March 11, 2022 • 50 mins
Our team sits down with Lisa Thee, CEO of AMP Solutions LLC, to explore how data innovation and artificial intelligence can drive positive societal impact. Lisa leads the Data for Good practice at Launch Consulting, working with top healthcare and technology companies to enhance digital safety, combat toxicity, and leverage AI for innovation. As an entrepreneur, she was the CEO and co-founder of Minor Guard, an AI-driven software company focused on protecting children both online and in real life. She recently delivered a TED Talk titled "Bringing Light to Dark Places Online: Disrupting Human Trafficking using AI." Lisa is also the co-author of Demystifying Artificial Intelligence for the Enterprise and the host of the Navigating Forward podcast. She serves on advisory boards for Engineered Medical Solutions, Humaxa, Spectrum Labs, and Marketplace Risk and is a board member of 3 Strands Global Foundation, an organization dedicated to human trafficking prevention and survivor reintegration. Lisa has been recognized among the Top Health and Safety Thought Leaders and Women in Business to Follow by Thinkers 360 and named one of the Top 100 Human Trafficking & Slavery Influence Leaders globally. Don’t miss this insightful conversation on the transformative power of data and AI. Hit play and join the discussion! 🎧
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:06):
Bedrop is an independent data science and aiphone specialized in
data driven business change. In this podcast, our guests help
us spread knowledge and experience with our listeners.
Speaker 2 (00:27):
Good morning, Lisa, how are you doing today?
Speaker 3 (00:30):
Good morning? I am bright eyed and bushytailed for a
long day ahead.
Speaker 2 (00:34):
Awesome. I'm freezing here. I've never said this, but it's
actually getting very cold. Even though it's Spain, we're in
the north and it's quite cold here. How are you there?
Speaker 3 (00:45):
You know, I'm in central California and it is thirty
five years so we're just above freezing ourselves. But it
should warm up towards the middle of the day and
I'll be able to see some Sunday skies and take
my dog for a walk.
Speaker 2 (00:59):
That sounds pretty good. Okay, So tell me, how does
your calendar look like for today? I mean, what what
goals or meetings and things do you have for the
day today.
Speaker 3 (01:09):
Sure, I'm kicking off some accountability work with some of
my coaches around the okay hours that I've set for
this year right after I wrap up this meeting, so
we're going to be talking about progress to those goals
we check in once a month. From there, I am
going to meet with a client in healthcare that is
(01:29):
focused on creating a more frictionless member experience for patients
in California, and we will be doing some interesting deep
dives into how do we help them sell some of
their business challenges using technology.
Speaker 2 (01:45):
Okay, yours that you've mentioned obviously you run your own company.
So the what you were talking about just now among
the the healthcare client, tell me more about it. I mean, what,
what's this company? What do you do? Et cetera.
Speaker 3 (02:03):
Sure, So one of my clients is Launch Consulting. They
are a firm that is focused in on doing boutique
consulting around digital transformation. I lead their Data for Good Practice.
So I like to be at the intersection of influential
people and innovation to address large social justice issues at scale.
(02:25):
Those can either fall in a few categories. Typically when
there's regulated data involved that needs to be unlocked in
order to make sure that outcomes for people are more positive,
but needs to be done in a cyber secure and
holistically considered way. We apply that through competential computing and
(02:47):
zero trust AI systems. With that in mind, I work
a lot across healthcare, tech companies and government institutions as
well as financial services businesses that need to make better
decisions around things like anti money laundering, finding criminal networks,
or improving the patient experience in healthcare and making sure
(03:11):
that we accelerate delivering the best quality of care at
the most affordable cost to serve the most benefit for society.
So this is in healthcare today. Other days of the week,
I'm talking with large social network platforms, or with financial
(03:31):
services companies or maybe some agencies with a few letters
and their titles to improve safety and benefit society and
the digital safety side of our practice.
Speaker 2 (03:42):
Everything relates to AI. As you know, the foundational you'd
say base and then you willed on top of that
services or you'd say advice on how different companies can
provide of or service avatory technology wrote to change with
(04:06):
ethics and good practices in mind.
Speaker 3 (04:08):
Right, Yeah, I think that's a great way to say.
At the end of the day, you know, AI is
a tool, not a solution. So I spend a lot
of time uncovering the business problems that are people are
trying to address, leveraging digital transformation and technology that can
be AI that can be different types of data analytics,
but trying to make the invisible visible in order for
(04:29):
them to be able to accelerate towards their business outcomes.
I choose to focus in on things that tend to
benefit society more. That's where I want to spend my
expertise at the stage in my career, and so I've
been lucky to find clients that are aligned with that
vision and are working alongside me to make sure that
(04:49):
they can deliver on what we have identified. So I
do that with some really innovative startups, which I really enjoy,
goes back to my entrepreneurial roots. And then I some
professional services companies that can scale up to meet the
customers needs for breaking through and having a better human
experience using technology.
Speaker 2 (05:10):
Very interesting. So the name of your business is Amplified Revenue.
If I'm not mistaken right.
Speaker 3 (05:15):
It is Yes, AM Solutions also operating under Amplified Revenue.
Speaker 2 (05:20):
Uh. I understand. So one of your clients you've mentioned
this Launch Consulting group, and I really liked that name
of that business unit, which is Data for Good, isn't it?
I mean, is it a business unit by itself or
it's yes, the name of a set of services that
you provide around people first transformation kind of services.
Speaker 3 (05:44):
It actually has its own services and solutions group. We
made a big bet a couple of years back, thinking, hey,
we've made really great inroads in technology companies on leveraging
data and analytics to improve business outcomes. What if we
pointed that towards social impact issues. Could we make a
tenable business out of that? And what we learned is yes.
(06:07):
So we put the do Good to Do Well opportunity
to test and we found there is a sustainable business
model in that. So I lead that practice for launch
and they've made significantly more investments as we've tested and
shown that the market is pulling for that. That double
line revenue impact that do good to Do Well not
(06:29):
just focused on the profits, but also on the human impact.
It aligned very well with their values and we were
able to show that it's not just a hobby or
a gift back or corporate social responsibility. It can bringing
leading edge technologies to solve big, hairy problems that have
impacts to the bottom line. So kind of blending my
chief revenue officer experience and fifteen years of sales and
(06:52):
development with being able to bring technology, innovation and disruption
to make things work more seamlessly for everyone to benefit.
Speaker 2 (07:01):
It isn't only about saying that you want to do good,
it's also that you know, it's a way of demonstrating
that the services and solutions that you're building or delivering
really add value. So following that people first approach, you're
ensuring you are bringing all of the views into account
(07:24):
and anything that again you want to build or deliver
to and with your clients really makes sense because it
comes from the individual needs and perspectives. Right. So it's
it's a way of you know, doing right, but also
because you are taking everyone and all of the estateholders
views into into designing a solution.
Speaker 3 (07:46):
Right. Yeah, I have the luxury of working with an
incredible delivery team. We have experts and research, we have
experts and user experience, we have experts in PR and
marketing and telling our story, and we have a full
slee of technical and engineering and dev staff. So I
can act almost like an orchestrator, pulling the right talent
(08:08):
at the right time to come in and almost an
army Ranger style engagement where we can come to the
client where they are today, help them envision where they
want to be, and then bring in the right experts
at the right times during the development process to get
them to their vision and an agile and quick way
(08:29):
that allows them to see incremental value all the way
through the process. And so I really like working for
an organization that has roots in the military in Americas.
The investors and the founders all came from the armed forces,
and so they bring that mission lens to how we
do things, and that making sure that we leverage everyone's
talent at the right time to be able to help
(08:51):
get large companies unblocked from the innovation that they're seeking
to implement. As we know, a lot of times digital
transferations failed not because they weren't good products or good
services that were designed, but because the humans weren't brought
along in the process. So I love that we focus
a lot more upfront about making sure that we're doing
(09:11):
the empathy mapping and doing the journey mapping and really
understanding how do we make the user experience as frictionless
as possible so people don't come along because they have to,
They come along because they want to and they believe
in it.
Speaker 2 (09:24):
And it's also related to managing change expectations from those people,
right because at the end of the day. Any deal detail, transformation,
growth map is going to somehow impact the day to
day and lives of those you know that are or
form the teams on the other side. So it's important
(09:45):
that they feel how would they say, involved in the
design of that solution right and they they don't know.
Speaker 3 (09:53):
What their pain points are. You're not building for them,
You're taking your vision and imposing it on them. I
think it's the important that these teams and their experiences
are weighed and valued. There's always a nuance when you
come into a new client to understand what they're managing through,
and I think it's really critical that you leverage frameworks
(10:16):
to get information, but you're really hearing from the client
mostly what do they need and how do we get there?
And I don't believe innovation is held at the highest
paid persons level. I think you can sess it out
from all different levels of the organization and come back
with something that's much better than where you started. And
(10:37):
with that in mind, having those conversations allows the alignment.
Because almost any project that we do in digital safety,
for example, across as multiple organizations, has a very complicated
stakeholder landscape. It mirrors a lot of the privacy and
cybersecurity challenges the companies face. So there's legal operations defining
(10:57):
the policy, there's engineering teams trying to build tools to
identify when policy has not been aligned with, and then
there's operations teams that are trying to take action when
action is required based on people not following terms of service.
All of that has impacts across multiple business units, multiple
organizations and making something really tangible for people to understand
(11:22):
in terms of why we're all going to do this.
It can be really valuable in terms of making sure
that you get commitment and everybody feels like they're winning
alongside this.
Speaker 2 (11:33):
Change, because sometimes you may be building something you know
for them to do better, but if you don't take
their view into account, you're just as you said, imposing
your viewing them rather than adjesting those or that technology
for it to be a tool for them to you know,
(11:54):
improve or do better in their day to day. But
I'm curious because before you were discussing that data for
good practice and the aim that that business unit or
practicing itself has, and I'm curious if apart from the
current work that you do, you also focus on what's
coming in terms of you know, upcoming regulatory frameworks, because
(12:20):
I mean this goes back to a few months ago
when I was reading that Europe I mean the government
at a European level was preparing strong regulatory framework in
regards to data privacy, obviously everything that relates to AI, explainability, transparency.
(12:43):
So obviously there is an opportunity in doing things the
right way because if you do things the right way
by including everyone your project, your brains will have a
better row. But is there also an opportunity in you know,
(13:04):
being prepared for when this framework will be mandatory instead
of you know, waiting for the finds to come to
add yest your processes to that. And this relates to ethics,
This relates to data management. This relates to many different
angles and areas, but is data would also focus on
(13:25):
data fuit also focusing on that. Are you through amplified
revenue also focusing on that, or you're waiting for moularity too.
Speaker 3 (13:35):
Yeah, so launches practice around data for is absolutely focusing
in that area. And we do have a point of
view that the digital safety will be the new cybersecurity.
You can spend twenty years building a brand and a
bad actor with bad intent you generate user generated content
(13:56):
on a platform could really turnish your brand quite quickly,
and so I think it's been a bit unregulated for
the last twenty years. I think there's been some legislative
frameworks in place with this Section two thirty Communication Decency Act,
which didn't require companies in the United States to be
accountable for what third parties posted on their platforms. This
(14:18):
was really important for the growth of the Internet at
the beginning, But that legislation is about twenty years old now,
and I think the people that wrote it in the
nineties could never have envisioned the world that we live
in today. I know I certainly couldn't have when I
was living in the nineties, and so there's some opportunities
to close some gaps. I think it mirrors other technology
(14:39):
innovation curves. If you look at the automobile, you had
to have a certain number of cars on the road
and a certain number of accidents before safety requirements became
something that's mandated. And I think that Europe has traditionally
been the leader in thinking through policy impacts around cybersecurity,
and I think they're now leading in safety and I'm
(15:00):
really excited to see where things move from here, because
I think the level playing field that this gives is
all multinational companies will be playing by the same set
of rules, which will help to focus the innovation versus
keeping it ambiguous. And I look forward to seeing all
the technologists that I had the luxury of working with
that are embedded in all of these companies in the
(15:22):
NGO landscape, that have visions for what can make this
a better user experience for everyone, and elevate brands have
something to focus on in terms of helping their executive
leadership team understand why it is critical to innovating this space.
I don't think we have a technology problem. I think
(15:43):
we have a lack of We have an education problem
of why this matters. And unfortunately I don't have the
luxury of pretending that I don't know what happens when
things go wrong on the Internet. There's so many conversations
around end to end encryption, privacybersecurity, and I don't think
people understand the implications when you don't allow law enforcement
(16:08):
to have any kind of say in protecting the most vulnerable.
Whose privacy is more important is that the eight year
old crying victim who is having their images be rewatched
over and over again of the worst day of their
lives for the enjoyment of adults, or the adults that
are consuming that content and don't want to be held
(16:29):
accountable for breaking the law. There's just a lot of
ethics challenges that come up that most people don't have
to think about. But I've been multiple years in this
space now, and i know that we're not talking about
really fringe use cases. There is enough accidents on the
(16:50):
road that we need to start having better protections in
place to protect our legacy and our humanity. It's really
not around silencing voices. It's about making sure that we
have a society where we can all agree that these
are some common core values we're just going to protect.
Speaker 2 (17:07):
Yes, I mean also the result the reason I was asking,
it's just because I mean ourselves as Bedrock, as a
professional services business, we do see an opportunity, a market
opportunity when it comes to advising companies on how to
do things the right way. But at the same time,
(17:28):
other players may feel that if they if they keep
doing things. I'm talking maybe about the media industry, media
as marketing industry, the way they're doing things now if
they can continue for a few more months, a few
more years to cooperate in the same way they've done
it for I know, a couple some decades or maybe
(17:52):
one decade. It is you know, they can extend the
operating model and it is are more beneficial as it
is now than transitioning towards you know, a new impost
model where they have less freedom. I don't know I'm
limiting in using this example, right because this is related
(18:16):
to privacy, data, privacy and everything. But it's about balancing
what's an opportunity for some and what's a burden for others,
and maybe prefer things the old way, right. But I'm
just curious, as you know, to ask you, because you're
involved in on different fronts, working for different companies, different
industries and everything. Who do you think is looking forward
(18:38):
to this and maybe who think it will be a
challenge for them to adapt.
Speaker 3 (18:44):
I think that some companies have more technical depth than
others to overcome. I think some companies have different expectations
of their user base than others. Not all environments are
created the same. You don't expect to have the same
behavior requirements in the library that you do in a nightclub.
So I think there are different incentivestructors based on the customers.
(19:07):
I also think there's different levels of maturity of the businesses.
I think when companies are newer, they tend to have
a higher risk profile because they need It's a grow
at all costs kind of model. Whereas once they start
to mature and recognize their responsibility and the ecosystem and
some of the risks that they're bringing on to their
company by allowing certain behaviors, it no longer justifies that
(19:33):
point of view, and that's a maturity curve that people
are going through. So I definitely see customers that are
leading in this space. We are lucky to have a
footprint at some of the more mature companies that are
really leading in terms of making sure that they are
aligning ethically. One I really enjoy working with is Microsoft.
(19:58):
I think they've had a really strong position in this
market for many many years of not only doing the
right thing from themselves, but open sourcing that technology does
small and medium sized business to encourage them to do
it as well. That I feel like their actions align
with their words, and so that's a customer I really
enjoyed collaborating with I think that some of the newer
(20:20):
companies that just didn't have as much experience of understanding
what can happen when this goes uncheck at scale, are
now coming and seeking more information. I'd love to see
the growth of these industry collaboration groups because everybody recognizes
that these digital safety problems, including livestream terror events, hate speech,
child grooming for exploitation, child sexual abuse material, all these
(20:46):
regulated areas, it's a cross platform problem. Criminals don't pick
one company and be like, I'm going to act bad here,
but I'm going to act great everywhere else. So it's
really looking at the networks and understanding who's causing the
most harm, and let's focus our resources there to make
sure that we can protect as many people as possible,
(21:09):
whether that be on the banking side with anti money laundering,
whether that be on the government side with you know,
anti human trafficking initiatives or anti terrorism initiatives, or whether
that be on the platform side of making sure that
we can identify and take down user generating content that
violence terms of service as quickly as possible.
Speaker 2 (21:30):
And we need to refair with some companies, businesses and
even leaders of these companies because maybe they've not inherited,
but they've built a business model around something that's far
or somehow far from technology. And they may be big, right,
maybe one thousand employees, And for them to transition towards
(21:54):
an operating model that takes all of these changes and
you know, frameworks into a is hard, and they don't
have that expertise either, and it's completely different, I mean,
living that situation in the resus. It's completely different to
you know, an emerging business that was built and born
at the core of I don't know, maybe data science, right,
(22:18):
or data technology or whatever, location intelligence. I mean, for them,
it's way easier to navigate these changes, firstly because they
are smaller and secondly because they are more used to
you know, that this detail ecosystem. So I guess there
is a whole window for opportunity for whoever it is
(22:40):
navigating this. So I really agree you are at the
forefront of you know, many many opportunities.
Speaker 3 (22:49):
I just want to clarify the reason I'm passionate about
doing this work is because I had I was an
a solution group owner at Intel Corporate, so I got
to see the broader landscape of what everyone's doing and
work on the leaders, and then I ran my own
startup developing software does this problem. So what I find,
(23:11):
what my ideal clients are are people that have connected
to wanting to build a legacy of doing something that
they will be proud of when they retire, that are innovative,
that are willing to be to be tenacious about helping
to keep something that de risks the company on the
(23:33):
forefront of everyone's mind. And when I find those leaders,
those are the people I want to settle up right
next to and help them locate where their data is,
understand the maturity of it, and understand their processes for
how they can get closer to the goals of trust
and safety alignment. Because I don't know anybody that works
in that field that shows up to do a haphazard
(23:55):
job any given day. But when the volume of threats
continues to grow exponentially, you need experts that live and
breathe the stuff. And it's a pretty small community of
people that choose to focus and on the hardest parts
of the Internet. And they're all my heroes.
Speaker 2 (24:12):
To be honest, I completely agree. And it does make
a difference to work on your day to day with
with someone that you're aligned with, right, that you share
the same values and level of thinking, and that you
want to do things you know to improve the lives
of others to make this a better world. And it
(24:32):
really sounds I wouldn't say it really sounds hopeful, but
you know, it's motivating to do things that you know
are going to improve how others go to the offices
and do their work. I think it's I think it's
really good and it's really rewarding. I'd say, I'm curious,
you know. I mean, you already mentioned that you started
(24:55):
to be exposed to this in the previous role that
you had, but I'm curious to know how you also
started to be immersed in AI and maybe data science
in this kind of field in the past. Was it
in the same role that you've mentioned, I mean, how
did you get to the forefront of technology evolution? Sorry
(25:15):
to say, yeah, so.
Speaker 3 (25:18):
I started my career in the technology industry. I've worked
across multiple sectors. Back in twenty fifteen, I had started
to get exposed to some of the work that was
happening around precision medicine and being able to leverage known
types and treatments for cancer patients across multiple medical institutions
(25:40):
to find the right path forward for having impaved outcomes
and more personalized cancer treatments. And my thought was, after
traveling the world and being the thirty six countries as
an IT leader early in my career, how could we
apply these same kinds of techniques leveraging AI and data
to improve outcomes for marginalized women. And so it was
(26:01):
a fairly naive journey. But what I found was there's
a lot of technologists out there that are really looking
to do something that they really believe in that gets
some out of bed in the morning in addition to
maybe what they get paid to do. And they were
very willing to lend their expertise to passion projects. And
(26:21):
so we ran it that way for a couple of years.
We worked with some startups, we worked with some across
the industry with Google and Microsoft to try to improve
the situation as it was. And the more and more
I learned about the topic was the same time, the
more and more I learned about AI technology, it was
saturated in data scientists and principal engineers and building data
structures to be able to find the pearls of wisdom
(26:44):
in the oceans of data and being exposed to a
lot of entrepreneurs that were leading and had a mission
focus to their company, not just a profit focus. Obviously
profit had to be part of that, or it would
have been a nonprofit, or it would have been a hobby.
But finding that intersection point where companies can accelerate innovation
(27:05):
with AI while solving something that needs to be addressed
was really appealing to me. So I really love the
intersection of helping machines do what they do uniquely well,
which is math and repetition and finding patterns, and partnering
those with subject matter experts in humanity, like the investigators
(27:27):
that are going and recovering missing children, being able to
help humans do what they do uniquely well and cut
out a lot of the mundane and repetitive tasks, and
bringing the right information to them at the right time
so they can shine in their area of expertise. I
really believe in the two key approach. I think it's
(27:48):
going to be how we are able to solve some
of the problems that we faced since the dawn of time.
Speaker 2 (27:54):
I know that you that you have some background, maybe
either the engineering degree when you were in university, did
this help somehow to better transition into everything that relates
to AI? Because obviously you can be really interested in,
(28:16):
you know, doing work in this field, but sometimes if
you don't have I'm just asking analytical background or a
technical background, it's harder for you to really understand the
implications of you know, maybe some algorithms, some work that
these machines may do that's repetitive or not. But did
(28:36):
this background help at all or you feel it it
wouldn't be needed to be doing the world that we
were doing these days?
Speaker 3 (28:44):
I can answer that two ways. So wasn't it an advantage?
Speaker 2 (28:47):
Yes?
Speaker 3 (28:48):
I have a degree in industrial engineering and operations management.
So when you look at how data science has evolved,
Number one, data science and AI wasn't a field that
most people were to prepare for when I was in university.
It was around, but the compute power hadn't accelerated to
make it at the scale of usefulness that it is today. Yeah,
(29:10):
So being able to look at a workflow of a
process and understand how to optimize that process and be
you know, having to take calculus for and statistics and
all of those things absolutely as an advantage for understanding.
But mind you like that that kind of skill set
had azure feet of me for almost two decades by
the time I approached this. So I think being able
(29:31):
to think like a problem solveregn an engineer makes it
easier to walk into this field. But I have brought
people into the companies that I've run straight out of
data science boot camps with backgrounds in counseling or backgrounds
in romance languages, that have the tenacity to want to learn,
and we have been able to see people bring such
(29:54):
cool innovation to the forefront of AI by bringing that
broader background, because it really is a combination of psychology,
computer science, and statistics and math that come together to
figure out how to address bigger problems. But somebody that
has lived experience of problems that are trying to be
(30:17):
solved can sometimes be the most effective in these fields
because it's really how they look at the problems and
are able to understand the impacts that they have to
society and businesses that help shape the right focus. You
can always build a team around you that can complement
any deficiencies. There are tons of roles within this ecosystem
(30:39):
that benefit from vision and leadership you don't have to
be hands on keyboard doing you know, coding and Python
to add value. And I'm a testament to.
Speaker 2 (30:48):
That, So I completely agree. And actually, when you when
you're building, designing, or implementing a solution for a client,
regardless of it being a machine learning model or just
a data visualization bid, having that perspective from someone that
(31:08):
isn't technical but has a certain degree of creativity or
has been exposed to the arts area in some sort
of way. Just having a different kind of mindset helps
to provide a better service, to provide a better solution
to the client. And it happened to us we drastically
changed on how we were delivering preits when we started
(31:32):
to bring the capabilities of someone who had a background
in psychology and was able to, you know, use the
same thing in framework at the start of a very
product to navigate you know, those needs of these stakeholders better.
So I completely agree that, you know, it really helps
(31:54):
when you are leading this kind of AI data initiatives
to have that solid analytical background, whether that's from engineering,
computer science, whatever it is, it really helps because I
think you're able to understand what's happening in the back end,
and you can translate that and you can be a
(32:14):
midpoint stayholder when it comes to translating things to everyone
around you, Right, I think that's a very valuable skill. Actually,
to me, what's important is to have someone in the
team that can also I mean, who can lead the
technical team and speak with data scientists and data engineers
(32:37):
about the code itself if needed, but that it's also
able to translate that into how that's going to impact
the business, and also so that those around him from
the business side understand this is a tool and a
mean to an end, as you said before. But because
but these people are very special. I mean the background
(32:58):
that you have on how you've navigated, you know, the
ward that you do now makes you very valuable to
any organization. I'd say that. I mean, obviously this is
my opinion.
Speaker 3 (33:08):
Well, I appreciate that. Yeah, as a founder of a
company myself stapping up in these areas when we were
building technology for Minor guard, which is now reflected in
products including chromebooks from Lenovo or you know, in the
Apple iPhone around child safety, I actually had a better
experience hiring the people that had more broader business analytics
(33:32):
backgrounds that upskilled in data science. We brought our data
scientists in that took our product to the next level
straight out of a boot camp and Frank she's now
the engineering manager for cloud Flair globally for Trust and
I have had more luck working with people that had
the commitment to the business impact and the tenacity to
(33:58):
learn and grow and deliver than I have with the
people that have come from very credential schools that have
a background in writing research papers, which a lot of
the data science community is because you have to have
that hunger to see the impact in the real world
and know how to move organizations and be politically savvy
(34:22):
about how to keep this on the forefront of people's minds.
And that's not engineering, and that's not coding. That's grit,
that's vision, that's tenacity, and that's helping people make the
invisible visible. And everybody has a role to play. Every
(34:42):
skill is needed.
Speaker 2 (34:45):
To be fair with everyone here. Not everyone wants to
be you know, client facing or trying to translate those
technical needs or you know, the technical insights of how
back end works. I mean, there is people that are
very capable of, you know, doing an excellent work, but
(35:06):
they want to do it themselves and they want to work.
I wouldn't say in isolation, but you know what I mean, right,
they are maybe they feel more introvert than they do
a tremendous amount of work and also of really high quality,
but they don't have those traits and they don't want to,
you know, to improve on the area. And that's perfectly fine.
But I really find, I mean from my own experience,
(35:30):
that's very hard to find those roles in between that
can coordinate the business vision, that can translate the technical needs,
the functionalities of a product, and to have that overall
view the business acumen all together. I mean that that's
(35:52):
a very valuable set of skills and that to me,
that's the hardest to find, regardless of IQ or regardless
of the background. I mean, yes to gain and to
being able to know navigate all of those areas. Okay,
before we close off the reason we.
Speaker 3 (36:13):
Are we only got to eleven am on my agenda
for today. Do you want to talk about the rest
of the rest of today?
Speaker 2 (36:19):
Looks like I know, I know, there is so many,
so many things we can discuss. Okay, tell me briefly,
what else do you have?
Speaker 3 (36:30):
So we have a lunch of the venture capital firm
in town talking about potential help across their portfolio. I
really enjoy functioning as like a advisor for revenue across
portfolios and helping companies get unstuck, especially as they're scaling
from seed to series AH in that early stages there,
(36:51):
it's always fun to help operators go faster and see
through their vision more exp the I then have a
meeting with a founder that I advice for in the
HR tech field. She runs a company called Umasa, and
they are looking to tune their product market fit with
(37:15):
marketing and sales adjustments. They've had a lot of success
coming out of five hundred startups, and now it's time
to put gasoline on the fire, which is really exciting.
And then from there I'll be working with another colleague
of mind from women in Data I believe you've interviewed
Sadie Saint Lawrence before. We're doing some envisioning for a
large apparel company around how to get more women into
(37:36):
data science roles and leadership. I really applaud their diversity initiatives.
We don't have a signed contract yet, but we have
a proposal for their VPS that lead AI and mL
around this topic in a couple of weeks, so hopefully
we can help them to determine where they are and
a path forward to what their vision is. They're doubling
and tripling their workforce un data Science, you're on here,
(37:59):
and they want to make sure that they have representation
at the table across genders and across marginalized groups, and
I think that's what's going to be help help them
to win. It's a very large brand you're all aware of,
so stay tuned. I will announce it on LinkedIn when
I can. If we are able to move forward.
Speaker 2 (38:17):
I'll be paying attention. And that's actually a kind of
hard thing. I don't know if it relates obviously to
STEM subjects being more I don't know. I would they
say whether they significantly have a stronger male presence, but
it shouldn't be that way. So do you feel or
(38:38):
maybe you can advise I don't know. We have a
strong relationship with the university here ourselves as pet Truck,
and we don't really know how to advise them on,
you know, attracting more women into joining STEM subjects, and
this can be anything, right, maybe the engineering side of
(39:00):
some of the pure sciences like mathematics physics now are
gaining more attraction because it feels like data science. But
I think, I don't think that universities have found the
right recipe for balancing genders here, And I don't know
if you have a piece of advice here. We know
(39:22):
and we are here today because Saydy from Women in
Data introduced us and probably see it has a comprehensive
view of things, probably much more than me, much weter
than me. But I'm curious to know how could we
advise You know, those academia leaders on balancing things, Well, if.
Speaker 3 (39:43):
I'm not mistaken, coming out of academia, you actually have
a decently balanced gender pool to work with. It's more
as people advance through their careers that you start to
see what I've heard referred to as the leaky pipeline.
So you come in with a nice gender balance in
the recruiting process of recent college graduates, but as time progresses,
(40:05):
the opportunities for advancement are maybe not equally distributed, and
then you know, you start to see attrition. And that's
why I'm so passionate about what Hu Maxa has focused on,
which is how do you make sure that people can
have opportunity to continue to grow. And you know, implicit
bias isn't the only determinant on who gets the chances
(40:25):
to prove what they can do and where they can grow.
With the work that Sadie and I look at, we're
really focusing in on helping people understand what you can
do with this background and how do I think where
we haven't appealed to in the hard sciences is the
vision of why it matters. And so that's why I
(40:48):
put a lot of energy into doing more public things
like this. Frankly, I was not known by anyone four
years ago. It wasn't until I launched my own company
that I realized that there is such a lot of
representation of women leaders in this field that live a
lifestyle that I can honestly pass the white face test
(41:08):
and tell my daughter she might want to follow in
one day. I'm still on my first marriage. I spend
time with my elementary school aged children every day, and
I choose to focus on working in things that really
align with my almost I'll say it my calling. There's
a reason I'm here, and it's not just to toot
(41:31):
my own horn. Is to make sure that we are
lifting the voices of all the folks that haven't had
a chance to participate. And there's no point at getting
a seat at the table if you're not changing the
conversation a little bit and contributing. And I think women
and diverse people have not had the opportunity to be
shaping the future of tech at the level that will
(41:54):
benefit us all as a society. They have a different
lived experience, a different perspective, and can see around corners
that you can't get in homogeneous groups. And I'm really
passionate about being a visible person out there because I
don't think you can be it if you can't see it.
(42:14):
And I don't think the next wave of innovation is
going to come from the people that we've create this
first wave of innovation. I have a huge, huge hope
for gen Z. The way they look at problems and
balance revenue and societal impact and progress and social justice,
(42:38):
it's just so inspiring to me. I think we've seen
so much that that generation can do, and I really
want to be out there paving as many pass as
I can for others to follow, not to do the
work that I do, to do the work. They're uniquely
focused on Is it animals, is it the elderly, is
it the environment, is it human trafficking, whatever it is,
(43:02):
you know what you want to see change in the world.
Math and science and data is the fastest way to
make that change real. And that's what I think we'll
call to different groups of people. Because technology is going
to disrupt everything. We've seen it happen in multiple industries.
It's going to continue, it's not going to slow down.
(43:24):
And if those voices aren't at the table, we're going
to create a society that none of us want to
live in.
Speaker 2 (43:32):
It is the same as when we were discussing before,
the way you build the right products for clients and
the way you deliver services for clients, right, the same
applies to society and the technology that's going to benefit
them supposedly, So you do need to, you know, to
have everyone respected through that process. And the pace that
(43:54):
we technology advances may even increase, right in comparison to
what we've seen till now. And obviously many industries, many
business models have changed, have been altered because of what
has appeared recently. So I really agree with everything that
you've said. Okay, so how far are we with your
(44:17):
agenda because we have five more minutes and.
Speaker 3 (44:21):
You take me the time to pick up my kids
from school and get them, get my daughter the theater,
and my son do locker and hopefully will my husband
has I believe it's smoking some try tip tonight for dinner,
which will be a really nice treat, and we will
(44:41):
huddle up and watch a family show together to wrap up,
put the kids to bed to day and probably collapse ourselves.
Speaker 2 (44:48):
Sounds like a perfect day to be honest, and if
not more important, I mean those last time slots of
the day, I'd say are very important, right, family, and
I'm spending time with the loved ones. I really value
that on the more time or the more the older
I get, the more I value time with loved ones
(45:10):
and friends and you know, having quality time with them.
Speaker 3 (45:13):
Yeah, I find my creativity and leadership are churched. And
the times when I've turned off now when I'm activated
and I've spent I made a lot of errors in
my thirties of trying to always be on and already
always service every master. But with you know, experience, you
glean some wisdom and I think more above it said
(45:35):
at best is time is the only resource you can't
buy more of, you can't get back, and you have
to own your time and choose how you use it
or you're never You're never going to be the leader
that you want to be. And so I really guard
(45:55):
my time around my family time, my time to walk
my dog and see that tops of the trees and
the blues of the sky, because my best ideas on
my best creativity come in the recharge. They don't come
in the action.
Speaker 2 (46:09):
I completely agree. I mean, and if they come doing
your focused time at work, it doesn't really make sense
because you should be doing something else. So I completely agree. Okay,
so again, you're here because Sadie made an introduction. I
think it was by email, but I'd be curious to
know if you would recommend me someone to join me
(46:30):
in another data stand up podcast episode, or maybe you
have someone in mind or you want to give you
that thought I do.
Speaker 3 (46:39):
I highly recommend you reach out to Edward Dixon, somebody
I collaborated with it intel that he runs his own
consulting firm. Now he focuses in a healthcare innovation as
well as child safety work as well, but he is
very technical and he is the peanut butter in my
jelly in terms of the driving a vision of how
(47:03):
we can see impact in the world. He's one of
the people that have been influential across my corporate journey,
my entrepreneurial journey, and someone that I know is making
a huge impact in the public sector on making sure
that we can provide better tools for the people that
(47:23):
are protecting us, not just for uh, the early adopter
innovator criminals getting to it quicker. So I highly recommend
Edward Dixon out of Cork, Ireland. He runs his own
consulting firm and I'd be happy to make an introduction.
Speaker 2 (47:42):
It's called Breaker Awesome, I mean really sounds promising. And
in terms of a book I read or maybe yes,
you know, newsletter that you consume every week, every month, anything.
It doesn't have to be related to data or AI,
maybe just something related to productivity, whatever it is, but
(48:03):
that you recommend well.
Speaker 3 (48:07):
Edward and I contributed a chapter together to a book
that came out last month called Demistifying Artificial Intelligence to
the Enterprise. Wow, that's available in bookstores now, So I
recommend that as a read. It was really fun to
collaborate on that kind of work. And it brings industry
leaders from all different sectors talking about how they're applying
(48:29):
AI to accelerate digital transformation. And so that's what I
would recommend.
Speaker 2 (48:35):
I will read it. I mean all of the recommended reads,
even newsletters. I subscribe to them, so whenever I have
someone doing this podcast, I forced myself to read what
I'm recommended for. Look at this book. Okay, Lisa, I
(48:56):
really appreciate you spending the time in taking me through
your every day, through your agenda, and explaining why you
have those time slots in your calendar. It's been a
very interesting conversation. I learned lots and I hope you
you really enjoyed this chat as well.
Speaker 3 (49:14):
Hi very much, thank you so much for the opportunity.
Speaker 2 (49:17):
Thank you, Lissa. I hope you'll have a nice nay,
but the stud
Bedrop is an independent data science and aiphone specialized in
data driven business change. In this podcast, our guests help
us spread knowledge and experience with our listeners.
Speaker 2 (00:27):
Good morning, Lisa, how are you doing today?
Speaker 3 (00:30):
Good morning? I am bright eyed and bushytailed for a
long day ahead.
Speaker 2 (00:34):
Awesome. I'm freezing here. I've never said this, but it's
actually getting very cold. Even though it's Spain, we're in
the north and it's quite cold here. How are you there?
Speaker 3 (00:45):
You know, I'm in central California and it is thirty
five years so we're just above freezing ourselves. But it
should warm up towards the middle of the day and
I'll be able to see some Sunday skies and take
my dog for a walk.
Speaker 2 (00:59):
That sounds pretty good. Okay, So tell me, how does
your calendar look like for today? I mean, what what
goals or meetings and things do you have for the
day today.
Speaker 3 (01:09):
Sure, I'm kicking off some accountability work with some of
my coaches around the okay hours that I've set for
this year right after I wrap up this meeting, so
we're going to be talking about progress to those goals
we check in once a month. From there, I am
going to meet with a client in healthcare that is
(01:29):
focused on creating a more frictionless member experience for patients
in California, and we will be doing some interesting deep
dives into how do we help them sell some of
their business challenges using technology.
Speaker 2 (01:45):
Okay, yours that you've mentioned obviously you run your own company.
So the what you were talking about just now among
the the healthcare client, tell me more about it. I mean, what,
what's this company? What do you do? Et cetera.
Speaker 3 (02:03):
Sure, So one of my clients is Launch Consulting. They
are a firm that is focused in on doing boutique
consulting around digital transformation. I lead their Data for Good Practice.
So I like to be at the intersection of influential
people and innovation to address large social justice issues at scale.
(02:25):
Those can either fall in a few categories. Typically when
there's regulated data involved that needs to be unlocked in
order to make sure that outcomes for people are more positive,
but needs to be done in a cyber secure and
holistically considered way. We apply that through competential computing and
(02:47):
zero trust AI systems. With that in mind, I work
a lot across healthcare, tech companies and government institutions as
well as financial services businesses that need to make better
decisions around things like anti money laundering, finding criminal networks,
or improving the patient experience in healthcare and making sure
(03:11):
that we accelerate delivering the best quality of care at
the most affordable cost to serve the most benefit for society.
So this is in healthcare today. Other days of the week,
I'm talking with large social network platforms, or with financial
(03:31):
services companies or maybe some agencies with a few letters
and their titles to improve safety and benefit society and
the digital safety side of our practice.
Speaker 2 (03:42):
Everything relates to AI. As you know, the foundational you'd
say base and then you willed on top of that
services or you'd say advice on how different companies can
provide of or service avatory technology wrote to change with
(04:06):
ethics and good practices in mind.
Speaker 3 (04:08):
Right, Yeah, I think that's a great way to say.
At the end of the day, you know, AI is
a tool, not a solution. So I spend a lot
of time uncovering the business problems that are people are
trying to address, leveraging digital transformation and technology that can
be AI that can be different types of data analytics,
but trying to make the invisible visible in order for
(04:29):
them to be able to accelerate towards their business outcomes.
I choose to focus in on things that tend to
benefit society more. That's where I want to spend my
expertise at the stage in my career, and so I've
been lucky to find clients that are aligned with that
vision and are working alongside me to make sure that
(04:49):
they can deliver on what we have identified. So I
do that with some really innovative startups, which I really enjoy,
goes back to my entrepreneurial roots. And then I some
professional services companies that can scale up to meet the
customers needs for breaking through and having a better human
experience using technology.
Speaker 2 (05:10):
Very interesting. So the name of your business is Amplified Revenue.
If I'm not mistaken right.
Speaker 3 (05:15):
It is Yes, AM Solutions also operating under Amplified Revenue.
Speaker 2 (05:20):
Uh. I understand. So one of your clients you've mentioned
this Launch Consulting group, and I really liked that name
of that business unit, which is Data for Good, isn't it?
I mean, is it a business unit by itself or
it's yes, the name of a set of services that
you provide around people first transformation kind of services.
Speaker 3 (05:44):
It actually has its own services and solutions group. We
made a big bet a couple of years back, thinking, hey,
we've made really great inroads in technology companies on leveraging
data and analytics to improve business outcomes. What if we
pointed that towards social impact issues. Could we make a
tenable business out of that? And what we learned is yes.
(06:07):
So we put the do Good to Do Well opportunity
to test and we found there is a sustainable business
model in that. So I lead that practice for launch
and they've made significantly more investments as we've tested and
shown that the market is pulling for that. That double
line revenue impact that do good to Do Well not
(06:29):
just focused on the profits, but also on the human impact.
It aligned very well with their values and we were
able to show that it's not just a hobby or
a gift back or corporate social responsibility. It can bringing
leading edge technologies to solve big, hairy problems that have
impacts to the bottom line. So kind of blending my
chief revenue officer experience and fifteen years of sales and
(06:52):
development with being able to bring technology, innovation and disruption
to make things work more seamlessly for everyone to benefit.
Speaker 2 (07:01):
It isn't only about saying that you want to do good,
it's also that you know, it's a way of demonstrating
that the services and solutions that you're building or delivering
really add value. So following that people first approach, you're
ensuring you are bringing all of the views into account
(07:24):
and anything that again you want to build or deliver
to and with your clients really makes sense because it
comes from the individual needs and perspectives. Right. So it's
it's a way of you know, doing right, but also
because you are taking everyone and all of the estateholders
views into into designing a solution.
Speaker 3 (07:46):
Right. Yeah, I have the luxury of working with an
incredible delivery team. We have experts and research, we have
experts and user experience, we have experts in PR and
marketing and telling our story, and we have a full
slee of technical and engineering and dev staff. So I
can act almost like an orchestrator, pulling the right talent
(08:08):
at the right time to come in and almost an
army Ranger style engagement where we can come to the
client where they are today, help them envision where they
want to be, and then bring in the right experts
at the right times during the development process to get
them to their vision and an agile and quick way
(08:29):
that allows them to see incremental value all the way
through the process. And so I really like working for
an organization that has roots in the military in Americas.
The investors and the founders all came from the armed forces,
and so they bring that mission lens to how we
do things, and that making sure that we leverage everyone's
talent at the right time to be able to help
(08:51):
get large companies unblocked from the innovation that they're seeking
to implement. As we know, a lot of times digital
transferations failed not because they weren't good products or good
services that were designed, but because the humans weren't brought
along in the process. So I love that we focus
a lot more upfront about making sure that we're doing
(09:11):
the empathy mapping and doing the journey mapping and really
understanding how do we make the user experience as frictionless
as possible so people don't come along because they have to,
They come along because they want to and they believe
in it.
Speaker 2 (09:24):
And it's also related to managing change expectations from those people,
right because at the end of the day. Any deal detail, transformation,
growth map is going to somehow impact the day to
day and lives of those you know that are or
form the teams on the other side. So it's important
(09:45):
that they feel how would they say, involved in the
design of that solution right and they they don't know.
Speaker 3 (09:53):
What their pain points are. You're not building for them,
You're taking your vision and imposing it on them. I
think it's the important that these teams and their experiences
are weighed and valued. There's always a nuance when you
come into a new client to understand what they're managing through,
and I think it's really critical that you leverage frameworks
(10:16):
to get information, but you're really hearing from the client
mostly what do they need and how do we get there?
And I don't believe innovation is held at the highest
paid persons level. I think you can sess it out
from all different levels of the organization and come back
with something that's much better than where you started. And
(10:37):
with that in mind, having those conversations allows the alignment.
Because almost any project that we do in digital safety,
for example, across as multiple organizations, has a very complicated
stakeholder landscape. It mirrors a lot of the privacy and
cybersecurity challenges the companies face. So there's legal operations defining
(10:57):
the policy, there's engineering teams trying to build tools to
identify when policy has not been aligned with, and then
there's operations teams that are trying to take action when
action is required based on people not following terms of service.
All of that has impacts across multiple business units, multiple
organizations and making something really tangible for people to understand
(11:22):
in terms of why we're all going to do this.
It can be really valuable in terms of making sure
that you get commitment and everybody feels like they're winning
alongside this.
Speaker 2 (11:33):
Change, because sometimes you may be building something you know
for them to do better, but if you don't take
their view into account, you're just as you said, imposing
your viewing them rather than adjesting those or that technology
for it to be a tool for them to you know,
(11:54):
improve or do better in their day to day. But
I'm curious because before you were discussing that data for
good practice and the aim that that business unit or
practicing itself has, and I'm curious if apart from the
current work that you do, you also focus on what's
coming in terms of you know, upcoming regulatory frameworks, because
(12:20):
I mean this goes back to a few months ago
when I was reading that Europe I mean the government
at a European level was preparing strong regulatory framework in
regards to data privacy, obviously everything that relates to AI, explainability, transparency.
(12:43):
So obviously there is an opportunity in doing things the
right way because if you do things the right way
by including everyone your project, your brains will have a
better row. But is there also an opportunity in you know,
(13:04):
being prepared for when this framework will be mandatory instead
of you know, waiting for the finds to come to
add yest your processes to that. And this relates to ethics,
This relates to data management. This relates to many different
angles and areas, but is data would also focus on
(13:25):
data fuit also focusing on that. Are you through amplified
revenue also focusing on that, or you're waiting for moularity too.
Speaker 3 (13:35):
Yeah, so launches practice around data for is absolutely focusing
in that area. And we do have a point of
view that the digital safety will be the new cybersecurity.
You can spend twenty years building a brand and a
bad actor with bad intent you generate user generated content
(13:56):
on a platform could really turnish your brand quite quickly,
and so I think it's been a bit unregulated for
the last twenty years. I think there's been some legislative
frameworks in place with this Section two thirty Communication Decency Act,
which didn't require companies in the United States to be
accountable for what third parties posted on their platforms. This
(14:18):
was really important for the growth of the Internet at
the beginning, But that legislation is about twenty years old now,
and I think the people that wrote it in the
nineties could never have envisioned the world that we live
in today. I know I certainly couldn't have when I
was living in the nineties, and so there's some opportunities
to close some gaps. I think it mirrors other technology
(14:39):
innovation curves. If you look at the automobile, you had
to have a certain number of cars on the road
and a certain number of accidents before safety requirements became
something that's mandated. And I think that Europe has traditionally
been the leader in thinking through policy impacts around cybersecurity,
and I think they're now leading in safety and I'm
(15:00):
really excited to see where things move from here, because
I think the level playing field that this gives is
all multinational companies will be playing by the same set
of rules, which will help to focus the innovation versus
keeping it ambiguous. And I look forward to seeing all
the technologists that I had the luxury of working with
that are embedded in all of these companies in the
(15:22):
NGO landscape, that have visions for what can make this
a better user experience for everyone, and elevate brands have
something to focus on in terms of helping their executive
leadership team understand why it is critical to innovating this space.
I don't think we have a technology problem. I think
(15:43):
we have a lack of We have an education problem
of why this matters. And unfortunately I don't have the
luxury of pretending that I don't know what happens when
things go wrong on the Internet. There's so many conversations
around end to end encryption, privacybersecurity, and I don't think
people understand the implications when you don't allow law enforcement
(16:08):
to have any kind of say in protecting the most vulnerable.
Whose privacy is more important is that the eight year
old crying victim who is having their images be rewatched
over and over again of the worst day of their
lives for the enjoyment of adults, or the adults that
are consuming that content and don't want to be held
(16:29):
accountable for breaking the law. There's just a lot of
ethics challenges that come up that most people don't have
to think about. But I've been multiple years in this
space now, and i know that we're not talking about
really fringe use cases. There is enough accidents on the
(16:50):
road that we need to start having better protections in
place to protect our legacy and our humanity. It's really
not around silencing voices. It's about making sure that we
have a society where we can all agree that these
are some common core values we're just going to protect.
Speaker 2 (17:07):
Yes, I mean also the result the reason I was asking,
it's just because I mean ourselves as Bedrock, as a
professional services business, we do see an opportunity, a market
opportunity when it comes to advising companies on how to
do things the right way. But at the same time,
(17:28):
other players may feel that if they if they keep
doing things. I'm talking maybe about the media industry, media
as marketing industry, the way they're doing things now if
they can continue for a few more months, a few
more years to cooperate in the same way they've done
it for I know, a couple some decades or maybe
(17:52):
one decade. It is you know, they can extend the
operating model and it is are more beneficial as it
is now than transitioning towards you know, a new impost
model where they have less freedom. I don't know I'm
limiting in using this example, right because this is related
(18:16):
to privacy, data, privacy and everything. But it's about balancing
what's an opportunity for some and what's a burden for others,
and maybe prefer things the old way, right. But I'm
just curious, as you know, to ask you, because you're
involved in on different fronts, working for different companies, different
industries and everything. Who do you think is looking forward
(18:38):
to this and maybe who think it will be a
challenge for them to adapt.
Speaker 3 (18:44):
I think that some companies have more technical depth than
others to overcome. I think some companies have different expectations
of their user base than others. Not all environments are
created the same. You don't expect to have the same
behavior requirements in the library that you do in a nightclub.
So I think there are different incentivestructors based on the customers.
(19:07):
I also think there's different levels of maturity of the businesses.
I think when companies are newer, they tend to have
a higher risk profile because they need It's a grow
at all costs kind of model. Whereas once they start
to mature and recognize their responsibility and the ecosystem and
some of the risks that they're bringing on to their
company by allowing certain behaviors, it no longer justifies that
(19:33):
point of view, and that's a maturity curve that people
are going through. So I definitely see customers that are
leading in this space. We are lucky to have a
footprint at some of the more mature companies that are
really leading in terms of making sure that they are
aligning ethically. One I really enjoy working with is Microsoft.
(19:58):
I think they've had a really strong position in this
market for many many years of not only doing the
right thing from themselves, but open sourcing that technology does
small and medium sized business to encourage them to do
it as well. That I feel like their actions align
with their words, and so that's a customer I really
enjoyed collaborating with I think that some of the newer
(20:20):
companies that just didn't have as much experience of understanding
what can happen when this goes uncheck at scale, are
now coming and seeking more information. I'd love to see
the growth of these industry collaboration groups because everybody recognizes
that these digital safety problems, including livestream terror events, hate speech,
child grooming for exploitation, child sexual abuse material, all these
(20:46):
regulated areas, it's a cross platform problem. Criminals don't pick
one company and be like, I'm going to act bad here,
but I'm going to act great everywhere else. So it's
really looking at the networks and understanding who's causing the
most harm, and let's focus our resources there to make
sure that we can protect as many people as possible,
(21:09):
whether that be on the banking side with anti money laundering,
whether that be on the government side with you know,
anti human trafficking initiatives or anti terrorism initiatives, or whether
that be on the platform side of making sure that
we can identify and take down user generating content that
violence terms of service as quickly as possible.
Speaker 2 (21:30):
And we need to refair with some companies, businesses and
even leaders of these companies because maybe they've not inherited,
but they've built a business model around something that's far
or somehow far from technology. And they may be big, right,
maybe one thousand employees, And for them to transition towards
(21:54):
an operating model that takes all of these changes and
you know, frameworks into a is hard, and they don't
have that expertise either, and it's completely different, I mean,
living that situation in the resus. It's completely different to
you know, an emerging business that was built and born
at the core of I don't know, maybe data science, right,
(22:18):
or data technology or whatever, location intelligence. I mean, for them,
it's way easier to navigate these changes, firstly because they
are smaller and secondly because they are more used to
you know, that this detail ecosystem. So I guess there
is a whole window for opportunity for whoever it is
(22:40):
navigating this. So I really agree you are at the
forefront of you know, many many opportunities.
Speaker 3 (22:49):
I just want to clarify the reason I'm passionate about
doing this work is because I had I was an
a solution group owner at Intel Corporate, so I got
to see the broader landscape of what everyone's doing and
work on the leaders, and then I ran my own
startup developing software does this problem. So what I find,
(23:11):
what my ideal clients are are people that have connected
to wanting to build a legacy of doing something that
they will be proud of when they retire, that are innovative,
that are willing to be to be tenacious about helping
to keep something that de risks the company on the
(23:33):
forefront of everyone's mind. And when I find those leaders,
those are the people I want to settle up right
next to and help them locate where their data is,
understand the maturity of it, and understand their processes for
how they can get closer to the goals of trust
and safety alignment. Because I don't know anybody that works
in that field that shows up to do a haphazard
(23:55):
job any given day. But when the volume of threats
continues to grow exponentially, you need experts that live and
breathe the stuff. And it's a pretty small community of
people that choose to focus and on the hardest parts
of the Internet. And they're all my heroes.
Speaker 2 (24:12):
To be honest, I completely agree. And it does make
a difference to work on your day to day with
with someone that you're aligned with, right, that you share
the same values and level of thinking, and that you
want to do things you know to improve the lives
of others to make this a better world. And it
(24:32):
really sounds I wouldn't say it really sounds hopeful, but
you know, it's motivating to do things that you know
are going to improve how others go to the offices
and do their work. I think it's I think it's
really good and it's really rewarding. I'd say, I'm curious,
you know. I mean, you already mentioned that you started
(24:55):
to be exposed to this in the previous role that
you had, but I'm curious to know how you also
started to be immersed in AI and maybe data science
in this kind of field in the past. Was it
in the same role that you've mentioned, I mean, how
did you get to the forefront of technology evolution? Sorry
(25:15):
to say, yeah, so.
Speaker 3 (25:18):
I started my career in the technology industry. I've worked
across multiple sectors. Back in twenty fifteen, I had started
to get exposed to some of the work that was
happening around precision medicine and being able to leverage known
types and treatments for cancer patients across multiple medical institutions
(25:40):
to find the right path forward for having impaved outcomes
and more personalized cancer treatments. And my thought was, after
traveling the world and being the thirty six countries as
an IT leader early in my career, how could we
apply these same kinds of techniques leveraging AI and data
to improve outcomes for marginalized women. And so it was
(26:01):
a fairly naive journey. But what I found was there's
a lot of technologists out there that are really looking
to do something that they really believe in that gets
some out of bed in the morning in addition to
maybe what they get paid to do. And they were
very willing to lend their expertise to passion projects. And
(26:21):
so we ran it that way for a couple of years.
We worked with some startups, we worked with some across
the industry with Google and Microsoft to try to improve
the situation as it was. And the more and more
I learned about the topic was the same time, the
more and more I learned about AI technology, it was
saturated in data scientists and principal engineers and building data
structures to be able to find the pearls of wisdom
(26:44):
in the oceans of data and being exposed to a
lot of entrepreneurs that were leading and had a mission
focus to their company, not just a profit focus. Obviously
profit had to be part of that, or it would
have been a nonprofit, or it would have been a hobby.
But finding that intersection point where companies can accelerate innovation
(27:05):
with AI while solving something that needs to be addressed
was really appealing to me. So I really love the
intersection of helping machines do what they do uniquely well,
which is math and repetition and finding patterns, and partnering
those with subject matter experts in humanity, like the investigators
(27:27):
that are going and recovering missing children, being able to
help humans do what they do uniquely well and cut
out a lot of the mundane and repetitive tasks, and
bringing the right information to them at the right time
so they can shine in their area of expertise. I
really believe in the two key approach. I think it's
(27:48):
going to be how we are able to solve some
of the problems that we faced since the dawn of time.
Speaker 2 (27:54):
I know that you that you have some background, maybe
either the engineering degree when you were in university, did
this help somehow to better transition into everything that relates
to AI? Because obviously you can be really interested in,
(28:16):
you know, doing work in this field, but sometimes if
you don't have I'm just asking analytical background or a
technical background, it's harder for you to really understand the
implications of you know, maybe some algorithms, some work that
these machines may do that's repetitive or not. But did
(28:36):
this background help at all or you feel it it
wouldn't be needed to be doing the world that we
were doing these days?
Speaker 3 (28:44):
I can answer that two ways. So wasn't it an advantage?
Speaker 2 (28:47):
Yes?
Speaker 3 (28:48):
I have a degree in industrial engineering and operations management.
So when you look at how data science has evolved,
Number one, data science and AI wasn't a field that
most people were to prepare for when I was in university.
It was around, but the compute power hadn't accelerated to
make it at the scale of usefulness that it is today. Yeah,
(29:10):
So being able to look at a workflow of a
process and understand how to optimize that process and be
you know, having to take calculus for and statistics and
all of those things absolutely as an advantage for understanding.
But mind you like that that kind of skill set
had azure feet of me for almost two decades by
the time I approached this. So I think being able
(29:31):
to think like a problem solveregn an engineer makes it
easier to walk into this field. But I have brought
people into the companies that I've run straight out of
data science boot camps with backgrounds in counseling or backgrounds
in romance languages, that have the tenacity to want to learn,
and we have been able to see people bring such
(29:54):
cool innovation to the forefront of AI by bringing that
broader background, because it really is a combination of psychology,
computer science, and statistics and math that come together to
figure out how to address bigger problems. But somebody that
has lived experience of problems that are trying to be
(30:17):
solved can sometimes be the most effective in these fields
because it's really how they look at the problems and
are able to understand the impacts that they have to
society and businesses that help shape the right focus. You
can always build a team around you that can complement
any deficiencies. There are tons of roles within this ecosystem
(30:39):
that benefit from vision and leadership you don't have to
be hands on keyboard doing you know, coding and Python
to add value. And I'm a testament to.
Speaker 2 (30:48):
That, So I completely agree. And actually, when you when
you're building, designing, or implementing a solution for a client,
regardless of it being a machine learning model or just
a data visualization bid, having that perspective from someone that
(31:08):
isn't technical but has a certain degree of creativity or
has been exposed to the arts area in some sort
of way. Just having a different kind of mindset helps
to provide a better service, to provide a better solution
to the client. And it happened to us we drastically
changed on how we were delivering preits when we started
(31:32):
to bring the capabilities of someone who had a background
in psychology and was able to, you know, use the
same thing in framework at the start of a very
product to navigate you know, those needs of these stakeholders better.
So I completely agree that, you know, it really helps
(31:54):
when you are leading this kind of AI data initiatives
to have that solid analytical background, whether that's from engineering,
computer science, whatever it is, it really helps because I
think you're able to understand what's happening in the back end,
and you can translate that and you can be a
(32:14):
midpoint stayholder when it comes to translating things to everyone
around you, Right, I think that's a very valuable skill. Actually,
to me, what's important is to have someone in the
team that can also I mean, who can lead the
technical team and speak with data scientists and data engineers
(32:37):
about the code itself if needed, but that it's also
able to translate that into how that's going to impact
the business, and also so that those around him from
the business side understand this is a tool and a
mean to an end, as you said before. But because
but these people are very special. I mean the background
(32:58):
that you have on how you've navigated, you know, the
ward that you do now makes you very valuable to
any organization. I'd say that. I mean, obviously this is
my opinion.
Speaker 3 (33:08):
Well, I appreciate that. Yeah, as a founder of a
company myself stapping up in these areas when we were
building technology for Minor guard, which is now reflected in
products including chromebooks from Lenovo or you know, in the
Apple iPhone around child safety, I actually had a better
experience hiring the people that had more broader business analytics
(33:32):
backgrounds that upskilled in data science. We brought our data
scientists in that took our product to the next level
straight out of a boot camp and Frank she's now
the engineering manager for cloud Flair globally for Trust and
I have had more luck working with people that had
the commitment to the business impact and the tenacity to
(33:58):
learn and grow and deliver than I have with the
people that have come from very credential schools that have
a background in writing research papers, which a lot of
the data science community is because you have to have
that hunger to see the impact in the real world
and know how to move organizations and be politically savvy
(34:22):
about how to keep this on the forefront of people's minds.
And that's not engineering, and that's not coding. That's grit,
that's vision, that's tenacity, and that's helping people make the
invisible visible. And everybody has a role to play. Every
(34:42):
skill is needed.
Speaker 2 (34:45):
To be fair with everyone here. Not everyone wants to
be you know, client facing or trying to translate those
technical needs or you know, the technical insights of how
back end works. I mean, there is people that are
very capable of, you know, doing an excellent work, but
(35:06):
they want to do it themselves and they want to work.
I wouldn't say in isolation, but you know what I mean, right,
they are maybe they feel more introvert than they do
a tremendous amount of work and also of really high quality,
but they don't have those traits and they don't want to,
you know, to improve on the area. And that's perfectly fine.
But I really find, I mean from my own experience,
(35:30):
that's very hard to find those roles in between that
can coordinate the business vision, that can translate the technical needs,
the functionalities of a product, and to have that overall
view the business acumen all together. I mean that that's
(35:52):
a very valuable set of skills and that to me,
that's the hardest to find, regardless of IQ or regardless
of the background. I mean, yes to gain and to
being able to know navigate all of those areas. Okay,
before we close off the reason we.
Speaker 3 (36:13):
Are we only got to eleven am on my agenda
for today. Do you want to talk about the rest
of the rest of today?
Speaker 2 (36:19):
Looks like I know, I know, there is so many,
so many things we can discuss. Okay, tell me briefly,
what else do you have?
Speaker 3 (36:30):
So we have a lunch of the venture capital firm
in town talking about potential help across their portfolio. I
really enjoy functioning as like a advisor for revenue across
portfolios and helping companies get unstuck, especially as they're scaling
from seed to series AH in that early stages there,
(36:51):
it's always fun to help operators go faster and see
through their vision more exp the I then have a
meeting with a founder that I advice for in the
HR tech field. She runs a company called Umasa, and
they are looking to tune their product market fit with
(37:15):
marketing and sales adjustments. They've had a lot of success
coming out of five hundred startups, and now it's time
to put gasoline on the fire, which is really exciting.
And then from there I'll be working with another colleague
of mind from women in Data I believe you've interviewed
Sadie Saint Lawrence before. We're doing some envisioning for a
large apparel company around how to get more women into
(37:36):
data science roles and leadership. I really applaud their diversity initiatives.
We don't have a signed contract yet, but we have
a proposal for their VPS that lead AI and mL
around this topic in a couple of weeks, so hopefully
we can help them to determine where they are and
a path forward to what their vision is. They're doubling
and tripling their workforce un data Science, you're on here,
(37:59):
and they want to make sure that they have representation
at the table across genders and across marginalized groups, and
I think that's what's going to be help help them
to win. It's a very large brand you're all aware of,
so stay tuned. I will announce it on LinkedIn when
I can. If we are able to move forward.
Speaker 2 (38:17):
I'll be paying attention. And that's actually a kind of
hard thing. I don't know if it relates obviously to
STEM subjects being more I don't know. I would they
say whether they significantly have a stronger male presence, but
it shouldn't be that way. So do you feel or
(38:38):
maybe you can advise I don't know. We have a
strong relationship with the university here ourselves as pet Truck,
and we don't really know how to advise them on,
you know, attracting more women into joining STEM subjects, and
this can be anything, right, maybe the engineering side of
(39:00):
some of the pure sciences like mathematics physics now are
gaining more attraction because it feels like data science. But
I think, I don't think that universities have found the
right recipe for balancing genders here, And I don't know
if you have a piece of advice here. We know
(39:22):
and we are here today because Saydy from Women in
Data introduced us and probably see it has a comprehensive
view of things, probably much more than me, much weter
than me. But I'm curious to know how could we
advise You know, those academia leaders on balancing things, Well, if.
Speaker 3 (39:43):
I'm not mistaken, coming out of academia, you actually have
a decently balanced gender pool to work with. It's more
as people advance through their careers that you start to
see what I've heard referred to as the leaky pipeline.
So you come in with a nice gender balance in
the recruiting process of recent college graduates, but as time progresses,
(40:05):
the opportunities for advancement are maybe not equally distributed, and
then you know, you start to see attrition. And that's
why I'm so passionate about what Hu Maxa has focused on,
which is how do you make sure that people can
have opportunity to continue to grow. And you know, implicit
bias isn't the only determinant on who gets the chances
(40:25):
to prove what they can do and where they can grow.
With the work that Sadie and I look at, we're
really focusing in on helping people understand what you can
do with this background and how do I think where
we haven't appealed to in the hard sciences is the
vision of why it matters. And so that's why I
(40:48):
put a lot of energy into doing more public things
like this. Frankly, I was not known by anyone four
years ago. It wasn't until I launched my own company
that I realized that there is such a lot of
representation of women leaders in this field that live a
lifestyle that I can honestly pass the white face test
(41:08):
and tell my daughter she might want to follow in
one day. I'm still on my first marriage. I spend
time with my elementary school aged children every day, and
I choose to focus on working in things that really
align with my almost I'll say it my calling. There's
a reason I'm here, and it's not just to toot
(41:31):
my own horn. Is to make sure that we are
lifting the voices of all the folks that haven't had
a chance to participate. And there's no point at getting
a seat at the table if you're not changing the
conversation a little bit and contributing. And I think women
and diverse people have not had the opportunity to be
shaping the future of tech at the level that will
(41:54):
benefit us all as a society. They have a different
lived experience, a different perspective, and can see around corners
that you can't get in homogeneous groups. And I'm really
passionate about being a visible person out there because I
don't think you can be it if you can't see it.
(42:14):
And I don't think the next wave of innovation is
going to come from the people that we've create this
first wave of innovation. I have a huge, huge hope
for gen Z. The way they look at problems and
balance revenue and societal impact and progress and social justice,
(42:38):
it's just so inspiring to me. I think we've seen
so much that that generation can do, and I really
want to be out there paving as many pass as
I can for others to follow, not to do the
work that I do, to do the work. They're uniquely
focused on Is it animals, is it the elderly, is
it the environment, is it human trafficking, whatever it is,
(43:02):
you know what you want to see change in the world.
Math and science and data is the fastest way to
make that change real. And that's what I think we'll
call to different groups of people. Because technology is going
to disrupt everything. We've seen it happen in multiple industries.
It's going to continue, it's not going to slow down.
(43:24):
And if those voices aren't at the table, we're going
to create a society that none of us want to
live in.
Speaker 2 (43:32):
It is the same as when we were discussing before,
the way you build the right products for clients and
the way you deliver services for clients, right, the same
applies to society and the technology that's going to benefit
them supposedly, So you do need to, you know, to
have everyone respected through that process. And the pace that
(43:54):
we technology advances may even increase, right in comparison to
what we've seen till now. And obviously many industries, many
business models have changed, have been altered because of what
has appeared recently. So I really agree with everything that
you've said. Okay, so how far are we with your
(44:17):
agenda because we have five more minutes and.
Speaker 3 (44:21):
You take me the time to pick up my kids
from school and get them, get my daughter the theater,
and my son do locker and hopefully will my husband
has I believe it's smoking some try tip tonight for dinner,
which will be a really nice treat, and we will
(44:41):
huddle up and watch a family show together to wrap up,
put the kids to bed to day and probably collapse ourselves.
Speaker 2 (44:48):
Sounds like a perfect day to be honest, and if
not more important, I mean those last time slots of
the day, I'd say are very important, right, family, and
I'm spending time with the loved ones. I really value
that on the more time or the more the older
I get, the more I value time with loved ones
(45:10):
and friends and you know, having quality time with them.
Speaker 3 (45:13):
Yeah, I find my creativity and leadership are churched. And
the times when I've turned off now when I'm activated
and I've spent I made a lot of errors in
my thirties of trying to always be on and already
always service every master. But with you know, experience, you
glean some wisdom and I think more above it said
(45:35):
at best is time is the only resource you can't
buy more of, you can't get back, and you have
to own your time and choose how you use it
or you're never You're never going to be the leader
that you want to be. And so I really guard
(45:55):
my time around my family time, my time to walk
my dog and see that tops of the trees and
the blues of the sky, because my best ideas on
my best creativity come in the recharge. They don't come
in the action.
Speaker 2 (46:09):
I completely agree. I mean, and if they come doing
your focused time at work, it doesn't really make sense
because you should be doing something else. So I completely agree. Okay,
so again, you're here because Sadie made an introduction. I
think it was by email, but I'd be curious to
know if you would recommend me someone to join me
(46:30):
in another data stand up podcast episode, or maybe you
have someone in mind or you want to give you
that thought I do.
Speaker 3 (46:39):
I highly recommend you reach out to Edward Dixon, somebody
I collaborated with it intel that he runs his own
consulting firm. Now he focuses in a healthcare innovation as
well as child safety work as well, but he is
very technical and he is the peanut butter in my
jelly in terms of the driving a vision of how
(47:03):
we can see impact in the world. He's one of
the people that have been influential across my corporate journey,
my entrepreneurial journey, and someone that I know is making
a huge impact in the public sector on making sure
that we can provide better tools for the people that
(47:23):
are protecting us, not just for uh, the early adopter
innovator criminals getting to it quicker. So I highly recommend
Edward Dixon out of Cork, Ireland. He runs his own
consulting firm and I'd be happy to make an introduction.
Speaker 2 (47:42):
It's called Breaker Awesome, I mean really sounds promising. And
in terms of a book I read or maybe yes,
you know, newsletter that you consume every week, every month, anything.
It doesn't have to be related to data or AI,
maybe just something related to productivity, whatever it is, but
(48:03):
that you recommend well.
Speaker 3 (48:07):
Edward and I contributed a chapter together to a book
that came out last month called Demistifying Artificial Intelligence to
the Enterprise. Wow, that's available in bookstores now, So I
recommend that as a read. It was really fun to
collaborate on that kind of work. And it brings industry
leaders from all different sectors talking about how they're applying
(48:29):
AI to accelerate digital transformation. And so that's what I
would recommend.
Speaker 2 (48:35):
I will read it. I mean all of the recommended reads,
even newsletters. I subscribe to them, so whenever I have
someone doing this podcast, I forced myself to read what
I'm recommended for. Look at this book. Okay, Lisa, I
(48:56):
really appreciate you spending the time in taking me through
your every day, through your agenda, and explaining why you
have those time slots in your calendar. It's been a
very interesting conversation. I learned lots and I hope you
you really enjoyed this chat as well.
Speaker 3 (49:14):
Hi very much, thank you so much for the opportunity.
Speaker 2 (49:17):
Thank you, Lissa. I hope you'll have a nice nay,
but the stud
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy, Jess Hilarious, And Charlamagne Tha God!