Mark Picone: So our job really is to be an enabler for the
company and to enable the company to be data driven. There's
a lot of things that make that very difficult. You can say
data is developed in, you know, it's created all over the
place. But how can you create a model such that the data is
stitched together, it's curated, it's governed, it's the
right data, it's correct. And then you can create mechanisms
to be able to either better communicate with your customers,
engage the customers, or better just understand your
business. And we've done that with this data driven
operating model. Data driven operating model has really
allowed us to look across all of the silos that we had four
or five years ago across our organization that was really as
a result of kind of going to the cloud, going to
subscription, and rationalize that together create a single
view of the customer. That single view of the customer
though, is based on very strong governance techniques, and
also a unified data architecture.
Combined with that we were able to take sort of what I call
an outside in approach of creating customer journeys, and
the customer journey and the customer journey steps, which
are for consumer based business when you think about the
Photoshop and InDesign, Illustrator: discover, try, buy,
use, and renew. And across every one of those steps, we
actually went to each and assigned owners and organizations
to own those different KPIs within that journey step, and
then to create calls to action. And that sort of outside in
approach allowed us to create data sets and analytical
experiences that we now use to run the entirety of our
digital media business. And it's been really
transformational from a number of different points. Number
one, when we actually run the business, when we close the
business every week, there's a group of over 100 people that
get together and they actually look at the single source of
truth analytical experience journey step by journey step.
And they understand what's happening in that market, did our
annual recurring revenue go up or down, what was traffic?
What were the ads, what were the promotions? How? And they
basically use that to turn the knobs on the business. And
then they expect to see results, different results from what
they're doing the following week. So they do that on a
week-in week-out basis. Further, the customer journey steps
have really ingrained a culture change across the business,
not only just to being data driven, but actually the
vocabulary. We actually have meetings now on buy, on
discover, on use. Engagement now is such a critical part of
our business. Because again, because of the subscription
model, and this focus, and this vocabulary has really
permeated throughout Adobe. If you think about it, when
there's five or six or seven data silos all crunching the
same data in any percent the same way. But the 20% that's
different causes really big variations. So the operational
efficiencies come number one reporting the same metrics up
to management in the same way. There is no question about
what that metric is. Number two, you don't have all this
different database operations going on. So absolutely cost
savings from there as well. But really the the true value is
how we run that business. So as an example, the way we do ad
spend is radically changed. The way we do targeting and
testing has radically changed, because we now have a purview
of every product, across every geography, across every route
to market. And when you make those decisions now, you
actually see how did that revenue, how did the annual
recurring revenue take place for those customers over time?
Same thing, when we do targeting, AB targeting, we now have
an understanding of not just what that switch was AB, what
was the result you were targeting, what you're looking for,
but actually what happens with those customers over
longitudinal way. And it's just been very revolutionary from
that point of view. Further, we've kind of taken this whole
data driven operating model and created a playbook. And this
didn't happen when we created it, it happened after we
actually did it and we said, "wow there's a lot of
reusability here," not just from a systems and a
capabilities perspective, like platforms governance, data
architecture, but really from the methodology. The
methodology really allows us to have a kind of this inside
out approach of understanding all of your data assets,
categorizing them, creating a database that has the lowest
level of granularity, get curated, ready for analytics,
number one, then number two, and use all these governance
aspects about who owns the data, what's the lineage, what's
the data quality characteristics, what's the actual
definition. Crossword definition is a harder problem than
one would think. And then you combine that with that top
down approach, which is customer journey steps, assigned
organizations and processes, and that yields actionable
results. And we're actually taking that model and rolling it
out to other parts of our business.
Even internal organizations and finance as an example,
procure to pay, or other areas where kind of the bottom up
top down, focusing on analytical results and KPIs, or even
personalization needs, and while we're inside out focusing
on the data that's created, and how it can be used in
multiple ways. The last part about how it's changed is
through the product management aspect of it. We treat the
data as a product. We are an internal team, our customers
are all largely internal, although the data we create
personalizes real time experiences within the products. So
we actually have and talk to what we develop at Adobe and
the data we create as having different product
characteristics. So our product is really about the actual
data set. So as an example, our consumer data set is a
product and the analytical experiences that go along with
it. And the reason we have to treat it that way is because
it goes across probably, you know, almost a dozen different
stakeholders. And when we get one request to do something
from one, it's really not that request, while interesting is
how do we build and expand the core. The other thing we've
done that's been pretty interesting is, you know, we service
every single department, every single organization across
Adobe with data. But we do that in some cases, what I call
full service, and full service is where we create the data,
and we create the last mile visualization experiences. We
actually have lot of customers where we have self serve. We
create the data, they create the visuals, they do the BI,
they do the other data wrangling. And we've introduced a
third step over the past year that we call code development.
And that's where we really open source the data. So now the
data is available for others to bring their engineers or
third parties, whatever the case may be, to come into our
environment, and actually build data assets and expand the
nucleus of what we call our unified data architecture. So
having a product approach to what we're building, allows us
to build once and serve many and create really a case set of
capabilities that is really very impactful. We spend, or are
spending less and less time creating data sets and
visualizations, and more time creating capabilities that
will actually enable the entirety of the organization.