We all are so interested in healthcare.
We all, at least the people watching this
show, are interested in artificial intelligence,
innovation, data, [and] machine learning.
Today, we bring it all together.
I'm Michael Krigsman.
I'm an industry analyst and the host of CxOTalk.
You are watching Episode #275 of CxOTalk.
I'm so thrilled because we have two extraordinary
guests.
Milind Kamkolkar is the chief data officer
at Sanofi Pharmaceuticals, and Hicham Oudghiri
is the CEO of Enigma Data, which is a very
interesting startup.
We'll learn more about his company, and Milind's
company Sanofi, in a moment.
I want to say a thank you to Livestream.
They have been supporting us for the last
several years, and they supply our video streaming
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If you go to Livestream.com/CxOTalk, in fact,
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and subscribe on YouTube.
Milind Kamkolkar, you have been on this show
before.
Tell us about Sanofi and tell us about your
work.
Sure.
Sanofi is one of the world's largest French
pharmaceutical companies specializing in a
number of different areas: consumer health,
pharma, general medicine, specialty and rare
disease, and oncology and, of course, in vaccines.
Most of my work at Sanofi--I joined hitting
up the ninth month now--is really focused
on helping Sanofi moving from a data-driven
or, let's say, data generation organization
to an insight generation organization.
As part of that data transformation journey,
[it's] really bringing together the best parts
of our organization and covering the areas
of where we need opportunistic growth and,
frankly, helping us make decisions better
across the firm.
Okay.
Fantastic.
You're the chief data officer, and I'm sure,
as we have this conversation, we'll learn
more about what that actually means and what
you do.
Our second esteemed guest, and this is his
first time here on CxOTalk, is Hicham Oudghiri.
Hicham, welcome to CxOTalk.
How are you?
Thanks for being here.
Thanks, Michael.
Thanks for having me.
Hicham, tell us about Enigma.
At Enigma, what we do is really help companies
create a center of gravity around their data
model.
If you look at Amazon, Google, [and] Facebook,
really what separates them from the rest of
the Fortune 500 is this notion that all of
their products are built upon a central data
model, these business objects, and every single
thing that you do on their platform enriches
that database and that view of their world.
For a lot of companies, this has to be cobbled
together, right?
Data is coming from legacy applications, applications
that weren't designed to speak to each other.
We really help companies link their data to
make smarter decisions on a unified view.
I thought this was very, very interesting
because you are also aggregating many, many
public data sources that organizations can
then use in their machine learning, AI, and
predictive analytics efforts.
Absolutely.
When we give people back these enriched views
of the data that they have, we layer on top
of it really the whole world as a context.
We aggregate information all the way from
H1B Visa records to cargo container shipments,
FDA data, [and] Medicare spending data.
For us, being able to integrate data and allow
companies to use more and more heterogeneous
data in every line of what they do is pretty
mission critical, and it's making sure that
people can actually interface and refer to
things in the same way.
Okay.
Fantastic.
Now, Milind, let me begin with you.
This notion of data and healthcare, set the
stage for us, if you would.
Why is this so crucial today?
What's unique about today's environment that
we need to pay attention so closely to this
issue?
Yeah.
I want to start off by saying data is nothing
new.
It's been around for years.
We've just been really good at accumulating
more and more and more of it.
But, I don't think there's ever really been
a business design behind how and why we accumulate
this data.
Particularly in healthcare today, we're often
still focused on the same questions we've
been trying to answer for the last 25, if
not 30, years.
Personally, I think the opportunity that we
have today is really around being able to
ask not only those foundational questions
but perhaps asking questions of new data sources,
new environments that might shed a whole new
light into the way in which we operate.
I call this phenomenon, at least the way in
which we're organizing it in our company,
all around the notion of doing things better
versus doing better things.
The doing things better is all around the
operational effectiveness of your decision-making.
Are you looking at data that's findable, accessible,
interoperable, reusable, and really using,
if you will, that fair data standard as the
FICO score for your information sets?
But, it doesn't stop there.
Once you've gotten that piece in play, how
do you start using these new data sources
to be able to ask questions to uncover new
insights that you may never have had before?
I think that opportunity with the advent of
big data, if you will, even as a data management
infrastructure, the advent of machine learning
algorithms that can actually operate faster
because, again, that's nothing new.
It's been around for some time.
But, it's really around this high compute
infrastructure now and the availability and
accessibility of this data that's allowing
us to do new things in a far more profound
way.
And, more importantly, being able to use those
new insights in ways that impact healthcare
in a more positive way.
Hicham, I love this notion that he was just
speaking about of using data to do things
better.
You're in the data business, so how can we
use data to do things better, to actually
make better decisions?
Think about it this way.
The whole industry is moving towards more
and more personalized delivery.
When you do a clinical trial today, it's possible
you're doing it with hundreds of people as
opposed to the thousands or even much more
so than you were doing before.
The barometer for data quality, using data
well, and doing things better, it's not just
opportunistic.
Literally, the science is leading us in that
direction as well.
Yeah.
Everything from the manufacturing process
being tighter from a quality perspective to
being able to, on the backend, manage and
triage adverse events faster, to the regulatory
submission process when you're researching
drugs, all of that is moving towards having
data being corroborating evidence to deliver
more and more personalized drugs and to be
much, much faster in the process of research
and development.
Milind, I know you have thoughts on this.
[Laughter]
Yeah, absolutely.
I couldn't agree more.
I think it's one of those areas where, to
echo what Hicham has been saying here, ultimately,
this comes down to, how well do you operate
your business and how effective is your business
in being able to address unmet needs in the
consumer space, in the patient space, or customer
space in this instance.
I think what we've begun to do much more effectively
now is use that data to deliver personalized
content for the right customer through the
right channel at the right time.
It's not limited, right?
When you think about that, that's almost marketing
101.
Frankly, it's not marketing 101; it's basic
human information engagement.
That notion of basic information engagement,
I think we've gone through this dawning of
the digital age [where] there are many companies
that are incredibly innovative in their digital
presence but are absolutely pathetic when
it comes to decision-making for their business.
I think that's the big game changer here.
When we talk about data as being oil, gold,
or whatever other appropriate highly quantifiable
entity is, it's that notion of saying, "Can
this data actually drive better business outcomes?
As a result of that, can our firms become
smarter in the way in which we make those
decisions?"
I think that's when you start tailoring into
the world of algorithms as well.
I don't want to take away, though, the importance
that with good algorithms and good data comes
the need of basic foundational elements of
data, which includes governance, change management,
the area of data quality.
I mean at what point do we agree the threshold
of data quality is good enough to at least
get the ball rolling?
I think, once you start doing those and that
to me is more the operational effectiveness
of how well you run your data operations,
you can really start managing a portfolio
of investments across your data and analytics
workspace.
I like to call it your systems of record.
It should be no more than 40% of resource
allocation.
Systems of innovation where you're competing
-- sorry, differentiation where you're competing
should be the last 40%.
Finally, the leapfrog capability comes from
those newer data elements and the newer algorithms
you deploy in the systems of innovation.
Today, almost 98% of our resource allocation
is still kept captive in systems of record.
Most of the time, we're not even doing those
well.
I think that's where the opportunity is to
start embracing these newer technologies,
capabilities, data, and so forth with, of
course, a profound impact on change management.
What do people do now versus what do machines
do now?
If you think about it, there is so much scale
inherently in that supply chain from research
to patient outcome.
If you just get one part of it right, you're
affecting millions of people, you're affecting
matters of life and death, and sickness and
not, right?
The ability to do something that is robust
enough and sustainable enough that you can
actually build on it and start innovating
on top of something that you can push out
to a variety of areas, I think that's very,
very important for pharma.
I think the accessibility of data is changing
ways in which pharma companies even organize
themselves.
Yeah.
I see a lot of pharma companies who organize
themselves around basically brand franchises;
this drug has this research database, this
marketing database, and this quality database.
Data is being able to kind of substantiate
more of a hub and spoke model around information
access for the first time.
I think that's a big change too.
These are cultural changes happening because
of the way information is liberating, basically,
resource access.
No, absolutely, and I think one of the things
that tie into that is this whole notion of,
with data being more available on the Web
as well through the Internet, the one really
nice thing that's happened is where we used
to have these, let's call it, cultural boundaries
that existed, well, the Internet doesn't have
those per se.
When we're seeing things like GDPR, et cetera,
all starting to introduce itself now into
the world around data privacy and security,
but really patients who are describing or
customers that may be describing disease outcomes
or parts of their patient journey in a language
that is non-native to, perhaps, say, folks
in North America, you can still actually leverage
their data to see, particularly in multicultural
countries, do the same kinds of people, for
example, share the similar kind of impact
or not?
I think this is where this world of personalization
makes such remarkable difference because,
whilst the Internet may not have geographical
boundaries per se, the reality is you can
now start leveraging that and translate that
into your domestic markets where things start
to become quite real as well.
I totally agree.
The only problem that kind of comes out of
all of this is, we do increase that noise
to signal ratio immensely.
Kind of what we need to do as practitioners
in this space is remember that basically there
has to be some domain expertise that comes
back into the creation of the data, the curation
of the data science behind all of it.
It's why data science, frankly, is a multidisciplinary
effort.
Yeah.
There's been, in my opinion, a little bit
of a rush in the last couple years towards
the infrastructure and the compute and not
enough of a marriage in between the underlying
science and the domain to basically make sense
of all of this flood of information that's
coming.
We've seen it in a variety of areas where
it really, really can hurt, and it's been
nice just anecdotally as a young company to
be able to work closely with people who actually
understand it.
That's, I think, the cool thing about pharma
is that there is so much domain expertise.
The problems are very deeply rooted.
Milind, can you give us some concrete examples
of this that the audience, who may not be
healthcare or data experts, would be able
to understand or relate to?
Sure.
Let's take the example of outcomes-based evidence.
This is a big area for many pharmaceutical
companies.
We know that contracts with payers, with insurance
companies, or even regulators in, let's say,
more socialized healthcare systems across
the world, are delving into i.e. the notion
that you only get reimbursed if your medication
proves a positive health outcome.
In these instances, this is where this new
data makes a profound impact because, for
example, geographies that might be very high
in pollen count might not always be producing
or, in some cases, might be producing the
right kind of results depending on patient
population in that area.
You'd be surprised how often we take for granted
the notion of weather, but don't often integrate
it into our treatment paradigms, into our
outcomes-based contracting.
I think what we're starting to see now are
people saying, "Well, hang on.
Are these new variables that are coming into,
let's call it, clinical settings actually
more important, because they start to give
a more holistic view of a patient's journey
through their treatment paradigm?"
I think the other one that's very interesting
here is what I would call the Fitbit paradox
where we're seeing a tremendous uptick of
digital gadgets.
I think the word of today in this year's CES
conference is you could literally put Alexa
on anything.
As we're seeing this sort of profound impact
in digital health, one of the struggles we're
facing is a lack of data that's being used
for preventional studies; i.e. people who
buy a Fitbit are generally fit, right?
The people who probably really need Fitbit
may not be using it as effectively.
You start getting into data biases that come
into play in the world of evidence and evidence-based
outcomes.
The last area that I would see where data
is being used into this specifically is the
world of effective computing; i.e. how do
you really measure pain?
Should it still be on the same ten-scale threshold?
Should it still be using the same emojis?
Now we've got emojis online, and we can start
collecting that more frequently through online
measures.
Do those now become the new clinical biomarkers,
if you will, or digital biomarkers of pain?
All of these sorts of things, when you start
combining them together, start giving you
both a more real-time context under which
you can start creating new interventional
studies or interventions that prescribers
and/or patients can sometimes self-administer,
versus how that gets reflected now into the
economics of healthcare management, i.e. when
is a reimbursement most relevant?
If you ask them to get high pollen counts,
for example, the likelihood of payouts during
that time in an economic burden or burden
of disease area would also be probably for
those who suffer asthma and other pulmonology
related or immunology related diseases.
Hicham, any thoughts on practical implementation
examples of data similar to what Milind was
just describing?
Yeah.
I'm really fascinated.
I like Milind's example of real-world evidence
because it's this notion that data can connect
parts of the system that haven't been connected
together tightly, so the actual drug manufacturers,
the payors, the providers.
I love the problem of pharmaceutical safety
as well.
I'm really passionate about what it takes
to pull drugs off the shelves when you hear,
historically, root cause analysis takes close
to a year to bring together an instance of
an adverse event.
Someone took a specific medicine and got really
sick in a way that they didn't expect, and
the time that it takes to understand how that
drug was manufactured.
What are the quality indicators of that specific
batch?
We now have sensors on boat shipments and
pallets recording the temperature of each
and every movement of the pharmaceutical batch
across the chain.
That stuff is being analyzed in real time
and so, when an event like that happens, we
can trace the lineage of that event all the
way and do the analysis in real time.
Furthermore, we're connecting that supply
chain together, but we're also connecting
kind of external evidence to the regulators,
the FDA, the World Health Organization, who
will report these.
We'll have individuals call into call centers.
We'll have doctors and hospitals kind of be
redundant in the way they report these things,
and it costs a lot of time and effort to triage
all of this information, and all of it kind
of comes in an incomplete way.
What we can do now with data is actually stitch
it all together really, really fast in real
time.
Instead of having doctors figure out kind
of who is on first, we're actually investigating
why things went wrong as opposed to gathering
evidence.
Kind of reducing that rule to something that's
much more palatable and much more efficient
for everyone's safety.
Yeah.
I think, to echo Hicham's point, there was
a really good point raised around the connectivity.
In the early 2000s, we had a lot of these
buzzword phrases like bench to bedside, molecule
to market, and these sorts of things.
Whilst the hype cycle of that was very promising,
it went through a bit of a lull because we
realized we just didn't have the infrastructure
to do that work.
Nowadays, I think, with the newer capabilities
that are coming through, that data has the
providence and lineage to really be able to
be tracked at a discrete level.
What machine did this particular batch of
compound get produced at?
Could you really quantify if a product label
change has to be made as a result of the adverse
event that's being reported in a call center?
Is that propagation going to happen consistently?
Is it going to happen in a way that's also
meaningful where maybe the pharma companies
don't need to report to the regulators anymore,
but we give the regulators direct access to
a portal to say, "Hey, you know what?
Take a look through our process.
You can see it anytime you want to make sure
that this kind of quality and compliance is
coming through the system."
I think that's kind of the power of the transformation
that we're seeing today as well.
I think the other part around cost is a big
one.
All of these processes cost money.
Any kind of interventional state, whether
it's algorithmic, data stitching, or data
transformation that can happen in play, if
it does reduce the cost, the anticipation
is that these, of course, will be benefits
that get transferred back to the patient population.
I want to remind everybody that you're watching
Episode #275 of CxOTalk.
We're speaking with Milind Kamkolkar, who
is the chief data officer at Sanofi Pharmaceuticals,
and we are also speaking with Hicham Oudghiri,
who is the co-founder and CEO of Enigma Data,
a very interesting startup.
Right now, there is a tweet chat going on.
You can ask your questions of these two very
smart guys using the hashtag #CxOTalk.
Let me address to either one of you.
You've both been painting a vision of data
linking together disparate parts, what today
are disparate parts, of the healthcare system:
payers, insurance companies, doctors, hospitals,
let's not forget patients.
Of course.
And then, their devices.
You're both painting this holistic picture.
What do we have to do?
Where are we today, and what do we have to
do to achieve that vision you've both been
describing?
I'm happy to take a quick stab.
Sure.
Definitely an incomplete answer, but there
are a lot of very encouraging, big, bold bets
being placed.
If you take a look at CVS and Aetna, or even
the announcement a couple days ago of Amazon
partnering up with JP Morgan and Berkshire
Hathaway to deliver more unified care, these
are moonshots and quite interesting.
They're very calculated.
I think that the risk on the data side of
the equation coming together, becoming lower,
and becoming more a game of, how well can
you do it?
There is a lot of low hanging fruit in our
industry to make data better, and I just want
to harp back on that one point that you made
earlier, Milind, which is, if it's not done
with an operational mindset, you're creating
a little more debt despite the amount of innovation
that we're springing out.
Yeah.
We won't be able to kind of reduce the entropy
in this complex system.
Yes.
I'm very encouraged that people have the confidence
that they're doing it the right way.
We certainly see that.
I certainly see how pharma is investing in
that.
As to how it gets done technically, I have
a bunch to say, too, but obviously, I'd love
to hear what you think about people's confidence
in that respect.
Yeah.
I think, on the pharma side, I would say it's
a cautious trepidation.
I think we are taking much bolder bets, but
it's all relative speaking.
To that extent, I'm incredibly encouraged
just seeing the recent investor calls between
Roche, Novartis, [and] certainly our own.
I think pharma CEO today, or every large healthcare
CEO today, is in their investor calls relating
to the importance of digital and data.
This really is a golden age of seeing these
things come together.
I think, when you have leadership that is
coming down and saying, "This is a strategic
objective for us, and we're going to apply
it in the areas of finding the right patients
for the right trials, optimizing clinical
trials so that the cost can be transferred
further down the line, ensuring that we're
engaging more effectively so it is the right
content, and it's not this superfluous advertising
spend that we often see today."
I think those are the areas that we're seeing
some really big moves.
I'm particularly encouraged by some of the
work that's going on in deep learning and
the applications of machine learning technologies
in operational effectiveness, simple things
of saying, "Can we automate FDA submissions
through using national language processing?"
We're doing some pretty cool work on that,
and my peers are doing some really cool work
in that stuff in the other companies.
The world of blockchain, I know there's a
really nice healthcare consortium coming together
between a couple of us in the industry where
we're openly sharing our experiences in working
with these different platforms and technologies
and trying to address, "Okay.
Look.
There's a really cool tech that's out there,
but is it really going to make the best sense
for us?"
I want to give you a really clear example.
We talk about blockchain, for example, in
the world of counterfeit and being able to
prevent counterfeit.
But, you often wonder and, sadly, that most
of this counterfeit action often happens in
emerging growth markets and/or parts of Africa.
Here we go.
We take a really big energy consuming infrastructure
like blockchain and apply it to areas that
have very little to zero bandwidth.
It makes you wonder, "Is that really the best
application of blockchain that we see today?"
Structurally, it may not work, but the idea
is correct.
I think what we're starting to see now is
that we're not jumping to technology first.
We're actually addressing what is the problem
we're trying to solve before we go into those
spaces.
I'm starting to see at least more relevant
questions, more interesting questions being
asked and the technology, honestly, taking
the seat it should, which is, okay, let's
see how we can best do this now.
You can really proxy into the answer of any
question.
Yeah.
It's this notion that kind of all models are
false.
Right.
[Laughter]
Some are more useful--
Yeah.
--than others, right?
And so, we work a lot with public data.
We see people all the time doing things like
trying to cobble together claims data from
a variety of providers.
It turns out Medicare puts out this information
for free for everyone.
Yeah.
Sure, it's not the whole population, but you
can get a sense of what's going on.
I see this on the data side.
I see this on the technology side, vis-à-vis
your question about blockchain.
Absolutely right, the best answer may actually
not be the best answer.
That's right.
It may just be the best theoretical answer.
[Laughter]
Yeah.
This space has, I think, a culture of kind
of engineering and setting things up to deliver.
It's different than if you are kind of marketing
and delivering ads on the Internet, which
is what big data has been for a really long
time.
The challenge that pharma has is it can't
be as fast as loose, and I think that that
ingenuity is coming through that challenge.
Look.
I fully agree.
I think, to me, the most satisfying thing
certainly I've seen in the last nine months
here at Sanofi and certainly speaking with
my peers in the other companies is, we're
finally embracing agile.
We're finally embracing the notion that it's
okay to be a little scrappy within reason.
I would say areas with respect to patient
safety, et cetera, absolutely no compromises
in terms of quality and safety.
But, I think in some of the other areas, this
notion of you can work agile, and it's okay.
I would not say it's okay to fail.
I would say it's more okay to experiment smartly.
When you do that, being scrappy, but recognizing
that much of this is still very much a marathon,
I think is a really positive sign that I'm
starting to see come through.
The other area that I see is incredibly encouraging
is the world of open source.
We're starting to see a bit more around data
sharing, around data observations, around
algorithms that are being published now in
open source as well.
What I would love is, and I guess this is
a challenge not only to myself but also to
the industry, can we get past the open data
stuff but really start thinking about open
data model sharing?
I think, when you can start deriving data
models that are more relevant to either disease
or other such areas, the data flows that come
through actually feed into a significant amount
of data prep work that no longer has to be
done.
I'm not saying it goes away, but at least
when you have an open data model, we're speaking
from the same platform.
In the absence of that today, this is where
you do start getting those nuances in data
clarity, data quality, and sometimes observations
that, frankly, may not be as intuitive as
what they appear.
We have an interesting question from Shelly
Lucas on Twitter.
Shelly, by the way, is a content marketer,
Internet influencer, strategy type of person
who is just among the very best out there.
I know Shelly well.
She asks, "Pharma has marketed directly to
consumers, but will it need a new engagement
model with this increased data sharing?"
Yeah.
Yes, absolutely.
Shelly, I think you hit the nail on the head.
If you think about the kind of work that we've
done in the past, content and engagement strategy
wasn't necessarily the top of the list.
At least, if it was, it wasn't always done
effectively because you have these long brand
plan sessions over a year where the ability
to change interventions through, let's call
it, the approval process was quite difficult
and often taking time.
I see there definitely needs to be a new engagement
model where the rep is, in fact, one of many
channels.
Historically, it's generally been rep led
conversations.
I don't think human interaction is going to
go away.
I still think there is a relevance for reps,
but the nature of what reps look like, I think
it's going to change more into this scientific
liaison and much deeper conversations around
patient archetypes, around genomic discussions,
around things that patients are actually searching
for answers.
Maybe it's a more sophisticated Dr. Google
[laughter], for example, that comes through,
and that becomes a new channel that's relevant.
I think content marketers, in general, and
the way in which we've been engaging, in general,
is going to go through a whole new world.
Even the notion of an agency today is already
going through its own challenges.
You're seeing this evolvement, if you will,
in the content marketing and marketing space,
in general.
Can I take that question with a fast-forward
10, 15, 20 years from now attitude?
Sure.
The thing that I keep thinking about at night
is, what if we had the data work for us and
what do we need to do in order to get there
in a safe way?
Right now, the diagnostic capability of pharmaceutical
companies to deliver personalized medicine,
we could literally be going to the doctor
and being told exactly what we should be taking,
right?
Mm-hmm.
Multiple companies could be getting that information
and analyzing it in real time to deliver very
personalized medicine.
On one hand, that is a really cool feature--
Yeah.
--where we're being taken care of.
On the other hand, there is a lot of data
privacy concerns going on.
I know Europe is at the forefront with GDPR
in this respect, and I know that there are
regulations coming in May around basically
what is the management of PII data look like.
I think these are some of the biggest questions
over the next 10, 15 years.
The almost ethical question is, how do we
enable pharma to know, essentially, our body
in a way that we feel comfortable with?
That's quite an engagement strategy.
On the subject of the ethical question, Milind,
let me direct to you that's coming from Zachary
Jeans on Twitter, a really interesting question.
He's asking, "What are the potential dark
sides of big pharma companies leveraging all
of this data," as Hicham was just describing?
Yeah.
Look.
I think one of the things we've done specifically
is established an ethics board, an external
ethics board, that looks at not only content
but also algorithms that appear in black boxes.
Honestly, my biggest fear, independent of
whether I work for a pharmaceutical company
or not, is exactly the same fear that you
have, which is, we talk about digital disruption
often in a highly positive way, but we can't
forget that there is the Black Mirror effect
in all of this.
Yeah.
Which is, the outcome of abuse.
Sadly, this happens across the board.
It's probably happening already today.
Do we really know how our data is actually
being moved across the board and is being
used for different reasons?
I would hope most of it is positive, but I
think we can all agree there's probably nefarious
things going on that we simply don't know
about.
I get the sense that, as a company, we need
to do everything we can.
I think there are new, let's call it, ethical
terms that we need to start addressing now
as a pharma company, as n industry body.
It's not just ethics in terms of what information
we can or cannot use.
There are a lot of intended use guidelines
around how we use data but, more importantly,
the ethics around the societal impact of that
data, the societal impact of black box algorithms
perhaps negating certain functions that physicians
do today.
We're already seeing this happen in radiology.
We're seeing this happen in numerous other
professions in the physician world.
My fear is, this is something that a pharma
company alone cannot handle.
I do think it's an industry, it's a healthcare
industry consortium thing that we need to
address.
I think that things like blockchain may bring
about a better-trusted relationship there
with our information sets and so forth.
To be honest with you, there are many things
that could be done negatively, none of which
I anticipate our company is doing today, and
I don't think, by design, they intend to do
that.
But, like anything in this world, the minute
you go online, your information could be used
ten ways to Sunday, and not all of it is going
to be great.
Hicham, Arsalan Khan is asking, in this same
vein, "What about data corruption that takes
place either on purpose or accidentally?
Who governs these open data models and the
data sharing," that you both have been advocating
so strongly and with very good reason, of
course?"
That is a great question.
One of the most important things of any data
transformation journey is lineage, understanding
everything about where the data was produced.
Much like it's very important to us to know
how our food was produced, it's very important
for us to know how the data landed, who transformed
it, why, and what.
We're trying really hard to do that, I think,
as a community of practitioners, without slowing
down results.
There are many, many ways.
AI and machine learning have been helping
us to do that now.
I'm seeing a lot of positive stuff, but it's
a quality standard that we have to keep.
Hicham raises a great point.
How can machine learning, AI, and these new
kinds of techniques that are based on data
help push innovation forward without suffering
the potential kind of issues that may happen
when data is aggregated and shared like we
were just describing?
Yeah, sure.
I think the first thing we need to recognize
is that AI and machine learning, any of these
coding languages is inherently a model, i.e.
it lacks morality.
It's the people who code them, the discipline,
and the ethical boundaries under which either
they've been raised or otherwise lends itself
to the behavior.
Also, once those algorithms go open, i.e.
they're part of an engagement plan, it depends
how we engage with them as well.
What do we train those algorithms on and the
data that gets trained into it?
When I think about how we approach this, one
area that I've been very adamant on is the
world of diversity and knowing that diversity
and diverse thinking, if you will, and people
coming from diverse backgrounds almost forms
the crux and basis under which you should
even deploy an algorithm, let alone design
it.
That's number one.
Number two, I think, as a society, we need
to start raising the bar on how we start engaging
with these platforms.
We've seen Microsoft had, unfortunately, a
disaster of an experience with an algorithm
or chatbot that they put out that unfortunately
was getting trained on by a 16-year-old or
a 15-year-old, rather vivacious teens that
were using all sorts of language and very
demoralizing kind of language, highly sexist.
They actually had to pull the chatbot off.
I think it's a two-sided equation.
Not all of it is just on the pharma side.
Most of it has to come from a diversity of
talent that you put into the program, but
then also the appropriate coaching then of
how we engage so that, as the model starts
enriching itself, you have the right ethical
guidance in terms of how we engage with it.
Any thoughts on this, Hicham?
The diversity point is probably the most important
one.
I could not agree more that challenging this
sort of technology from as many viewpoints
as possible is the best way forward.
I've been very optimistic about what we do
as a society and people, as for how we come
together.
We want to create that opportunity to see
things differently.
Yes.
Remember, the impact of AI and machine learning
is that it scales fast, and it's designed
to learn and reinforces biases.
Yeah.
It's a self-optimizing system.
The opportunity to challenge it, the design
of multiple points of view, that must be inherent
in how we go about it here.
How do we do this right?
Milind, how do we do it right?
I think it comes in talent acquisition, too.
When you put teams onboard, make sure they
do have opposing views.
I think the echo chamber phenomenon that we're
seeing, be it in politics or otherwise, is
something that's come culturally over a period
of time, but it doesn't mean it has to be
integrated into our algorithms, let alone
our data systems.
The data exists.
It's what we do with and how we do with it
that's important.
I think it starts there.
Putting teams together, an appropriate ethics
board around it or some kind of principles
of design that go around it, and really start
thinking about, as a human, how would you
feel about this, these responses and this
engagement?
It comes down to delivering a customer experience
that is relevant, an engaging experience that's
relevant and meaningful.
We have about two minutes left, and so in
140 characters or so -- [Laughter]
[Laughter]
[Laughter]
Hicham, I'm going to direct this one to you.
[Laughter] Milind just raised the issue of
customer experience.
What does customer experience in healthcare
actually mean?
What does it consist of?
In ten seconds, can you summarize that entire
body of knowledge and way of looking at the
world?
Honestly, it's abstracting away most of everything
we talked about and not making it complex.
Just better patient outcome.
It's more transparent, and it comes with fewer
caveats.
Leave that to the people who are working for
those patients and make that relationship
clear.
I love that.
Milind, your final take on this notion of
customer experience and algorithms.
Yeah.
Make it convenient.
Make it fun.
Make it meaningful.
Boy, you guys are quick.
[Laughter] Any final, final thoughts?
We're really out of time, but how about final
thoughts from each of you.
Who wants to go first?
All right, final thoughts.
I think one of the opportunities specifically
in healthcare is that it is a system that
has all of this compartmentalization.
You do have the providers.
You have the manufacturers and the drug makers.
You have the people doing research in a more
isolated way.
I really think data is going to bring the
system together.
I think it's a complex system for a reason.
It's amazing what we've achieved, but I think
we'll be able to iron out a lot of the kinks
in the next coming years and make the whole
system feel like more of one thing for a patient.
I know that's a huge source of confusion and
anxiety for people who are just trying to
get better.
Yeah, I would just leave it with, I think
if more people contracted obsessive compulsive
data disorder, the better it's going to be.
[Laughter]
I think when we're aware and conscious of
how we use information and for what purpose
and be relentless about the problem you're
trying to solve and ask more questions, I
think the better it's going to be.
I'll leave it with saying don't let technology
be the lead in this instance.
Really focus on, what is the experience you're
trying to deliver; what is the problem you're
trying to solve?
Let technology really just do its role as
an enabler.
Okay.
Well, with that, what an interesting and very,
very fast discussion.
I would like to say thank you to Milind Kamkolkar,
who is the chief data officer at Sanofi.
Milind, thanks.
It's great having you here, and I hope you'll
come back and do it again.
Absolutely.
My pleasure.
I would like to also say thank you to Hicham
Oudghiri, who is the co-founder and CEO of
Enigma Data.
Hicham, thank you for being here.
I hope that you'll come back and do it again
as well.
My pleasure.
Thanks so much for having me.
You have been watching Episode #275 of CxOTalk.
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