc, what's unique about this point in time
that allows organizations such as ADP to begin to do
entirely new and powerful things with its vast data?
Rind: What’s changed today is the capability to take data
-- and not just data that you originally collect for a certain
purpose, I am talking about the “data exhaust” -- and to
start using that data for purposes that are not the original
intention you had when you started collecting it.
We pay one in six full-time employees in the US, so you
can imagine the data that we have around the country,
and around the world of work. But it's not just data about how they get paid -- it's how
they are structured, what kind of teams are they in, advances, bonuses, the types of
hours that they work, and everything across the talent landscape. It's data that we have
been able to collect, curate, normalize, and then aggregate and anonymize to start
leveraging to build some truly fascinating insights that our clients are able to leverage.
Gardner: It's been astonishing to me that companies like yours are now saying they
want all of the data they can get their hands on -- not just structured data, but all kinds of
content, and bringing in third-party data. It's really “the more, the merrier” when it comes
to the capability to gather entirely new insights.
The vision of data insight
Rind: Yes, absolutely. Also there have been advances in methodologies to handle this
data -- like you said, unstructured data, non-normalized data, taking data from across
hundreds of thousands of our clients, all having their own way that they define,
categorize, and classify their workforces.
Now we are able to make sense of all of that -- across the board -- by using various
approaches to normalize, so that we can start building insights across the board. That’s
something extremely exciting for us to be able to leverage.
Gardner: Dr. Goh, it's only been recently that we have been able to handle such vast
amounts of data in a simplified way and at a manageable cost. What are partners like
HPE bringing to the table to support these data platforms and approaches that enable
organizations like ADP to make analytics actionable?
Goh: As Marc mentioned, these are massive amounts of data, not just the data you
intend to keep, but also the data exhaust. He also mentioned the need to curate it. So
the idea for us in terms of data strategy with our partners and customers is, one, to
retain data as much as you can.
Goh

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Secondly, we ensure that you have the tools to curate it, because there is no point
having massive amounts of data over decades – and when you need them to train a
machine – and you don’t know where all of the data is. You need to curate it from the
beginning, and if you have not, start curating your data now.
The third area is to federate. So retain, curate, and federate. Why is the third part, to
federate, important? As many huge enterprises evolve and grow, a lot of the data starts
to get siloed. Marc mentioned a data lake. This is one way to federate, whereby you can
cut across the silos so that you can train the machine more intelligently.
We at HPE build the tools to provide for the retention, curation, and federation of all of
that data.
Gardner: Is this something you are seeing in many different industries? Where are
people leveraging ML, AI, and this new powerful infrastructure?
Learn How IT Best Supports
The Greatest Data Challenges
Goh: It all begins with what I call the shift. The use of these technologies emerged
when industries shifted from when prediction decisions were made using rules and
scientific law-based models.
Then came a recent reemergence of ML, where instead of being based on laws and
rules, you evolve your model more from historical data. So data becomes important
here, because the intelligence of your model is dependent on the quantity and quality of
the data you have. And by using this approach you are seeing many new use cases
emerge, of using the ML approach on historical data.
One example would be farming. Instead of spraying the entire crop field, they just squirt
specifically at the weeds and avoid the crops.
Gardner: This powerful ML example is specific to a vertical industry, but talent
management insights can be used by almost any business. Marc, what has been the
challenge to generate talent management insights based on historical data?
Rind: It’s fascinating because Dr.
Goh’s example pertains to talent
management, too. Everyone that
we work with in the HCM space is
looking to gain an advantage when
it comes to finding, keeping, and
retaining their best talent.
It’s fascinating because Dr. Goh’s
example pertains to talent management,
too. Everyone that we work with in the
HCM space is looking to gain an
advantage when it comes to finding,
keeping, and retaining their best talent.

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We look at a vast amount of employment data. From that, we can identify people who
ended up leaving an organization voluntarily versus those who stayed and grew, why
they were able to grow, based on new opportunities, promotions, different methods of
work, and by being on different teams. Similar to the agriculture example, we have been
able to use the historical data to find patterns, and then identify those who are the
“crops” and determine what to do to keep them happier for longer retention.
This is a big shift in the talent management space. We are leveraging vast data -- but not
presenting too much data to an HCM professional. We spend a lot of time handling it on
their behalf so the HCM professional and even managers can have the insights pushed
to them, rather than be bombarded with too much data.
At the end of the day, we are using AI to say, “Hey, here are the people you should go
speak with. Or this manager has a lot of high-risk employees. Or this is a critical job role
that you might see higher than expected turnover with.” We can point the managers in
that direction and allow them to figure out what to do about it. And that's a big shift in
simplifying analysis, and at the same time keeping the data secure.
Data that directs, doesn’t distract
Goh: What Marc described is very similar to what our customers are doing by
converting their call center voice recordings into text. They then anonymize it but gain
the ability to figure out the sentiment of their customers.
The sentiment analysis of the text -- after converting from a voice recording – helps them
better understand churn. In the telco industry, for example, they are very concerned
about churn, which means a customer leaving you for another vendor.
Yes, it’s very similar. First you go through a massive amount of historical data, and then
use smart tools to convert the data to make it useable, and then a different set of tools
analyzes it all -- to gain such insights as the sentiment of your customers.
Gardner: When I began recording use case discussions around big data, AI, and ML, I
would talk to organizations like refineries or chemical plants. They were delighted if they
could gain a half-percent or a full percent of improvement. That alone meant billions of
dollars to them.
But you all are talking about the high-impact improvement for employees and talent. It
seems to me that this isn’t just shaving off a rounding number of improvement. Marc, this
type of analysis can make or break a company's future.
So let's look at the stakes here. When we talk about improving talent management, this
isn’t trivial. This could mean major improvement for any cdanStaveMen66ompany.

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Rind: Every company. Any leader of an organization will tell you that their most
important resource is the people that work for the company. And that value is not an
easy thing to measure.
We are not talking about how much more we can save on our materials, or how to be
smarter in electricity savings. You are talking about people. At the end of the day, they
are not a resource as much as they are human beings. You want to figure out what
makes them tick, gain insight into where people need to be growing, and where you
should spend the human time with them.
Where the AI comes in is to
provide that direction and offer
suggestions and
recommendations on how to
keep those people there, happy
and productive.
Another part of keeping people productive is in automating the processes necessary for
managers. We still have a lot of users punching clocks, managing time, and approving
pay cards and processing payroll. And there are a lot of manual things that go on and
there is still a lot of paperwork
We are using AI to simplify and make recommendations to handle a lot of those pieces,
so the HR professional can be focused on the human part -- to help grow careers rather
than be stuck processing paperwork and running reports.
Cost-effective AI, ML has arrived
Gardner: We’re now seeing AI and ML have a major impact on one of the most
important resources and assets a company can have, human capital. At the same time,
we’re seeing the cost and complexity of IT infrastructure that support AI go down thanks
to things like hyperconverged infrastructure (HCI), lower cost of storage, capability to
create whole data centers that can be mirrored, backed up, and protected -- as well as
ongoing improvements in composable infrastructure.
Are we at the point where the benefits of ML and AI are going up while the cost and
composability of the underlying infrastructure are going down?
Goh: Absolutely. That’s the reason we have a reemergence of AI through machine
learning of historical data. These methods were already available decades ago, but the
infrastructure was just too costly to amass enough data for the machine to be intelligent.
You just couldn’t get enough compute power to go through that data for the machine to
be intelligent. It wasn’t until now that the various infrastructure required came down in
cost, and therefore you see this reemergence of ML.
Where the AI comes in is to provide that
direction and offer suggestions and
recommendations on how to keep those
people there, happy and productive.

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If one were to ask why in the last few years there has been a surge to AI, it would be
lower cost of compute capability. We have reached a certain point where it is cost-
effective enough to amass the data. Also because of the Internet, the data has become
more easily accessible in the last few years.
Learn How IT Best Supports
The Greatest Data Challenges
Gardner: Marc, please tell us about ADP. People might be familiar with your brand
through payroll processing, but there's a lot more to it.
Find, manage, and keep talent
Rind: At ADP, or Automatic Data Processing, data is our middle name. We’ve been
working at a global scale for 70 years, now with $12 billion in revenue and supporting
over 600,000 businesses -- ranging from multinational corporations to three-person
small businesses. We process $2 trillion in payroll and taxes, running about 40 million
employee records per month. The amount of data we have been collecting is across the
board, not just payroll.
Talent management is a huge thing now
in the world of work -- to find and keep the
best resources. Moving forward, there is a
need to understand innovative
engagement of that workforce, to
understand the new world of pay and
micro-pay, and new models where people
are paid almost immediately.
The contingent workforce means a work market where people are moving away from
traditional jobs. So there are lots of different areas within the world of payroll processing
and talent management. It has really gotten exciting.
With all of this -- optimizing your workforce – also brings better understanding of where
to save the organization from lost dollars. Because of the amounts of data, we can
inform a client not just on, “Okay, this is what your cost of turnover is based on who is
leaving and how long it takes them to get productive again, and the cost of recruiting.”
We can also show how your HCM compares against others in your field. It's one thing to
share some information. It’s another to give an insight on how others have figured this
out or are handling this better. You gain the potential to save more by learning about
other methods out there that you should explore to improve talent retention.
Once you begin generating cost savings for an organization -- be it in identifying people
who are leaving, getting them on-boarded better, or reducing cost from overtime – it
There is a need to understand
innovative engagement of that
workforce, to understand the new
world of pay and micro-pay, and
new models where people are paid
almost immediately.

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shows the power of the insights and of having that kind of data. And that’s not just about
your own organization, but it’s in how you compare to your peers.
So that’s very exciting for us.
All-access data analytics
Goh: Yes, we are very keen to get such reports on intelligence with regards to our
talent. It’s become very difficult to hire and retain data scientists focused on ML and AI.
These reports can be helpful in hiring and to understand if they are satisfied in their jobs.
Rind: That’s where we see the future of work, and the future of pay, going. We have the
organization, the clients, and the managers -- but at the end, it’s also data insights for
the employees. We are in a new world of transparency around data. People understand
more, they are more accepting of information as long as they are not bombarded with it.
As an employee, your partner in your career growth and your happiness at work is your
employer. That’s the best partnership, where the employer understands how to put you
into the right place to be more productive and knows what makes you tick. There should
be understanding of the employees’ strengths, to make sure they use those strengths
every day, and anticipate what makes them happier and more productive employees.
Those conversations start to happen because of the data transparency. It’s really very
exciting. We think this data is going to help guide the employees, managers, and human
resources (HR) professionals across the organizations.
Gardner: ADP is now in a position where your value-added analysis services are at the
level where boards of directors and C-suite executives will be getting the insights. Did
that require a rethinking of ADP’s role and philosophy?
Rind: Through our journey we
discovered that providing insights
to the HR professional is one
thing. But we realized that to fully
unleash and unlock the value in
the data, we needed to get it into
the hands of the managers and
executives in the C-suite.
And the best way to do that was to build ADP’s mobile app. It’s been in the top three of
the most downloaded applications from the business section on the iTunes Store.
People initially got this application to check their paystub and manage their deductions,
et cetera. But now, that application is starting to push up to the managers, to the
executives, insights about their organization and what's going on.
Providing insights to the HR professional is
one thing. We realized that to fully unleash
and unlock the value in the data, we needed
to get it into hands of the managers and
executives in the C-suite.

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A key part was to understand the management persona. They are busy running their
organizations, and they don’t have the time to pore through the data like a data scientist
might to find the insights.
So we built our engine to find and highlight the most important critical data points based
on their statistical significance. Do you have an outlier? Are you in the bottom 10 percent
as an organization in such areas as new hire attrition? Finding those insights and
pushing them to the manager and executive gets them these headlines.
Next, as they interact with the application, we gain intelligence about what's important to
that manager and executive. We can then then push out the insights related to what's
most important to them. And that's where we see these value-added services going. An
executive is going to care about some things differently than a supervisor or a line
manager might.
We can generate the insights based on their own data when they need it through the
application versus them having to go in and get it. I think that push model is a big win for
us, and we are seeing a lot of excitement from our clients as they are start using the
app.
Gardner: Dr. Goh, are you seeing other companies extend their business models and
rethinking who and what they are due to these new analytics opportunities?
Data makes all the difference
Goh: Yes, yes, absolutely. The industry has shifted from one where your differentiated
asset was your method and filed patent, to one where your differentiated asset is the
data. Data becomes your defensible asset, because from that data you can build
intelligent systems to make better decisions and better predictions. So you see that
trend.
In order for this trend to continue, the infrastructure must be there to continually reduce
cost, so you can handle the growing amounts of data and not have the cost become
unmanageable. This is why HPE has gone with the edge-to-cloud hybrid approach,
where the customer can implement this amassing of data in a curated and federated
way. They can handle it in the most cost-effective way, depending on their operating or
capital budgets.
Gardner: Marc, you have elevated your brand and value through trends analysis around
pay equity or turnover trends, and gaining more executive insights around talent
management. But that wouldn't have been possible unless you were able to gain the
right technology.
What do you have under the hood? And what choices have you made to support this at
the best cost?

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Rind: We build everything in our own development shop. We collect all the data on our
Cloudera [big data lake] platform. We use various frameworks to build the insights and
then push those applications out through our ADP Data Cloud.
We have everything open via a RESTful API, so those insights can permeate throughout
the entire ADP ecosystem -- everyone from a practitioner getting insights as they on-
board a new employee and on out to the recruiting process. So having that open API is a
critical part of all of this.
Learn How IT Best Supports
The Greatest Data Challenges
Gardner: Dr. Goh, one of the things I have seen in the market is that the investments
that companies like ADP make in the infrastructure to support big data analytics and AI
sets in motion a virtuous adoption benefit. The investments to process the data leads to
an improvement in analytics, which then brings in more interest in consumption of those
analytics, which leads to the need for more data and more analytics.
It seems to me like it’s a gift that keeps giving and it grows in value over time.
Steps in the data journey
Goh: We group our customers on this AI journey into three different groups: Early,
started, and advanced. About 70 percent of our customers are in the early phase, about
20 percent in the started phase, where they have already started on the project, and
about 10 percent are in the advanced phase.
The advanced-phase customers are like the automotive customers who are already on
autonomous vehicles but would like us to come in and help them with infrastructure to
deal with the massive amounts of data.
But the majority of our customers are in the early phase. When we engage with them,
the immediate discussion is about how to get started. For example, “Let’s pick a low-
hanging fruit that has an outcome that’s measurable; that would be interesting.”
We work with the customer to decide on an
outcome to aim for, for the ML project. Then
we talk about gaining access to the data. Do
they have sufficient data? If so, does it take a
long time to clean it out and normalize it, so
you can consume it?
After that phase, we start a proof of concept (POC) for that low-hanging fruit outcome --
and hopefully it turns out well. From there the early customer can approach their
management for solid funding to get them started on an operational project.
We work with the customer to
decide on an outcome to aim for,
for the ML project. Then we talk
about gaining access to the data.

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That’s typically how we do it. It always starts with the outcome, and what we are aiming
for this machine to be trained at. Once they have gone through the learning phase, what
is it they are trying to achieve, and would that achievement be meaningful for the
company? A low-hanging fruit POC doesn’t have to be that complex.
Gardner: Marc, any words of wisdom looking back with 20/20 hindsight? When it comes
to the investments around big data lakes, AI, and analytics, what would you tell those
just getting started?
Rind: Much to Dr. Goh’s point, picking a manageable
project is a very important idea. Go for something that
is tangible, and that you have the data for. It's always
important to get a win instead of boiling the ocean, to
prove value upfront.
A lot of large organizations -- instead of building data lakes, they end up with a bunch of
data puddles. Large companies can suffer from different groups building their own.
We have committed to localizing all of the data into a single data lake. The reason is that
you can quickly connect data that you would never have thought to connect before. So
understanding what the sales and the service process is, and how that might impact or
inform the product or vice versa, is only possible if you start putting all of your data
together. Once you get it together, just work on connecting it up. That's key to opening
up the value across your organization.
Connecting the data dots
Goh: It helps you connect more dots.
Gardner: The common denominator here is that there is going to be more and more
data. We’re starting to see the Internet of Things (IoT) and the Industrial Internet of
Things (IIoT) bring in even more data.
Even relevant to talent management, there are more ways of gathering even more data
about what people are doing, how they are working, what their efficacy is in the field,
especially across organizational boundaries like contingent workforces, being able to
measure what they are doing and then pay them accordingly.
Marc, do you see ever more data coming online to then need to be measured about how
people work?
Rind: Absolutely! There is no way around it. There are still a lot of disconnected points
of data, for sure. The connection points are going to just continue to be made possible,
so you get a 360-degree view of the world at work. From that you can understand better
how they are working, how to make them more productive and engaged, and bringing
It’s always important to
get a win instead of
boiling the ocean, to
prove value upfront.wae

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flexibility to allow them to work the way they want. But only by connecting up data across
the board and pulling it all together would that be possible.
Gardner: We haven’t even scratched the surface of incentivization trends. The more
data allows you to incentivize people on a micro basis in near-real time, is such an
interesting new chapter. We will have to wait for another day, another podcast, to get
into all of that.
I’m afraid we’ll have to leave it there. We’ve been discussing how global human capital
management services provider ADP has unlocked new business insights and services
from its vast data resources. And we have learned that by deploying the most advanced
infrastructure for AI, a new era is dawning for talent management.
Please join me now in thanking our guests, Marc Rind, Vice President of Product
Development and Chief Data Scientist at ADP Analytics and Big Data. Thank you so
much, Marc.
Rind: Thank you for having me, Dana.
Gardner: And we have been joined too by Dr. Eng Lim Goh, Vice President and Chief
Technology Officer for High Performance Computing and Artificial Intelligence at HPE.
Thank you so much, Dr. Goh.
Goh: Thank you, Dana. Thank you, Marc.
Rind: Great, thank you.
Gardner: And a big thank you as well to our audience for joining us for this
BriefingsDirect Voice of the Customer digital transformation success story discussion.
I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing
series of Hewlett Packard Enterprise-sponsored interviews.
Thanks again for listening. Please pass this along to your own IT community, and do
come back next time.
Listen to the podcast. Find it on iTunes. Download the transcript.
Sponsor: Hewlett Packard Enterprise.
Transcript of a discussion on how advances in infrastructure, data access, and AI
combine to produce a step-change in human capital analytics and new business
services. Copyright Interarbor Solutions, LLC, 2005-2019. All rights reserved.
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