>> Man: From around the
globe, it's the Cube
with digital coverage of
MIT Chief Data Officer
and information quality symposium
brought to you by Silicon Angle Media.
>> Hello this is Dave Vallante
and welcome back to
our continuing coverage
of the MIT chief data officer CDOIQ event.
Inderpal Bhandari is here.
He's a leading voice in the CDO community
and a long time cube alum,
Inderpal great to see you
thanks for coming on for
this special program.
>> My pleasure.
>> So when you and I first met,
you laid out what I thought was,
one of the most cogent sort
of frameworks to understand
what a CDO's job was,
where the priorities should be.
And one of those was really understanding
how data contributes to the monetization
(indistinct) aligning with
the lines of business,
a number of other things.
And that was several years ago,
a lot has changed since then.
We've been doing this
conference since probably 2013,
and back then, Hadoop
was coming on strong.
A lot of CDOs didn't want
to go near the technology.
That's beginning to change
CDOs and CTOs are becoming
much more aligned at the hip.
The reporting organizations have changed,
but I'd love your perspective
on what you've observed
as changing in the CDO role
over the last half decade or so.
>> Well, Dave you know that
I became a Chief Data Officer in 2006.
December 2006, and I've
done this job now four times
four major organizations.
I've created of the organization
from scratch, each time.
Now in December of 2006, when
I became Chief Data Officer,
there were only four Chief
Data Officer globally.
And I was the first in healthcare
and there were three others,
one in the internet,
one in credit cards,
one in banking.
And I think I'm the only one
I'm actually left standing,
still doing this job.
I don't know if that's a
good thing or a bad thing,
but like you noted, it
certainly has allowed me
to learn the craft,
and then also scripted
down to the level that,
I actually do think of
it purely as a craft
that is, I know going into a new job,
what I'm going to do day
one, et cetera, et cetera.
Now, the interesting
things that have unfolded,
obviously the profession's taken off.
There are literally thousands
of Chief Data Officers now,
and there are plenty of changes.
I think the main change with the job is
it's I think a little less
daunting in terms of convincing
the senior leadership
that it's needed,
because I think the
awareness at the CEO level
is much, much, much better
than what it was in 2006
across the board.
Now, having said that, I think
it is still only awareness,
I don't think that there is really
a deep understanding of those levels.
And so there's a lot of confusion
and which is why you will,
you kind of, and this is my theory,
but you saw all these
professionals take off
of the C titles like Chief Data Officer,
Chief Analytics Officer,
Chief Digital Officer
and Chief Technology Officer,
CIO of course has been
there for a long time.
And, but I think these newer C positions,
they're all very, very related
and they all kind of
went to the same need,
which had to do with
enterprise transformation,
digital transformation at enterprises,
chief Digital Officer that's another one
and people were all trying to
essentially feel the elephant
and they can only see part
of it at the senior levels.
And they came up with whichever role,
seemed most meaning to them,
but really all of us are
trying to do the same job,
which is to accelerate
digital transformation
in the enterprise.
Your comment about you
kind of see that the CTOs
and CDLs now partnering up
much more than in the past.
And I think that's inevitable.
The major driving force for
that is in my view anyway,
is artificial intelligence.
As people try to infuse
artificial intelligence,
then it's very technical field still.
It's not something that,
you can just head over to somebody who has
the business chops,
but not the deep technical
chops to pull that off.
And so in the case of Chief Data Officers
that do have the technical jobs,
you'll see them also pretty
much heading up the AI effort
internally.
And as I do for in the IBM case,
where you building the data
and AI enablement
internal platform for IBM.
But I think in other cases you've got
Chief Data Officers who are coming in
from a different angle,
they will partner up with the
CTO now because they have to,
otherwise you cannot get AI
infused into the organization.
>> So there were a lot of
other priorities, obviously.
I mean, certainly digital transformation.
We've been talking about it for years,
but still in many organizations,
there was a sense of,
well, not on my watch.
Maybe a sense of complacency
or maybe just other priorities.
COVID obviously has changed that.
Now a hundred percent of the
companies that we talked to
are really putting this
digital transformation on
the, on the front burner.
So, how has that changed the role of CDO?
Is it just been Inderpal and
acceleration of that reality
or has it also somewhat
altered the swim lanes?
>> I think it's both actually.
So I have a way of looking at this
in my mind, the CDO role, right.
If look at it from a business perspective,
they're looking for three things.
The CEO is looking for
three things from the CDL.
One is this person is going to
help with the revenue of the company
by enabling the production
of new products.
New products resulting in
new revenue and so forth.
That's kind of one aspect
of the monetization.
Another aspect is the CDO is going to
help with the efficiency
within the organization,
by making data a lot more accessible,
as well as enabling insights
that reduce end to end cycle
time for major processes.
And so that's another way that
they're going to help monitor.
And the last one is risk reduction.
Like they've got to reduce the risk
as regulations and as you
have the cyber security
exposure on incidents that
just keep accelerating as well.
You got to have to also
step in and help with that.
So every CDL,
the way their senior
leadership looks at them
is some mix of the three.
And in some cases,
one is given more importance
than the other and so forth,
but that's how they are
essentially looking at it.
Now, I think what digital
transformation has done
is it's managed to accelerate,
accelerate all three of these outcomes
because you need to attend to
all three as you move forward.
But I think that the individual
balance that's struck
for individual CDOs really
depends on their company,
their situation, who their peers are,
who is actually leading the
transformation and so forth.
>> You know, in the value pie.
A lot of the early or
the activity around CDOs
sort of emanated from
the quality portions of the organization.
It was sort of a
compliance, weighted a role,
not necessarily,
(laughs) when you
started your own journey,
you obviously have been
focused on monetization,
how data contributes to that.
But you saw that generally organizations,
even if they didn't have a CDO,
they had this sort of
back office clients thing
that has totally changed
in the value equation.
It's really much more about
insights as you mentioned.
So one of the big changes
we've seen in the organization
is that data pipeline, you
mentioned end to end cycle time.
And I'd like to dig into that a little bit
because you and I have talked about this.
This is one of the ways
that a Chief Data Officer
and the related organizations
can add the most value
reduction in that cycle time.
That's really where the
business value comes from.
So I wonder if we could
talk about that a little bit
and how that, the constituents
in the stakeholders
in that life cycle, across that
data pipeline have changed.
>> That's a very good question.
Very insightful question, Dave.
So, if you look at a company like IBM
and my role internally within
IBM is to enable IBM itself
to become an AI enterprise.
So infuse AI into our
major business processes,
things like our supply chain
or lead to cash process,
our finance process is
like accounts receivable
and procurement and so forth.
I mean every major process
that you can think of
is using Watson now.
So that's the vision.
That's essentially what we've implemented.
And that's how we are using
that now as a showcase
for our clients and customers.
One of the things that we realized
is the data and AI enablement,
parts of the business.
The work that I do also has processes
and that's the pipeline you referred to
we're setting up the data pipeline.
We were setting up the
machine learning pipeline,
the deep learning pipeline,
we're always setting up these pipelines.
And so now you have the opportunity
to actually turn the so-called
AI ladder on its head
because the AI ladder has to do with,
Hey, first you collect the data.
Then you curate it,
you make sure that it's high quality,
et cetera, et cetera, fit for AI.
And then eventually you get to applying AI
and then infusing it into
business processes and so forth.
But once you recognize
that the very first,
the earliest pieces of work with the data,
those themselves are
essentially processes.
You can infuse AI into those processes
and that's, what's made
the cycle time reduction
and all the things that
I'm talking about possible,
because it just makes it much, much easier
for somebody to then implement AI
within a large enterprise.
I mean, AI requires specialized knowledge.
There are pieces of AI,
like deep learning, whether,
typically a company is
going to have like a handful
of people who even understand
what that is, how to apply it,
how models drift when
they need to be refreshed,
et cetera, et cetera.
And so that's difficult.
You can't possibly expect
every business process,
every business area to
have that expertise.
And so you've then got to
rely on some core group,
which is going to enable them to do so,
but that group can't do it manually
because I again otherwise
that doesn't scale again.
So then you come down to these pipelines
and you've got to actually infuse AI
into these data and AI
enablement processes
so that it becomes much, much easier
to scale across an enterprise.
>> Some of the CDOs maybe they don't have
the reporting structure that you do
or maybe it's more of a
far flung organization.
Not that IBM's not far
flung, but they may not have
the ability to sort of inject AI.
Maybe they can advocate for it.
Do you see that as a
challenge for some CDOs
and how do they sort of get through that?
What's, the way in which they
should be working with their
constituents across the organization
to successfully infuse AI?
>> Yeah, that's it's in fact,
again, a very good point.
I mean, when I joined IBM,
one of the first observations I made
and I fact made it to
our senior leadership
is that I didn't think that
from a business standpoint,
people really understood what AI meant.
So when we talked about
a cognitive enterprise
or an AI enterprise as IBM,
our clients didn't really
understand what that meant,
which is why it became really
important to enable IBM itself
to be an AI enterprise.
That that's my data strategy.
You kind of alluded to the
fact that I have this approach,
whether these five steps, well,
the very first step is to
come up with a data strategy
that enables a business
strategy that the company is on.
And in my case, it was,
Hey, I'm going to enable the company
because it wants to become a
cloud and cognitive company.
I'm going to enable that.
And so we essentially,
our data strategy became one of
making IBM itself an AI enterprise.
But the reason for doing that,
the reason why that was so important
was because then we could
use it as a showcase
for clients and customers.
And so when I'm talking with
our clients and customers,
that's my role.
I'm really, the only role that
I'm playing is what I call
an experiential selling one.
One where I'm saying,
forget about the fact that we are selling
this particular product
or that particular product
if you've got GPU servers,
we've got Watson open scale or whatever.
It doesn't really matter.
Why don't you come and see
what we've done internally
at scale.
And then we also lay out for you
all the different pain points
that we had to work
through using our products
so that you can kind of make the same case
when you apply it internally
and same comment with
regard to the benefits
the cycle time reduction,
some of the cycle time
reductions that we've seen
in my processes itself,
like this thing about metadata
business metadata generating
that is so difficult.
And it's, again,
something that's critical.
If you want to scale your data,
because you can't really
have a good catalog of data
if you don't have good business metadata.
So anybody looking at
what's in your catalog
won't understand what it is.
They won't be able to use it, et cetera.
And so we've essentially automated
business, metadata generation using AI.
And the cycle time
reduction there is like 95%.
I would actually argue it's more than that
because in the past,
most people would not,
for many, many datasets the
pragmatic approach would be,
don't even bother with
the business metadata.
Then it becomes just put somewhere in your
data architecture,
somewhere in your data lake
or whatever you have data warehouse.
And then it becomes a data swamp
because nobody understands it.
Now with regard to our
experience, applying AI,
infusing it across all our
major business processes.
Our average cycle time reduction is 70%.
So just tremendous amount
of gains are there.
But to your point,
unless you're able to point
to some application at scale
within the enterprise
that's meaningful for the enterprise,
which is kind of
the role I play in terms
of bringing it forward
to our clients and customers.
It's harder to argue or make
a case for investment into AI,
within enterprise without
actually being able to point to
those types of use cases
that have been scaled
and where you can demonstrate the value.
So that's extremely important
part of the equation
to make sure that that
happens on a regular basis
with our clients and customers.
I will say that your point is valid.
A lot of our clients and
customers come back and say,
"Tell me when they're
having a conversation."
I was having a conversation just last week
with a major, major financial
services organization.
And I got the same point
saying if you're coming out of regulation,
how do I convince my leadership
about the value of AI?
And I basically responded,
yes we've got the scale use cases.
You can show that,
but perhaps the biggest
point that you can make
as a CBO back to the senior leadership
is can we afford to be left out?
That is the, I think the biggest
point that the leadership
has to appreciate.
Can you afford to be left out?
>> I want to come back to
this notion of 70% on average,
the cycle time reduction.
I mean, that's astounding,
and I want to make sure
people sort of understand
the potential impacts.
And I would suspect that many CDOs,
if not most understand
sort of system thinking,
it's obviously something
that you're big on,
but oftentimes within organizations,
you might see them trying to
optimize one little portion
of the data life cycle and having, okay,
Hey, celebrate that success.
But unless you can take that systems view
and reduce that overall cycle time,
that's really where the business value is.
And I guess my real
question around this is
every organization has
some kind of north star,
many are about profit and
you can increase revenue
or cut costs, and you
can do that with data.
It might be saving lives,
but ultimately to drive this data culture,
you've got to get people
thinking about getting insights
that help you with that North star,
that mission of the company,
but then taking a systems view.
And that's 70% cycle
time reduction is just
the enormous business
value that that drives,
I think sometimes gets lost on people.
And these are telephone
numbers in the business case,
aren't they?
>> Yes, absolutely.
It's there's just a
tremendous amount of potential
and it's not an easy
thing to do by any means.
So, and we'd been always already
transparent about the
data as you know, I mean,
we put forward this,
this blueprint, right.
The cognitive enterprise
blueprint, how you get to it.
And I kind of have
these four major pillars
for the blueprint.
There's obviously there's data
and you're getting the data
ready for the transformation
that you want to do, but also things like,
training data sets,
how do you kind of run hundreds
of thousands of experiments
on a regular basis,
which kind of then leads
you into the other pillar,
which is technology.
But then the last two pillars
are business process change
and the culture, organizational culture.
Managing organizational
considerations of culture.
'Cause if you don't keep
all four in lock step,
the transformation is
usually not successful
at an end to end level.
Then it becomes much more
what you pointed out,
which is you have kind of
point solutions and the role,
the CDO role doesn't make
the kind of strategic impact
that otherwise it could do.
So, and this also comes back
to some of the earlier points,
your need to do
If you think about how do
you keep those four pillars
in lock sync, it means you've
got to have the data leader
You've also got to have
the technology leader.
And in some cases they
might be the same people,
but just for the moments sake of argument,
let's say they're all different people.
And many times they are.
So data leader, technology
leader, the operations leaders,
because they're the ones who
own the business processes,
as well as the organizational leaders.
They've got to all work together
to make it an effective transformation.
And so the organization
structure that you talked about
that in some cases, my
peers may not have that,
that is true.
If the senior leadership is not thinking
overall digital transformation,
it's going to be
difficult for them to then
go down that path.
>> You've also seen that
culturally historically,
when it comes to data and
analytics, a lot of times
the lines of business,
their first response is to
attack the quality of the data.
'Cause the data may not
support their agenda.
So there's this idea of a data culture.
And then I want to ask you
how self-serve fits into that.
I mean, to the degree that
the business feels as though
they actually have some kind
of ownership in the data,
and it's largely their
responsibility as opposed to
a lot of the finger pointing
that has historically gone on
whether it's been decision support
or enterprise data warehousing,
or even, data lakes,
they've sort of failed to live up to that,
that promise particularly
from a cultural standpoint.
And so I wonder, how have
you guys done in that regard?
How, how did you get there
and any other observations
you could make in that regard?
>> Yeah, so I think
culture is probably the
hardest nut to crack out
of those four pillars
that I had taught about.
And you've got to address that.
Not just top down, but also bottom up
as well as, peer to peer.
And I'll give you
some examples based on
our experience at IBM.
So the way my organization
is set up is there is a,
obviously a technology arm
and they are the people
who are doing the data
engineering work,
kind of laying out the
foundational technical elements
or the transformation the AI enablement,
the deep learning networks and so forth.
So there are those people.
And then there is another senior leader
who reports directly to me
and his organization
is all around adoption.
So he's responsible for
essentially taking what's available
in the technology and then
working with the business areas
to move forward and make
this make and infuse AI
into the processes that
the business is working.
It's done in a bottom up way.
It's deliberately set up, I
designed it to be bottom up.
So what I mean by that
is the team on my side
is fully empowered to move forward,
provided they find a like
minded team on the other side,
and go ahead and do it.
They don't have to come back for funding.
They don't have to,
they can just go ahead and do it.
They're basically empowered to do that.
And that particular setup
enabled us in a couple of years
to have a hundred thousand
internal users on our central
data and AI enabled platform
and what I mean hundred thousand users,
I mean, users who are using
it on a monthly basis,
we compound.
So if you haven't used it in
a month, we won't count you.
So it's over a hundred thousand
even very rapidly to that.
That's kind of an enterprise wide story.
That's kind of the bottom up direction.
The top down direction was
the strategic element that I
talked with you about where I said,
Hey, our data strategy is going
to create, make IBM itself
into an AI enterprise.
And then use that as a showcase
for clients and customers
that kind of we reiterate that,
I worked the senior leadership
on that view all the time,
talking to customers, et
cetera, and our senior leaders.
And so that's kind of
the air cover to do this.
That mix gives you that possibility.
I think from a peer to peer standpoint,
were you get to these large
scale end to end processes.
And there are a couple of
ways I worked that one way is
we've kind of looked
at our enterprise data
and said, okay, there are
four major pillars of data
that we want to go after.
There's the data about our clients,
data about our offerings
or data about, financial data that we
and then our workforce
data and than within that
there obviously sub pillars
there's like some sales
data that comes in and some,
other than workforce,
you can have contractors
versus employees, et cetera.
But think for the moment about
these four major pillars of
data.
And so let me map that to end to end
large business processes
within the company.
The really large ones like
enterprise performance management
end to end or lead to
cash generation end to end
risk insights across our full supply chain
end to end things like that.
And we've kind of tied
these major data pillars
to those major end to end processes.
There's a mechanism there
obviously in terms of
facilitating and to some extent,
one might argue even
forcing some interaction
between teams that
otherwise might not talk,
but it also brings me and my
peers much closer together
when you set it up that way.
And that means people from the HR side,
people from the operations
side, the data side,
the technology side,
all coming together to
really move things forward.
So all three tracks
being hit very, very hard
to move the culture forward.
>> Am I also correct that you
have a Chief Data Officers
that report to you,
whether it's a matrix or
direct within the divisions?
Is that right?
>> Yes.
So IBM, in terms of our structure,
as you know, we are a global company.
We are also, far-flung company
and we have many different products
and business units and so forth.
And so one of the things
that I realized early on
was we are going to need data officers
in each of those business units.
And the business units
there is obviously the
enterprise objective
and you could take on
the enterprise objectives
in terms of some examples based
on what I said in the past,
which is so an enterprise
objective would be, yeah,
we've got to have our data
foundation by essentially making
data along these four pillars.
I talked about clients
offerings, et cetera,
very accessible self service.
You had mentioned self-serve actually,
this is where the self
service piece comes in right.
So you can get at that data quickly
and appropriately, right.
I mean you want to be,
make sure that the access
control and all that stuff
is designed out
and you are able to do
change your policies
and it's not manual,
but those things got implemented
very rapidly and quickly.
And so you've got that piece
of the puzzle to go after.
And then I think the other aspect
of this as though when you recognize
that every business unit
also has its own objectives
and they are looking
at some of those things
somewhat differently.
So I'll give you an example.
I mean, we've got data
on AI product units.
Now those CDOs right
their concern is going to be a lot more
around the products themselves
and how we are monetizing those products.
And so they're not per se concerned with,
how you reduce the end to end cycle time
of IBM's internal supply chain.
So this is my point.
But they're going to have
substantial considerations
and objectives that
they want to accomplish.
And so I recognize that early on,
and we came up with this notion
of a data officer council
and I helped staff the council.
So this is why that's
the matrix reporting that we talked about,
that I selected some of the key players
that we have in those units.
And I also made sure they
were funded by the unit.
So they report into the units
because their paycheck is
actually determined by the unit
and which makes them then
aligned with the objectives
of their unit,
but also obviously part
of my central approach
so that I can disseminate
it out to the organization.
It comes in very, very handy
when you are trying to do things
across the company as well.
So when we GDPR,
when we have to get the
company ready for GDPR,
I would say that this mechanism
became a key, key aspect
of what enabled us to move
forward and do it rapidly,
from within the organization.
>> Because you had the structure
that perhaps the lines of business
weren't maybe as concerned about GDPR,
but you had to be
concerned with it overall.
And this allowed you to sort
of heighten their importance.
>> Right because think
of, in the case of GDPR,
they have to be a company wide
policy and implementation.
>> Dave: Right.
>> And if you did not have that
structure already in place,
it would have made it that much harder
to get that uniformity and
consistency across the company.
>> Dave: Right.
>> So you would have to
invent that's structured,
but we already had it because we said,
"Hey, this is (indistinct) data.
"We are going to have these
"types of considerations that play out."
And so we have this regular,
this network that meets
regularly every month actually.
And when things like
GDPR hit then much more
frequently than that.
>> Right so that, that makes sense.
So we're out of time, but I
wonder if we could just close,
if you could address the,
the MITCDO audience that probably
this is the largest
audience, believe it or not,
now that it's virtual, it
definitely expanded the audience,
but it's still a very elite group.
And the reason why I was so pleased
that you agreed to do this is
because you've got one of the more complex
organizations out there
and you've succeeded
and a lot of the hard work.
So what, what message would you leave
The MITCDO audience in Inderpal?
>> So I would say that,
this particular professional,
the CDO profession
is if I had to pick one trait,
let me pick two traits.
I think one is, you're a change agent.
So you have to be really
comfortable with change.
Things are going to change.
The organization is going to look to you
to make those changes.
And so that's one aspect of your job that,
may or may not be thought out immediately,
but those particular set of skills
and characteristics are
something that one has to develop
over time.
And I think the other thing I
would say is it's a continuous
learning job.
So you're continuously learning
and things keep changing around
you and changing rapidly.
And if you just even think
just in terms of the subject areas,
I mean the (indistinct) of today,
you've got to understand the technology.
Obviously you've got to understand data,
you've got to understand
AI and data science.
You've got to understand cybersecurity.
You've got to understand
the regulatory framework,
and you've got to keep all that in mind
and you've got to distill
it down to certain trends
that's happening right?
I mean, so this is an example of that as
there's a trend towards more
regulation around privacy
and also in terms of individual
ownership on payment right.
Which is very different
from what's before,
but that's kind of where
the pocket is going.
And so you've got to be on
top of all those things.
And so the characteristic of
being a continual learner,
I think is a key aspect of this job.
One other thing I would add,
and this is post COVID-19,
pre COVID-19 in terms
of those four pillars
that we talked about,
which had to do with the data
technology, business process
and organization and culture
from a CDO perspective,
the data and technology would
obviously be front and center.
I would say post COVID-19
most of the civil unrest
and so forth.
The other two aspects
are going to be critical
as we move forward.
And so the people aspect
of the job has never been
more important than it is today.
>> Dave: Right.
>> That's something that
I find myself not regularly doing
just talking at all levels of
the organization one-on-one,
which is something that we
never really did before,
but now we find time to do it.
So obviously it's doable.
I also think it's just,
it's a change that's here
to stay and it should stay.
>> Well to your point about change.
If you were in your
comfort zone before 2020
this year has certainly
taken you out of it.
Inderpal Bhandari thanks so much for
coming back in the Cube and
addressing the MITCDO audience.
I really appreciate it.
>> Thank you for having
me Dave, my pleasure.
>> You're very welcome and thank
you for watching everybody.
This is Dave Vallante we'll be right back
right after this short
break watching the cube.
(upbeat music)
