Two disciplines, becoming a “data-driven organization” and enabling the
“future of work” are related objectives, and excelling at one can accelerate
We seek data-driven organizations where leaders use data and rely less on
intuition when making decisions. We want to help all employees access data,
analytics, and the tools to ask questions, discover data sources, and expose
potential answers with data visualizations.
All organizations must find ways to use data, analytics, and machine
learning to strategic and competitive advantages. One report shows that an
organization-wide strategic focus on real-time
data increases the likelihood of a “transformative impact” on revenue growth
by 2.3 times.
The future of work means many things to different people. Improving the
culture. Excelling at diversity, equity, and inclusion. Driving
sustainability. Automating more repetitive tasks and accelerating innovation
practices. Supporting hybrid working, collaboration, and globally dispersed
teams. McKinsey shared
56 foundational skills
in four categories: Cognitive, interpersonal, self-leadership, and digital,
people in data-driven organizations are data literate and comfortable
working with AI.
Enable the future of work with these five data-driven practices
In my new book,
Digital Trailblazer, I share several of my stories about leading data-driven organizations,
from marketing departments adopting citizen data science to nonprofit
organizations investing in dataops.
So how does enabling a data-driven organization prepare them for the future
of work? Here are three ways
Automate dataops, but encourage data prep
Many organizations still have departments and outsourced teams handling data
operations with tasks to load data sets in manually. They run scripts, check
for errors, and make manual data quality fixes.
Other organizations have automated some
dataops, but the process and skills are siloed to a department, selected data
sets, or embedded in one application.
And while many orgs have data prep tools, they are more often used by a
select few analysts when exploring new data sets. There’s often a disconnect
in operationalizing their work and taking their prep into a production
Creating standards and centralizing some dataops activities is
a key responsibility of chief data officers, yet getting the organization onboard with their charters isn’t easy.
Attack this challenge because it’s hard to be data-driven and seek data
literacy when the data pipelines are a tangled mess of technical and
Centralize data catalogs, data dictionaries, and ML training data
When a waterfall fills a pristine lake three miles into the forest, is there
a well-marked trail for people to find it? Once I’m at the lake, are there
clearly identified steps to easily find the waterfall and climb it to its
You have lots of data sources, and maybe you’ve centralized them in a data
lake, but that doesn’t mean employees know how to find usable data sets,
have access to them, or understand the policies on how they can and cannot
use them. That’s the
role of data catalogs
and why they are key platforms to enable transformation.
But data-driven organizations do more than cataloging their data sources.
Survey your CRM and count how many date, currency, category, and attribute
fields people must understand before using them in their analysis.
Proactive data governance practices, including creating data dictionaries and profiling data sets, help people
understand data’s meaning before they apply it to their analysis and
Lastly, organizations with many data scientists working independently in
different departments recognize the cost and complexity of creating ML
training data. They’ll take steps to centralize this data and make it easier
for more people to link and load it into their own machine learning and
Ban presenting data in PowerPoint, Spreadsheets, and Tools Disconnected from
In my first book,
Driving Digital, I share one of
my data center of excellence frameworks
for getting executives on board with data-driven practices. It requires taking
the PowerPoints and spreadsheets away and presenting real-time data directly
from workflow tools (CRMs, ERPs) or analytics solutions (Tableau, PowerBI) at
meetings. I tell the stories behind the framework in
The “past of work” had only a few people that could wrangle their way to
meaningful data analysis, presentation, and storytelling. Today, these
responsibilities must be democratized, but a
Digital Trailblazer’s efforts
will fall short if the executives in the room still want their insights
presented as 5-course meals with creative plating that masks what goes on
behind the scenes to prep and cleanse ingredients.
Several additional practices of evolving data-driven cultures help enable
the future of work, and I’ll share two more ways in an upcoming episode of
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