On the Path to a New Normal: Gain Insights and Reassurance Using Data and Artificial Intelligence
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On the Path to a New Normal:
Gain Insights and Reassurance
Using Data and Artificial Intelligence
A discussion on how AI is the new pandemic response team member for helping businesses
reduce risk of failure and innovate with confidence.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett
Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of AI
Innovation podcast series.
I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for
this ongoing discussion on the latest insights into artificial intelligence (AI) strategies.
As businesses and IT strategists plan their path to a new normal throughout the COVID-
19 pandemic and recovery, AI and data science are proving impactful and
By leveraging the latest tools and gaining data-driven inferences, architects and analysts
are both effectively managing the pandemic response -- and giving more people better
ways to improve their path to the new normal.
Stay with us here and now as we examine how AI forms the indispensable pandemic
response team member for helping businesses reduce risk of failure and innovate with
confidence. To learn more about the analytics, solutions, and methods that support
advantageous reactivity -- amid unprecedented change -- we are joined by two experts.
Please join me in welcoming Arti Garg, Head of
Advanced AI Solutions and Technologies, at Hewlett
Packard Enterprise (HPE). Welcome, Arti.
Arti Garg: Good morning.
Gardner: We’re also here with Glyn Bowden, Chief
Technologist for AI and Data, HPE Pointnext Services.
Glyn Bowden: Thanks, Dana.
Gardner: Arti, why should we look to data science and AI
to help at a time when there’s not much of a historical record to rely on? It seems we’re
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in a time when people are in uncharted waters in dealing with the complexities of the
novel coronavirus pandemic.
AI, ML assist in the fight against COVID-19
Garg: Because we don’t have a historical record, I think data science and AI are
proving to be particularly useful right now in understanding this new disease and how we
might potentially better treat it, manage it, and find a vaccine for it. And that’s because at
this moment in time, raw data that are being collected from medical offices and through
research labs are the foundation of what we know about the pandemic.
This is an interesting time because, when you know a disease, medical studies and
medical research are often conducted in a very controlled way. You try to control the
environment in which you gather data, but unfortunately, right now, we can’t do that. We
don’t have the time to wait.
And so instead, AI -- particularly some of the more advanced AI techniques -- can be
helpful in dealing with unstructured data or data of multiple different formats. It’s
therefore becoming very important in the medical research community to use AI to better
understand the disease. It’s enabling some unexpected and very fruitful collaborations,
from what I’ve seen.
Gardner: Glyn, do you also see AI delivering more, even though we’re in uncharted
Bowden: The benefits of something like machine
learning (ML), for example, which is a subset of AI, is
very good at handling many, many features. So with a
human being approaching these projects, there are only
so many things you can keep in your head at once in
terms of the variables you need to consider when
building a model to understand something.
But when you apply ML, you are able to cope with
millions or billions of features simultaneously -- and then
simulate models using that information. So it really does
add the power of a million scientists to the same problem
we were trying to face alone before.
Gardner: And is this AI benefit something that we can apply in many different avenues?
Are we also modeling better planning around operations, or is this more research and
development? Is it both?
Garg: There are two ways to answer the question of what’s happening with the use of AI
in response to the pandemic. One is actually to the practice of data science itself.
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One is, right now data scientists are
collaborating directly with medical science
research and learning how to incorporate
subject matter expertise into data science
models. This has been one of the challenges
preventing businesses from adopting AI in more
complex applications. But now we’re developing
some of the best-practices that will help us use
AI in a lot of domains.
In addition, businesses are considering the use of AI to help them manage their
businesses and operations going forward. That includes things such as using computer
vision (CV) to ensure that social distancing happens with their workforce, or other types
of compliance we might be asked to do in the future.
Gardner: Are the pressures of the current environment allowing AI and data science
benefits to impact more people? We’ve been talking about the democratization of AI for
some time. Is this happening more now?
More data, more opinions, more options
Bowden: Absolutely, and that’s both a positive and a negative. The data around the
pandemic has been made available to the general public. Anyone looking at news sites
or newspapers and consuming information from public channels -- accessing the
disease incidence reports from Johns Hopkins University, for example -- we have a
steady stream of it. But those data sources are all over the place and are being thrown
to a public that is only just now becoming data-savvy and data-literate.
As they consume this information, add their context, and get a personal point of view,
that is then pushed back into the community again -- because as you get data-centric
you want to share it.
So we have a wide public feed -- not only from universities and scholars, but from the
general public, who are now acting as public data scientists. I think that’s creating a
Garg: I agree. Making such data available exposes pretty much anyone to these
amazing data portals, like Johns Hopkins University has made available. This is great
because it allows a lot of people to participate.
It can also be a challenge because, as I mentioned, when you’re dealing with complex
problems you need to be able to incorporate subject matter expertise into the models
you’re building and in how you interpret the data you are analyzing.
And so, unfortunately, we’ve already seen some cases -- blog posts or other types of
analysis -- that get a lot of attention in social media but are later found to be not taking
Right now, data scientists are
collaborating directly with
medical science research and
learning how to incorporate
subject matter expertise into
data science models.
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into account things that people who had spent their careers studying epidemiology, for
example, might know and understand.
Gardner: Recently, I’ve seen articles where people now are calling this a misinformation
pandemic. Yet businesses and governments need good, hard inference information and
data to operate responsibly, to make the best decisions, and to reduce risk.
What obstacles should people overcome to make data science and AI useful and
integral in a crisis situation?
Garg: One of the things that’s underappreciated is that a foundation, a data platform,
makes data managed and accessible so you can contextualize and make stronger
decisions based on it. That’s going to be critical. It’s always critical in leveraging data to
make better decisions. And it can mean a larger investment than people might expect,
but it really pays off if you want to be a data-driven organization.
Know where your data comes from
Bowden: There are a plethora of obstacles. The kind that Arti is referring to, and that is
being made more obvious in the pandemic, is the way we don’t focus on the provenance
of the data. So, where does the data come from? That doesn’t always get examined,
and as we were talking about a second ago, the context might not be there.
All of that can be gleaned from knowing the source of the data. The source of the data
tends to come from the metadata that surrounds it. So the metadata is the data that
describes the data. It could be about when the data was generated, who generated it,
what it was generated for, and who the intended consumer is. All of that could be part of
Organizations need to look at these data
sources because that’s ultimately how
you determine the trustworthiness and
value of that data.
Now it could be that you are taking data from external sources to aggregate with internal
sources. And so the data platform piece that Arti was referring to applies to properly
bringing those data pieces together. It shouldn’t just be you running data silos and
treating them as you always treated them. It’s about aggregation of those data pieces.
But you need to be able to trust those sources in order to be able to bring them together
in a meaningful way.
So understanding the provenance of the data, understanding where it came from or
where it was produced -- that’s key to knowing how to bring it together in that data
Organizations need to look at these
data sources because that’s ultimately
how you determine the
trustworthiness and value of that data.
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Gardner: Along the lines of necessity being the mother of invention, it seems to me that
a crisis is also an opportunity to change culture in ways that are difficult otherwise. Are
we seeing accelerants given the current environment to the use of AI and data?
AI adoption on the rise from research to retail
Garg: I will answer that question from two different perspectives. One is certainly the
research community. Many medical researchers, for example, are doing a lot of work
that is becoming more prominent in people’s eyes right now.
I can tell you from working with researchers in this community and knowing many of
them, that the medical research community has been interested and excited to adopt
advanced AI techniques, big data techniques, into their research.
It’s not that they are doing it for the first time, but definitely I see an acceleration of the
desire and necessity to make use of non-traditional techniques for analyzing their data. I
think it’s unlikely that they are going to go back to not using those for other types of
studies as well.
In addition, you are definitely going to see AI
utilized and become part of our new normal
in the future, if you will. We are already
hearing from customers and vendors about
wanting to use things such as CV to monitor
social distancing in places like airports
where thermal scanning might already be
used. We’re also seeing more interest in
using that in retail.
So some AI solutions will become a common part of our day-to-day lives.
Gardner: Glyn, a more receptive environment to AI now?
Bowden: I think so, yes. The general public are particularly becoming used to AI playing
a huge role. The mystery around it is beginning to fade and it is becoming far more
accepted that AI is something that can be trusted.
It does have its limitations. It’s not going to turn into Terminator and take over the world.
The fact that we are seeing AI more in our day-to-day lives means people are beginning
to depend on the results of AI, at least from the understanding of the pandemic, but that
drives that exception.
When you start looking at how it will enable people to get back to somewhat of a normal
existence -- to go to the store more often, to be able to start traveling again, and to be
able to return to the office -- there is that dependency that Arti mentioned around video
You are definitely going to see AI
utilized and become part of our new
normal in the future … some AI
solutions will become a common
part of our day-to-day lives.
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analytics to ensure social distancing or temperatures of people using thermal detection.
All of that will allow people to move on with their lives and so AI will become more
I think AI softens the blow of what some people might see as a civil liberty being eroded.
It softens the blow of that in ways and says, “This is the benefit already and this is as far
as it goes.” So it at least forms discussions whenever it was formed before.
Garg: One of the really valuable things happening right now are how major news
publications have been publishing amazing infographics, very informative, both in terms
of the analysis that they provide of data and very specific things like how restaurants are
recovering in areas that have stay-in-place orders.
In addition to providing nice
visualizations of the data, some of the
major news publications have been
very responsible by providing captions
and context. It’s very heartening in
some cases to look at the comments
sections associated with some of
these infographics as the general public really starts to grapple with the benefits and
limitations of AI, how to contextualize it and use it to make informed decisions while also
recognizing that you can go too far and over-interpret the information.
Gardner: Speaking of informed decisions, to what degree you are seeing the C-suite --
the top executives in many businesses -- look to their dashboards and query datasets in
new ways? Are we seeing data-driven innovation at the top of decision-making as well?
COVID-19 data inspire C-suite innovation
Bowden: The C-suite is definitely taking a lot of notice of what’s happening in the sense
that they are seeing how valuable the aggregation of data is and how it’s forwarding
responses to things like this.
So they are beginning to look internally at what data sources are available within their
own organizations. I am thinking now about how do we bring this together so we can get
a better view of not only the tactical decisions that we have to make, but using the macro
environmental data, and how do we now start making strategic decisions, and I think the
value is being demonstrated for them in plain sight.
So rather than having to experiment, to see if there is going to be value, there is a full
expectation that value will be delivered, and now the experiment is how much they can
draw from this data now.
It’s very heartening … as the general
public really starts to grapple with the
benefits and limitations of AI, [and to
understand] how to contextualize it
and use it to make informed decisions.
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Garg: It’s a little early to see how much this is going change their decision-making,
especially because frankly we are in a moment when a lot of the C-suite was already
exploring AI and opening up to its possibilities in a way they hadn’t even a year ago.
And so there is an issue of timing here. It’s hard to know which is the cause and which is
just a coincidence. But, for sure, to Glyn’s point, they are dealing with more change.
Gardner: For IT organizations, many of them are going to be facing some decisions
about where to put their resources. They are going to be facing budget pressures. For IT
to rise and provide the foundation needed to enable what we have been talking about in
terms of AI in different sectors and in different ways, what should they be thinking about?
How can IT make sure they are accelerating the benefits of data science at a time when
they need to be even more choosy about how they spend their dollars?
IT wields the sword to defend, deliver digitization
Bowden: With IT particularly, they have never had so much focus as right now, and
probably budgets are responding in a similar way. This is because everyone has to now
look at their digital strategy and their digital presence -- and move as much as they can
online to be able to be resistant to pandemics and at-risk situations that are like this.
So IT has to have the sword, if you like, in that battle. They have to fix the digital
strategy. They have to deliver on that digital promise. And there is an immediate
expectation of customers that things just will be available online.
If you look at students in universities, for example, they assume that it will be a very
quick fix to start joining Zoom calls and to be able to meet that issue right away. Well,
actually there is a much bigger infrastructure that has to sit behind those things in order
to be able to enable that digital strategy.
So, there is now an AI
movement that will get
driven purely from the fact
that so much more
commerce and business
is going to be digitized.
Gardner: Let’s look to some more examples and associated metrics. Where do you see
AI and data science really shining? Are there some poster children, if you will, of how
organizations -- either named or unnamed -- are putting AI and data science to use in
the pandemic to mitigate the crisis or foster a new normal?
Garg: It’s hard to say how the different types of video analytics and CV techniques are
going to facilitate reopening in a safe manner. But that’s what I have heard about the
most at this time in terms of customers adopting AI.
There is now an AI movement that will get driven
purely from the fact that so much more
commerce and business is going to be digitized.
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In general, we are at very early stages of how an organization is going to decide to adopt
AI. And so, for sure, the research community is scrambling to take advantage of this, but
for organizations it’s going to take time to further adopt AI into any organization. If you do
it right, it can be transformational. Yet transformational usually means that a lot of things
need to change -- not just the solution that you have deployed.
Bowden: There’s a plethora of examples from the medical side, such as how we have
been able to do gene analysis, and those sorts of things, to understand the virus very
quickly. That’s well-known and well-covered.
The bit that’s less well covered is AI
supporting decision-making by
governments, councils, and civil
bodies. They are taking not only the
data from how many people are
getting sick and how many people
are in hospital, which is very
important to understand where the
disease is but augmenting that with
data from a socioeconomic situation. That means you can understand, for example,
where an aging population might live or where a poor population might live because
there’s less employment in that area.
The impact of what will happen to their jobs, what will happen if they lose transport links,
and the impact if they lose access to healthcare -- all of that is being better understood
by the AI models.
As we focus on not just the health data but also the economic data and social data, we
have a much better understanding of how society will react, which has been guiding the
principles that the governments have been using to respond.
So when people look at the government and say, “Well, they have come out with one
thing and now they are changing their minds,” that’s normally a data-driven decision and
people aren’t necessarily seeing it that way.
So AI is playing a massive role in getting society to understand the impact of the virus --
not just from a medical perspective, but from everything else and to help the people.
Gardner: Glyn, this might be more apparent to the Pointnext organization, but how is AI
benefiting the operational services side? Service and support providers have been put
under tremendous additional strain and demand, and enterprises are looking for
efficiency and adaptability.
Are they pointing the AI focus at their IT systems? How does the data they use for
running their own operations come to their aid? Is there an AIOps part to this story?
AI supports decision-making by
governments, councils, and civil
bodies, which are taking not only the
data from how many people are getting
sick and how many people are in
hospital … but augmenting that with
data from a socioeconomic situation.
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AI needs people, processes
Bowden: Absolutely, and there has definitely become a drive toward AIOps.
When you look at an operational organization within an IT group today, it’s surprising
how much of it is still human-based. It’s a personal eyeball looking at a graph and then
determining a trend from that graph. Or it’s the gut feeling that a storage administrator
has when they know their system is getting full and they have an idea in the back of their
head that last year something happened seasonally from within the organization making
decisions that way.
We are therefore seeing systems such as HPE’s InfoSight start to be more prominent in
the way people make those decisions. So that allows plugging into an ecosystem
whereby you can see the trend of your systems over a long time, where you can use AI
modeling as well as advanced analytics to understand the behavior of a system over
time, and how the impact of things -- like everybody is suddenly starting to work remotely
– does to the systems from a data perspective.
So the models-to-be need to catch up in that sense as well. But absolutely, AIOps is
desirable. If it’s not there today, it’s certainly something that people are pursuing a lot
more aggressively than they were before the pandemic.
Gardner: As we look to the future, for those organizations that want to be more data-
driven and do it quickly, any words of wisdom with 20/20 hindsight? How do you
encourage enterprises -- and small businesses as well -- to better prepare themselves to
use AI and data science?
Garg: Whenever I think about an organization adopting AI, it’s not just the AI solution
itself but all of the organizational processes -- and most importantly the people in an
organization and preparing them for the adoption of AI.
I advise organizations that want to use AI and corporate data-driven decision-making to,
first of all, make sure you are solving a really important problem for your organization.
Sometimes the goal of adopting AI becomes more important than the goal of solving
some kind of problem. So I always encourage any AI initiative to be focused on really
Use your AI initiative to do something
really valuable to your organization
and spend a lot of time thinking
about how to make it fit into the way
your organization currently works.
Make it enhance the day-to-day
experience of your employees because, at the end of the day, your people are your most
Make AI enhance the day-to-day
experience of your employees
because, at the end of the day, your
people are your most valuable assets.
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Those are important non-technical things that are non-specific to the AI solution itself
that organizations should think about if they want the shift to being AI-driven and data-
driven to be successful.
For the AI itself, I suggest using the simplest-possible model, solution, and method of
analyzing your data that you can. I cannot tell you the number of times where I have
heard an organization come in saying that they want to use a very complex AI technique
to solve a problem that if you look at it sideways you realize could be solved with a
checklist or a simple spreadsheet. So the other rule of thumb with AI is to keep it as
simple as possible. That will prevent you from incurring a lot of overhead.
Gardner: Glyn, how should organizations prepare to integrate data science and AI into
more parts of their overall planning, management, and operations?
Bowden: You have to have a use case with an outcome in mind. It’s very important that
you have a metric to determine whether it’s successful or not, and for the amount of
value you add by bringing in AI. Because, as Arti said, a lot of these problems can be
solved in multiple ways; AI isn’t the only way and often isn’t the best way. Just because it
exists in that domain doesn’t necessarily mean it should be used.
The second part is AI isn’t an on/off switch; it’s an iteration. You can start with something
small and then build into bigger and bigger components that bring more and more data
to bear on the problem, as well as then adding new features that lead to new functions
The other part of it is: AI is part of an ecosystem; it
never exists in isolation. You don’t just drop in an
AI system on its own and it solves a problem. You
have to plug it into other existing systems around
the business. It has data sources that feed it so that it can come to some decision.
Unless you think about what happens beyond that -- whether it’s visualizing something
to a human being who will make a decision or automating a decision – it could really just
be hiring the smartest person you can find and locking them in a room.
Pandemic’s positive impact: in data we trust
Gardner: I would like to close out our discussion with a riff on the adage of, “You can
bring a horse to water but you can’t make them drink.” And that means trust in the data
outcomes and people who are thirsty for more analytics and who want to use it.
How can we look with reassurance at the pandemic as having a positive impact on AI in
that people want more data-driven analytics and will trust it? How do we encourage the
perception to use AI? How is this current environment impacting that?
AI is part of an ecosystem;
it never exists in isolation.
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Garg: The fact that so many people are checking the trackers of how the pandemic is
spreading and learning through a lot of major news publications as they are doing a
great job of explaining this. They are learning through the tracking to see how stay-in-
place orders affect the spread of the disease in their community. You are seeing that
We are seeing growth and trust in how analyzing
data can help make better decisions. As I
mentioned earlier, this leads to a better
understanding of the limitations of data and a
willingness to engage with that data output as not
just black or white types of things.
As Glyn mentioned, it’s an iterative process, understanding how to make sense of data
and how to build models to interpret the information that’s locked in the data. And I think
we are seeing that.
We are seeing a growing desire to not only view this as some kind of black box that sits
in some data center -- and I don’t even know where it is -- that someone is going to
program, and it’s going to give me a result that will affect me. For some people that
might be a positive thing, but for other people it might be a scary thing.
People are now much more willing to engage with the complexities of data science. I
think that’s generally a positive thing for people wanting to incorporate it in their lives
more because it becomes familiar and less other, if you will.
Gardner: Glyn, perceptions of trust as an accelerant to the use of yet more analytics
and more AI?
Bowden: The trust comes from the fact that so many different data sources are out
there. So many different organizations have made the data available that there is a
consistent view of where the data works and where it doesn’t. And that’s built up the
capability of people to accept that not all models work the first time, that experimentation
does happen, and it is an iterative approach that gets to the end goal.
I have worked with customers who, when they saw a first experiment fall flat because it
didn’t quite hit the accuracy or targets they were looking for, they ended the experiment.
Whereas now I think we are seeing in real time on a massive scale that it’s all about
iteration. It doesn’t necessarily work the first time. You need to recalibrate, move on, and
do refinement. You bring in new data sources to get the extra value.
What we are seeing throughout this pandemic is the more expertise and data science
you throw in an instance, the much better the outcome at the end. It’s not about that first
result. It’s about the direction of the results, and the upward trend of success.
We are seeing growth
and trust in how
analyzing data can help
make better decisions.
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Gardner: I’m afraid we’ll have to leave it there. We have been exploring how AI and data
science are proving impactful and indispensable as business architects chart their path
to a new normal.
And we have learned how AI is the new pandemic team member for helping businesses
reduce risk of failure and innovate with confidence. So please join me in thanking our
guests, Arti Garg, Head of Advanced AI Technologies and Solutions at HPE. Thank you.
Garg: Thank you.
Gardner: And we have been with Glyn Bowden, Chief Technologist for AI and Data at
HPE Pointnext Services. Thank you.
Bowden: Thanks, Dana.
Gardner: And a big thank you as well to our audience for joining us for this sponsored
BriefingsDirect Voice of AI Innovation discussion. I’m Dana Gardner, Principal Analyst at
Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-
Thanks again for listening. Please pass this along to your IT community, and do come
back next time.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett
A discussion on how AI is the new pandemic response team member for helping businesses
reduce risk of failure and innovate with confidence. Copyright Interarbor Solutions, LLC, 2005-
2020. All rights reserved.
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