Why data culture
matters PART II
Tak Nagumo, MURC:
For MUFG, data culture
is a part of our value system. Like eating rice or bread—if you don’t eat it,
you miss the day. Ultimately, everyone in the organization has to adopt a
mind-set of data culture, but it doesn’t happen overnight. Creating a
cross-cutting data set across the organization is a key for success.
Cameron Davies, NBCU:
Just getting the people
the data gets them excited. I’ve never met anybody in all my time at NBCU, or in
my past 20 years at another very highly creative company, where I had someone
look at me and say, “No, please don’t give me any information to help me make a
better product.” At the same time, I don’t believe in the Field of
Dreamsphilosophy that seems to be inculcated through a lot of data
analysis, which is, if you just build it, build something cool, it’ll come.
I’ve never seen that work.
Ted Colbert, CIO, Boeing:
You have to figure out
how to really democratize the data-analytics capability, which means you have
to have a platform through which people can easily access data. That helps
people to believe in it and to deliver solutions that don’t require an expensive
data scientist. When people begin to believe in the data, it’s a game changer:
They begin to change their behaviors, based on a new understanding of all the
richness trapped beneath the surface of our systems and processes.
Ibrahim Gokcen, Maersk:
Data has to flow across
the organization seamlessly. Now that our data is democratized, thousands of
people can access it for their daily work. We see a lot of energy. We see a lot
of oxygen in the organization, a lot of excitement about what is possible and the
innovation that’s possible. Because data, applied to a business problem,
creates innovation. And our people now have the ability to act on their
innovative ideas and create value.
Ted Colbert, Boeing:
For Boeing, safety
always comes first. There’s no “sort of” in it. Always comes first. The
certification requirements for software embedded on our products are
tremendous, for example. Data about how people use a system can help us
understand exactly what they’re doing, so that productivity and safety go hand
in hand.
Cameron Davies, NBCU:
There are things we
demand about our data and how we treat and consume it. For example, we take PII1 very seriously. It’s a
written rule: “This is what you can and can’t do.” We have policies that are
allowed and things that are not allowed. And going against those policies will
probably end up in you losing your job. There are expectations that if I do get
the data, I treat it safely and effectively. If I transform it or I move it,
it’s in a place where most people can get to it with the controls in place.
There also is the risk
of getting [analytics] wrong. Solutions now are starting to help us understand
what’s happening inside the box. And it’s important to understand that as you
build up these capabilities, there is a support cost you’re going to have to
take on. You should have people monitoring to make sure it makes sense. You
should build alerts into place. Sometimes the data goes south, which I’ve seen
happen, and nobody realizes it. I won’t throw anybody under the bus, but we had
a vendor that couldn’t recognize an ampersand. But that’s how somebody decided
to title one of our shows. We think that issue cost us tens of millions in
potential revenue—an ampersand!
We used to think we
could build these systems and hand them to people, and they’d be sophisticated
enough to run them. We found very quickly that wasn’t always the case. We ended
up actually staffing to help run it or assist them with it.
Tak Nagumo, MURC:
It’s almost like a yin
and yang, or a dark side and a sunny side. Introduction of the data-management
policy documents, procedures, data catalog, data dictionary—the fundamental
setting is common for the [financial] industry. And the mind-set necessitated
to this area is more of “rule orientation.” The other side, the sunny side, I
would say, is more Silicon Valley–oriented, more of the data usage, data
science, data analytics, innovation, growth. Housing those two ideas into one
location is so important.
If you don’t have a
solid foundation, you can’t use the data. If you have a solid foundation but
are not using the data creatively, you’re not growing. This mixing of those two
is a key challenge for our entire industry. You have to combine both, that’s
the bottom line.
Ibrahim Gokcen, Maersk:
Every company has
constraints. Even the Silicon Valley companies have a lot of constraints.
Clearly, we are regulated. We have to comply with lots of rules and regulations
across the globe. We are a global company. But failing fast and cheap doesn’t
mean making bad decisions. It means complying within the constraints that you
have, and learning how do you go faster or how do you test things faster. And
then implementing the decisions properly. So I think it’s really all about the
culture of using data, experimenting, building stuff, doing all that as fast as
you can—and delivering that to the front line, of course with the right
mechanisms.
Cameron Davies, NBCU:
You can talk about a
CEO-mandated thing. It only goes so far. People work, breathe their business
every day. Nobody knows it as well as they do.
We had a business unit
that needed to produce forecasts on an annual basis. There are a lot of players
in that process. We went into the organization and found one of the key
researchers, who seemed the most open, and we said, “Hey, what do you think?
Let’s bring you in and you work with us.” He became our point person. He
interfaced with all his peers throughout this process. Anything we needed to
do, this person was the interpreter.
Then we built a set of
algorithms, largely machine-learning-driven, with a lot of different features
that proved to be fairly accurate. We surfaced them into a tool. And this
evangelist on the team was the first to adopt it. He then went out and trained
other people how to use it. He brought feedback to us, and through that process
took on ownership. Now it’s, “This is my project. I’m responsible for making
sure this happens.” Nice for us! I don’t have to have a product manager now
that’s meeting with seven different people every month. They’ve fully taken it
on and adopted the process.
Tak Nagumo, MURC:
A key role for us is
middle management. They’re a kind of knowledge crew, conceptualizing and really
justifying ideas from upper management, and also leading implementation
throughout the entire organization. So that’s up, middle, and down. We’ve also
found that “expats” are really well-suited to blend different elements, particularly
as we become more globalized. Understand that we have people who work in, among
other places, Tokyo, London, New York, or Singapore. No one can communicate
better in Tokyo, for example, the needs of employees in the United States than
someone who has actually lived and worked in the United States.
Jeff Luhnow, Houston Astros:
We decided that in the
minor leagues, we would hire an extra coach at each level. The requirements for
that coach were that he had to be able to hit a fungo, throw batting practice,
and program in SQL. It’s a hard universe to find
where those intersect, but we were able to find enough of them.
What ended up happening
was, we had people, at each level, who were in uniform, who the players began
to trust, who could sit with them at the computer after the game, or before the
game, and show them the break charts of their pitches or their swing mechanics
and really explain to them, in a lot more detail, why we’re asking you to raise
your hand before you start swinging or why we’re asking you to change your
position on the rubber or how you deliver the ball. Once we got someone in
uniform to be part of the team, ride the buses with them, eat the meals with
them, and stay in the motels they have in Single A, it began to build
trust. They were real people there to help them.
That was great, and
that transition period worked for about two years, until the point where we
realized that we no longer needed that, because our hitting coaches and our
pitching coaches and our managers are now fully technology enabled. They can do
the translation. And they’re actually real baseball people who have had careers
in coaching and playing. The “translators” have essentially become the coaches
themselves.
CONTINUES IN PART III
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