>> Live from the MIT campus
in Cambridge, Massachusetts, it's theCUBE
covering the 12th annual MIT Chief Data Officer
and Information Quality Symposium,
brought to you by SiliconANGLE media.
>> We are wrapping up a day of coverage here
at theCUBE for MIT CDOIQ here in Cambridge, Massachusetts.
I'm Rebecca Knight, along with Peter Burris.
We've been here all day, folks.
We've learned a lot, we've had a lot of great
conversations here, a lot of lively debate and interest.
So, Peter, this morning, you were talking about
this fundamental idea that data needs to be viewed
as an asset within an organization.
Obviously we're here with a bunch of people
who are drinking that Kool-Aid, but--
>> Living that Kool-Aid.
>> Living that Kool-Aid, embodying that Kool-Aid.
So based on what we heard today,
do you think that business has caught up?
>> Well, I would say two things.
First of all, this has been, as you said,
it's been absolutely marvelous
series of conversations in many respects.
This is what theCUBE is built for, right?
Smart people in conversation on camera.
And we've had some smart people here today.
What I got out of it on that particular issue is that
there is general agreement among CDOs
that they have to start introducing this notion of asset
and what that means in their business.
There's not general agreement,
or there's a general, I guess not agreement,
but there's general concern that we still aren't there yet.
I think that everybody that we talk to I think,
would come back and say, yes we grew those practices,
but the conventions are not as established and mature
as they need to be for everybody in our business
to agree so that we can acculturate.
Now we did hear some examples of folks that have done it.
So that great BBDA case we talked about was an example.
There was a company that is actually becoming,
is really truly institutionalizing,
acculturating that notion of data as an asset
that performs work, but I think we've got
general agreement that that's
the right way of thinking about it,
but also a recognition that more work needs to be done,
and that's why conferences like this are so important.
>> Well, one of the things that really struck me
about what BBDA did was this education campaign
of its 130,000 employees, and as you said,
really starting from the ground and saying,
this is how we're going to do things.
This is who we are as an organization.
>> Yeah, and it was a great conversation
because one of the points I made was, specifically,
that BBDA is a bank.
It is an information-based business
that has very deep practices and principles
associated with information,
and when they decided that they need to move beyond that,
they were able to get the entire bank
to adopt a set of practices
that are leading to new types of engagement models,
product orientations, service capabilities.
That's a pretty phenomenal feat.
So, it's happening and it can get done,
and there are examples of it happening.
Another thing we talked about was the fact that
over the course of the next few years, one of the big,
one of the most exciting things about digital business
is not just digital business and digital,
what people call digital maintenance,
but that transformation practices.
That way forward.
And we talked about the idea of how you wrapper
existing goods and services and offerings with data
to turn them into something else,
and the incumbents are going to find ways of doing that
so they can re-establish themselves as leaders
in a lot of different markets.
>> And that's what will separate the people
who really get this from the people who,
or from the organizations that are going to lag.
>> Yeah, we're starting to hear that a lot more
from clients, is that the idea increasingly is,
okay, I've already got customers.
I've already got offers.
How do I wrapper them?
Using a term we heard from a professor at MIT.
How do I wrapper them to improve them utilizing data?
And that's a big challenge, but it's happening.
>> One of the other fun interviews we had
was all about clinical trials,
and the use of data in these clinical trials.
There are so many challenges about,
with clinical trials because of the time it takes
to conduct one of these, the cost that it takes,
and then at the end you are dealing with
patients who just say,
"oh, I think I'm not going to take that drug today."
Or other factors that take place here.
I mean, what do you see, I know your dad is a physician,
what do you see as the most exciting thing
about the use of data in clinical trials,
but also just in the healthcare industry in general?
>> Well, so what we heard,
and it was a great combination of interviews,
but what we heard is that to bring a new drug to market
can cost $4 billion and take 15 years.
And the question is, can data, first off,
reduce the cost of bringing a new drug to market?
And we heard numbers like, yeah, by $1 billion or even more.
So imagine having the cost
of bringing a new drug to market,
but also reducing the time by as much as two thirds.
That's very, very powerful stuff when we come down to it.
And as you said, the way you do that
is you have to protect your data
to make sure you're complying with various regulations,
but as you said, for example,
sustaining someone in the trial
even though they're starting to feel better
because the drug's working.
Well, people opt out.
They abandon the trial.
Well can you use data to keep them tied in,
to provide new types of benefits
and new types of capabilities
so they want to sustain their participation in the trial.
>> Or at least the pharma company,
hey, this person's dropping out,
you need to explain that to the FDA,
and that's going to become a point, yeah.
>> Or you need to provide an incentive to keep them in.
>> Right.
>> Or another example that was used was,
if we can compress the amount of time,
but then recognize that we can sustain an engagement
with a patient and collect data longer,
that even though we can satisfy
the specific regulatory mandates of a trial,
shorter, we can still be collecting data
because we have a digital engagement model
as part of this whole process
subject to keeping privacy in place
and ownership notions in place, and everything else,
complying with regulatory notions.
So that is I think a very powerful example.
And again, Santi, Dr. Santi was talking specifically about
how ERT is helping to accelerate this whole process
because over the course of the next dozen years,
we're going to learn more about people,
the genome is going to become better understood.
Genomics is going to continue to evolve.
Data is going to become increasingly central
to how we think about defining disease
and disease processes,
and one of the key responses is to learn from that
and apply data so that we can more rapidly build
the new procedures, devices, and drugs
that are capable of responding.
>> When we're thinking about what keeps
the chief data officers up at night,
we know that data security, data fidelity, privacy,
the other thing we really heard about
from Melana Goldban from PwC Accelerator
is the idea about bias, and that is a real concern.
From the way she is talking about, it sounded as though
companies are more aware of this.
It really is an organizational challenge
that they recognize that not just matters for social reasons
but really for business reasons too, frankly.
It affects your bottom line.
Where do you come out on that?
Do you think we're moving in the right direction?
>> First of all, it was a great interview,
and a lot of what Melana said was illuminating to me,
and I agree with virtually everything she said.
We're doing a piece of research on that right now.
I would say that, in fact, most companies are not fully
factoring the role that bias plays
in a lot of different ways.
That's one of the things that absolutely must happen
as part of the acculturation process,
what's known as evidence-based management
starts to take grip more within businesses
is to understand not only what bias
introduced into data now,
but as you create derivatives on that data,
how that bias changes, delays that data.
And that is a relatively poorly understood problem.
>> But it's a big problem.
>> Oh, it's going to be even bigger
because we're going to utilize AI and it's actually
going to limit the range of options
that people consider as they make a decision,
or make the decisions directly for the individual,
act on behalf of the brand, what we call agency,
a system of agency.
And not understanding that range,
not having it be auditable,
not understanding what the inherent bias is
can very quickly send a business off the rails
in unexpected ways.
So we're devoting a lot of time and energy
into understanding that right now.
But here's the challenge, that we've got
business decision makers who are very familiar
with certain kinds of information.
There's nobody gets to be the CEO or the COO
or a senior person in business if they don't have
a pretty decent understanding of findings.
So financial information is absolutely adopted
within the board room and the senior ranks of management
in virtually all businesses of any consequential size today.
What we're asking them to do
is to learn about wholly new classes of data.
New data conventions, what it means, how to apply it,
how you should factor it,
how to converge agreement around things,
that allows them to be as mature in their use
of customer data or production data or partner data
or any other number of metrics as they are
with financial data.
That's a real tall order.
It's one of the significant challenges
that a lot of businesses face today.
So it's not that they don't get data
or they don't understand data.
It's that the sources of data
and therefore the range of options
that are going to be shaped by data
are becoming that much more significant in business.
>> And it's how they need to think about data too.
I mean I was really struck by Tom Sasala
at the very beginning saying,
one of the reasons the intelligence community
didn't predict 9/11 is that we didn't have people
who were thinking like Hollywood people,
thinking audaciously enough about what could happen
and that similarly we need to have business leaders
and executives, who may be very good at crunching numbers,
really think much more broadly about the kinds of--
>> And Tom is absolutely right.
We also, cuz I was very close to the DoD at the time,
there was serious confirmation bias
that was going on at that time too.
>> Exactly.
But clearly he's right, that the objective
is for executives to, as a group,
acknowledge the powerful role that data can play,
have a data-first mentality as opposed to a bias
or experience-first mentality.
Because my experience is very private
relative to your experience.
And it takes a lot of time for us to negotiate that
before we can make a very, very consequential move.
That's not going to go away.
We're human beings.
But we increasingly need to look at data,
which can provide a common foundation
for us to build our biases upon
so that we can be more specific
and more transparent about articulating my interpretations.
You can't start doing that until you are better,
more willing to utilize data as a potentially
unifying tool and mechanism for thinking about,
thinking about how we move forward with something.
>> That's great.
And it's a great way to end our day of coverage
here at M.I.T CDOIQ.
Thank you so much.
It's been a pleasure, >> As always, Rebecca.
>> hosting with you.
And thanks to the crew and everyone here.
It's been really a lot of fun.
I'm Rebecca Knight for Peter Burris.
We will see you next time on theCUBE.
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