Why data culture matters PART I
Organizational
culture can accelerate the application of analytics, amplify its power, and
steer companies away from risky outcomes. Here are seven principles that
underpin a healthy data culture.
Revolutions, it’s been
remarked, never go backward. Nor do they advance at a constant rate. Consider
the immense transformation unleashed by data analytics. By now, it’s clear the
data revolution is changing businesses and industries in profound and unalterable
ways.
But the changes are
neither uniform nor linear, and companies’ data-analytics efforts are all over
the map. McKinsey research suggests
that the gap between leaders and laggards in adopting analytics, within and
among industry sectors, is growing. We’re seeing the same thing on the ground.
Some companies are doing amazing things; some are still struggling with the
basics; and some are feeling downright overwhelmed, with executives and members
of the rank and file questioning the return on data initiatives.
For leading and lagging
companies alike, the emergence of data analytics as an omnipresent reality of
modern organizational life means that a healthy data culture is becoming
increasingly important. With that in mind, we’ve spent the past few months
talking with analytics leaders at companies from a wide range of industries and
geographies, drilling down on the organizing principles, motivations, and
approaches that undergird their data efforts. We’re struck by themes that recur
over and again, including the benefits of data, and the risks; the skepticism
from employees before they buy in, and the excitement once they do; the need
for flexibility, and the insistence on common frameworks and tools. And,
especially: the competitive advantage unleashed by a culture that brings data
talent, tools, and decision making together.
The experience of these
leaders, and our own, suggests that you can’t import data culture and you can’t
impose it. Most of all, you can’t segregate it. You develop a data culture by
moving beyond specialists and skunkworks, with the goal of achieving deep
business engagement, creating employee pull, and cultivating a sense of
purpose, so that data can support your operations instead of the other way
around.
In this article, we
present seven of the most prominent takeaways from conversations we’ve had with
these and other executives who are at the data-culture fore. None of these
leaders thinks they’ve got data culture “solved,” nor do they think that
there’s a finish line. But they do convey a palpable sense of momentum. When
you make progress on data culture, they tell us, you’ll strengthen the nuts and
bolts of your analytics enterprise.
That will not only
advance your data revolution even further but can also help you avoid the
pitfalls that often trip up analytics efforts. We’ve described these at length
in another article and have
included, with three of the seven takeaways here, short sidebars on related
“red flags” whose presence suggests you may be in trouble—along with rapid
responses that can mitigate these issues. Taken together, we hope the ideas
presented here will inspire you to build a culture that clarifies the purpose,
enhances the effectiveness, and increases the speed of your analytics efforts.
Rob Casper, chief data officer,
JPMorgan Chase:
The best advice I have
for senior leaders trying to develop and implement a data culture is to stay
very true to the business problem: What is it and how can you solve it? If you simply
rely on having huge quantities of data in a data lake, you’re kidding yourself.
Volume is not a viable data strategy. The most important objective is to find
those business problems and then dedicate your data-management efforts toward
them. Solving business problems must be a part of your data strategy.
Ibrahim Gokcen, chief digital officer,
A.P. Moller – Maersk:
The inclination,
sometimes, when people have lots of data is to say, “OK, I have lots of data
and this must mean something, right? What can I extract from data? What kind of
insights? What does it mean?” But I’m personally completely against that
mind-set. There is no shortage of data, and there is even more data coming in.
Focus on the outcomes
and the business objectives. Say, “OK, for this outcome, first let’s look at
the landscape of data and what kind of analytics and what kind of insights I
need.” Then act on it rapidly and deliver that back to the team or the
customer. This is the digital feedback loop: use the insights, ideas, and innovation
generated by the team or your customer as an accelerator for improving the
capability and product and service that you already have.
Cameron Davies, head of corporate decision
sciences, NBCUniversal (NBCU):
It’s not about the data
itself. It’s not just about the analytics—any more than taking a vitamin is
only so you can claim you successfully took a pill every morning. When it comes
to analytics, we have to keep in mind the end goal is to help make better
decisions more often. What we try to do first and foremost is look at places
where people are already making decisions. We review the processes they use and
try to identify either the gaps in the available data or the amount of time and
effort it takes to procure data necessary to make an evaluation, insight, or
decision. Sometimes we simply start by attempting to remove the friction from
the existing process.
Jeff Luhnow, general manager, Houston
Astros:
We were able to start
with a fresh piece of paper and say, “OK, given what we think is going to
happen in the industry for the next five years, how would we set up a
department?” That’s where we started: “OK, are we going to call it analytics or
are we going to call it something else?” We decided to name it “decision
sciences.” Because really what it was about for us is: How we are going to
capture the information and develop models that are going to help the decision
makers, whether it’s the general manager, the farm director who runs the
minor-league system, or the scouting director who makes the draft decisions on
draft day. How are we going to provide them with the information that they need
to do a better job?
Cameron Davies, NBCU:
You can talk about
being CEO-mandated. It only goes so far. Our CEO [Steve Burke] is very engaged.
He’s willing to listen and share feedback. We try to be thoughtful of his time
and not waste it. A CEO, especially for a company of size, is thinking about
billion-dollar decisions. He’s thinking big, as you would expect. So we try to
focus on the larger things. We have a mantra: even if you have nothing to
communicate, communicate that. We have a cadence with Steve that happens on a
quarterly basis, where we say, “Here’s what we’re doing. Here’s what the
challenges are and here is how we’re spending the funding you gave us. Most
importantly, here is the value we’re seeing. Here is our adoption.”
Our CEO also provides
encouragement to the team when he sees it. For a data scientist—if you’re an
analyst or a manager—to get the opportunity to go sit with the CEO of a company
and then have him look at you and say, “That’s really cool. That’s awesome.
Well done,” that goes further to retention than almost anything else you can
do. And he’s willing to go do that from a culture perspective, because he
understands the value of it as well.
Takehiko (“Tak”)
Nagumo, managing
executive officer, Mitsubishi UFJ Research and Consulting (MURC); formerly
executive officer and general manager, corporate data management, Mitsubishi
UFJ Financial Group (MUFG):
Just like any other
important matters, we need the board’s backing on data. Data’s existed for a
long time, of course, but at the same time, this is a relatively new area. So a
clear understanding among the board is the starting point of everything. We
provide our board educational sessions, our directors ask questions, and all
that further deepens their understanding. And it’s good news, too, that
directors are not necessarily internal. They bring external knowledge, which
lets us blend the external and the internal into a knowledge base that’s
MUFG-specific. Having those discussions with the board and hearing their
insights is an important exercise and, increasingly, a key part of our data
culture.
Rob Casper, JPMorgan Chase:
Senior management now
realizes that data is the lifeblood of organizations. And it’s not just
financial services. As more and more people digitize all that they do, it all
comes down to having transparency and access to that data in a way that’s going
to deliver value. Senior leaders need to promote transparency on every level.
Whether it’s the budget, what you’re spending your time on, or your project
inventory, transparency is paramount. As Louis Brandeis said, “Sunlight is the
best disinfectant.” If everybody sees what everybody else is doing, then the
great ideas tend to rise to the top and the bad ideas tend to fall away.
CONTINUES IN PART II
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