Data tells us that a data culture matters.
The digital revolution presents every business with
unprecedented opportunity and risk. Cheap and abundant
online resources promise new products, new markets, and new
opportunities for richer customer relations. They also
threaten heated competition and perpetual disruption.
When we’re beset by change, it’s good to remember the
unchanging core principles: Know your market. Focus on your
customer. Perfect your offering, and be ready to adapt it to
changing conditions. Seek efficiency.
In other words, get your data together and use it well.
Build a workplace culture around it. That will look very
different depending on the people involved. If you provide
the same set of technologies and data to two different teams
with the objective to innovate, or solve a hard problem, you
can get two very different outcomes. Different teams will
need to align on their goals and their data to start
building a successful culture.
Culture is an accelerating agent in and of itself.
According to McKinsey and Company’s
Why data culture matters:
“Culture can be a compounding problem or a compounding
solution. When an organization’s data mission is detached
from business strategy and core operations, it should come
as no surprise that the results of analytics initiatives may
fail to meet expectations. But when excitement about data
analytics infuses the entire organization, it becomes a
source of energy and momentum. The technology, after all, is
amazing. Imagine how far it can go with a culture to match.”
And remember: Using data is nothing new. Since the dawn of
commerce, people have observed facts, figured out what
matters, and sought patterns to leverage.
Modern statistics dates to 1749,
and data-powered management has radically raised global GDP
for over a century with ever-increasing sophistication.
These are revolutionary, data-driven times we live in. We
came to them because we used data well.
How people organize their work changes with the amount and
quality of the data they have. Ancient farmers used the
informal data of watching weather patterns, while
industrialists patented standardized machine tools. At the
dawn of the computer era, we had applied mathematics and
operations research. Now we need a stronger method, one that
can become widespread throughout the enterprise.
What does that look like? Let’s start with the magnitude of
the opportunity. In 2002,
digital storage capacity overtook total analog capacity.
Since then, the compounded annual growth rate of data owned
a typical corporation has been about 60%.
Not only has the amount of data increased, it now comes from
a more diverse set of sources, including browsers, sensors,
smartphones and mobile devices, not to mention other
computers. The compound annual growth rate of change is
Google thinks about these opportunities a lot. We were
founded, after all, with a mission to organize all the
world’s information, and over the years we’ve solved a
number of fascinating problems around yielding insights and
action from large amounts of different kinds of data—now
done at blistering velocity.
We work to provide digital insights and the capability to
take action to both consumers and enterprises, both in our
advertising work with businesses and now, through the tools
and services for data management and insights we offer at
Google Cloud. We hear how our products are helping
accelerate digital transformation and innovation at
companies around the world, including
and more. Take AirAsia as an example, who’re en route to
becoming a “digital airline.”
Their transformation is already helping them extract new
insights, become more agile, and deliver more personalized
experiences so they can stand out in their industry. “We had
to become a digital airline to offer customers more,
personalize customer experiences, and improve booking and
ticketing,” says Nikunj Shanti, chief product officer at
AirAsia. “We’ve gone from data management to data-driven
We’ve also learned a number of lessons on internal
organization to optimize on data, as well, both in our own
journey and from helping our customers solve hard problems.
Some of those lessons inform this ebook on why a data
culture matters. It comes down to four key pillars:
operating with trust, democratizing insights, increasing
business agility, and applying intelligence.
There are several striking things about organizing for data
operations at scale. Advances in the technologies
surrounding data means there’s more access and easier
manageability. Managing and working with data at scale is
hard, and poses a new challenge when compared to working
with data in the past; in many cases, this is balanced by
today’s better process automation and tools to make sense of
the data. Of course, more access means new challenges in
security, in quality, and interpretability.
Great businesses are effective because they have great
processes that make great products, reflecting great
understanding and care for their customers. In other words,
all great businesses have great internal cultures that
produce these results. People adapt with curiosity and
creativity. When appropriate, they challenge the status quo
and innovate based on new insights. They leverage the power
of data they are entrusted with, adapting and applying
processes to generate new value from data.
That’s never been more true than today, when the titanic
digital shifts bring into new and sharper focus the need to
get culture right around collecting and using data at scale.
Getting it right early on is important, because history
shows us something else: Those working toward new goals
never turn down having more data, as long as it’s useful.
Advances in cloud computing, data management, data
analytics, and artificial intelligence technologies aren’t
slowing down. Neither should any business, in its hunger to
change the world.
When businesses are looking to reinvent their data culture,
or create a new one, we often hear that this handful of
Google Cloud’s products is useful.
to easily migrate on-premises workloads into fully managed
cloud-native database services for massive scalability
for petabyte-scale, super-fast data warehousing
for modern BI, embedded analytics and data-driven
for big data processing using Hadoop and Spark clusters
to process both batch and streaming data quickly and
to send messages to and from independent apps
for fast and visual preparation of data for analysis or
to train custom ML models without much expertise
so you can take ML models into deployment on-premises or
on Google Cloud