10 Steps to Develop a Data Literate Workforce

10 Steps to Develop a Data
Literate Workforce
1

2
Data Governance Practice Leader
Dallas / Fort Worth
aschneider@sensecorp.com
Alissa Schneider
About the Presenter
 15+ years leading complex data
transformation projects for Fortune
500 and mid-size companies

3
• What is Data Literacy?
• Why is Data Literacy Important?
• Audience Survey
• 10 Steps to Develop and Foster a Data
Literate Workforce
• Q & A
AGENDA

4
Data-literate companies that are successfully
navigating this gap are already disrupting
industries, fundamentally changing expectations
and service levels.
What is Data Literacy?
Data literacy is the ability to read, work with, analyze, and argue
with data. Much like literacy as a general concept, data literacy
focuses on the competencies involved in working with data.

Learning to speak data? Here is what we learn at each step:
5
Reading Data
The organization has a limited amount of data
literacy established across the enterprise. It
may exist in certain departments but not
across the organization. The organization has
made limited investments in data literacy.
o Understanding Data Literacy Building Blocks
o Developing a Foundation of Data
o Questioning the Quality of Data
Writing Data
The organization is working to establish and
educate the organization on data literacy.
Education is a priority for every employee,
from data-focused departments to front-line
employees. The organization is actively
investing in data literacy and hiring
accordingly.
o Conducting Data Analysis
o Presenting with Data
o Telling the Story with Data
Speaking Data
The organization is data-literate and has
prioritized its data as a core asset. All
employees recognize the value of data. The
organization is investing to further the reach
of data literacy with leading-edge technologies
(e.g. AI/ML).
o Evaluating Data Ethics
o Making Decisions with Data
o Tracking Progress with Data
1 32

Data Literacy and COVID-19
6
Sources:
• https://www.theatlantic.com/health/archive/2020/05/cdc-and-states-are-misreporting-covid-19-test-data-pennsylvania-
georgia-texas/611935/
• https://www.nytimes.com/2020/05/22/us/politics/coronavirus-tests-cdc.html
• https://www.npr.org/sections/coronavirus-live-updates/2020/05/21/860480756/scientists-warn-cdc-testing-data-could-
create-misleading-picture-of-pandemic

Human Progress and Literacy
7
Throughout history, the literate people of their time have always led.
Literacy is the road to human progress and the means through which every man,
woman, and child can realize his or her full potential.

Key Findings
8
Enterprise-wide data literacy is low.
24% of business decision makers surveyed
are fully confident in their ability to read,
work with, analyze, and argue with data.
Future employees are underprepared for
data-driven workplaces.
21% of 16- to 24-year-olds are data-literate,
suggesting schools and universities are failing
to ensure students have the skills they need to
enter the working world.
Data is key for professional credibility.
94% of respondents using data in their
current roles agree data helps them do their
jobs better, and 82% believe greater data
literacy would give them more credibility in
the workplace.
Senior leaders do not display confidence.
32% of the C-suite is viewed as data-literate,
potentially holding senior leaders back from
encouraging their workforces to use data to
their advantage.
Organizations are losing competitive
advantage because data literacy drives
higher enterprise performance.
85% of data-literate people say they are
performing very well at work, compared
to 54% of the wider workforce.
There is an appetite to learn.
78% of business decision makers said they
would be willing to invest more time and
energy into improving their data skillsets.
Source: Lead with Data: How to Drive Data Literacy in the Enterprise, Qlik

Companies that use data to establish their business models
have 3 distinct advantages over established companies:
9
High-Quality Data Collection
Each process and interaction is evaluated
through a “can-I-track-this” lens followed by a
“how-can-I-scale-this” discussion. This
requires a strong foundation of underlying
data to be captured at every step.
Established companies often have legacy
processes with limited and disparate data
collection from which the data might be of
questionable quality.
Strong Data-literate Workforce
Knowing how to work with data is the
“minimum height to ride” at a data-literate
company. These companies hire strong data
practitioners who, through an increasing focus
on data, have been able to monetize their data
assets.
Established companies need employees who
can maintain their existing non-data based
processes, so they hire differently. Developing
data literacy is typically not a priority for them
and is reflected in their workforce.
Agility as a Mindset
The culture at these companies is focused on
their abilities to bring products to market
quickly, move critical decision making to front-
line managers, promote innovative thinking,
foster competitive drive, and rapidly evaluate
progress based on data.
Established companies work hard to adopt
these traits on small teams but struggle to
scale them throughout the organization,
where employees prefer to operate as-is.

The data practices that differentiated high performers from
others involved data leadership in the C-suite, broadly
accessible data, and a culture that tolerates failure.
1. Out of 10 practices that were presented as answer choices. For respondents at high-performing organizations, n = 170; for all other respondents, n = 405.
2. Respondents who said their organizations (a) have had an average annual organic growth rate of 10% or more over past 3 years and (b) have had an
average annual growth rate in earnings before interest and taxes of 10% or more over past 3 years.
Current data practices at respondents’ organizations1
% of respondents
At all other
organizations
At high-performing
organizations2
C-Suite team includes
at least one data leader
Data are broadly accessible
to front-line employees
whenever needed
Organizational culture
supports rapid testing and
iteration based on data and
tolerates fast failure
Hiring criteria for non-
management roles include
proficiency in data-related
topics
Hiring criteria for
management roles include
proficiency in data-related
topics
Catch Them if You Can: How Leaders in
Data and Analytics Have Pulled Ahead,
McKinsey & Company; Survey

Established companies often feel like
prisoners to what made them
successful – their static processes and
hierarchical controls.
Meanwhile, the new kids in class are
faster, smarter, and better prepared to
succeed in a data-driven environment.

{ }
Survey the Audience
Data Literacy In Your World

13
1. Have you ever heard anyone at your organization talk
about or discuss the need for Data Literacy?
orYES NO

14
2. In terms of maturity, on a 1-5 scale, how would you
rank your organization’s enterprise-wide data literacy?
CHAOTIC
AD-HOC & UNKNOWN
REACTIVE
UNPREDICTABLE & REACTIVE
DEFINED
PROACTIVE, RATHER THAN REACTIVE
INTEGRATED
MEASURED & CONTROLLED
OPTIMIZED
STABLE & FLEXIBLE

15
Assess Data Literacy Status
01
Develop a Data Literacy Strategy
02
Secure Executive Support
03
Identify Data Champions
04
Invest in a Data Literacy Foundation
05
Develop a Customized Data Literacy Curriculum
06
Use Data in Decision Making
07
Provide Access to Data
08
Hire Data Savvy Employees
09
Integrate Data into Daily Activities
10
The 10 Steps to Fostering a
Data-Literate Workforce

01Data Pride Data Platform Data Prowess
This refers to how much the organization
recognizes and values data. We can determine
this by asking the following questions:
o Does your organization inherently
value data?
o What is the organization’s sentiment
about data?
o Does your entire organization value
data as an asset?
o Is every process and interaction viewed
as a source of valuable data?
o Does the organization push for
automated data collection where possible?
o Do front-line workers recognize the
value of collecting high-quality data?
This refers to how much of the organization’s
data is easily accessible in a highly useable
format. We can determine this by asking the
following questions:
o Does your organization make its data
easily available in a high-quality format?
o Are business and technology working
together to open up the data?
o Does the organization host the data in an
easy-to-access manner?
o Does the organization invest in breaking
down data silos?
o Does the organization certify a set of the
overall data across the enterprise?
o Does the organization invest in data
standards?
o Does the organization offer a set of
enterprise tools to provide the data to
employees?
o Does the organization invest in
collaborative uses of data?
This refers to how much the organization
knows about working effectively with
data. We can determine this by asking the
following questions:
o Does your organization understand and
know how to work with data?
o Are the organization’s employees data
savvy?
o Does the organization know how to convert
business questions into data questions?
o Does the organization understand how to
interpret and analyze data?
o Does the organization understand how to
communicate with data?
o Is the organization using artificial
intelligence, machine learning, and data
science?

DataProwessDataPlatformDataPride
High PerformersMid PerformersLow Performers
A few departments value data, but by and large, the
organization operates with the inertia of its
established processes and organizational structure.
There is a limited effort across the organization to
capture or create high-quality, automated, and
standardized data. Collection of data feels
burdensome because there is limited to no direct
value attached to it. Front-line workers don’t quite
understand why existing processes require certain
data to be captured.
A number of departments value data in the
organization and are beginning to see the value of
data as an asset that can drive competitive
advantage. There are pockets of initiatives to
capture and create high-quality, automated, and
standardized data. Collection of high-quality data,
while still challenging, is desired. Front line workers
understand the importance of capturing operational
data in a timely manner but need improved
processes and systems.
Most departments across the organization see value
in collecting every aspect of data and are using data
competitively. The organization is actively investing
in capturing high-quality data and leveraging
automated processes. Collection of data is
widespread and actively managed and coordinated.
Front-line workers clearly understand both the value
and need for quality and timely data and have
efficient processes and systems that aid in the
capture of data.
Most of the organization data is locked up in
legacy or functional systems with access through
reporting technologies. Some form of an enterprise
data warehouse might exist but with only a limited
set of enterprise data collected and integrated.
Data standards are lax and limited to certain system
implementations. Reporting and analytical
technologies are not broadly available or deployed
across the organization.
Some of the organization data is still locked up in
legacy or functional systems but there is a
substantial effort underway to unlock this data. An
enterprise data warehouse exists and there is a
concerted effort involving business and IT to
provide certified data. Establishing data standards is
still a work in progress but gaining momentum.
Reporting and analytical technologies are broadly
available across the organization, but adoption is
still underway.
Most of the organization’s data has been
integrated and made available through internal
and external data platforms. The organization is
also actively investing in data lakes and other newer
data platform technologies. Certified data is widely
available. Business and IT organizations actively
manage data standards and data usage. Reporting
and analytical technologies have been deployed
and are in use across the organization.
Decisions are based on anecdotal assessments and
are highly instinct-driven. There is a limited effort
undertaken to evaluate underlying data to assist
with a decision. Data used to make decisions is
often communicated by simply displaying the data.
Not much is done to weave the data into a story.
The organization has made limited investments in
advanced analytics capabilities.
Decisions are starting to be based on underlying
data, and decision-makers ask questions about the
data and its source and validity. The data used to
make decisions is displayed using data visualization
to enhance storytelling and gain buy-in. The
organization is making tangible and focused
investments in advanced analytics capabilities such
as machine learning.
Decisions are mainly insight-driven and utilize a
strong foundation of data. The decision-makers
converse fluently using data terms. There is active
use of data visualization to communicate business
decisions. Business and data weave
interchangeably. The organization is using advanced
analytic capabilities to drive their competitive
advantage.

18
02Develop a Data Literacy Strategy
Distinguish where you are from where you want to be
Set the “baseline” from your assessment, identify your goals,
and incorporate continuous improvement after that.
01
02
03
04
05
Create an actionable plan for how to get there
Include all necessary tasks, develop interim steps, identify
dependencies and roadblocks, and customize for your
organization.
Align around the strategic value of data and the role data
literacy plays
This is where the organization must buy into a cultural shift
and recognize what it means to be a data-driven company.
Gather the necessary resources
Strategy success depends on ensuring you have the right
resources with the right skills, empowered in the right way
with the right enterprise reach. Remember, data literacy is not
an IT driven project – that’s the wrong mindset.
Ensure the plan is achievable and executable
Review goals and tactics, take a leadership approach to make
things happens, be a proponent of change, and above all –
do data dailySM.
Once you have completed your data
literacy assessment and understand your
company’s data literacy status, you can
begin to develop a comprehensive data
literacy strategy.
Each strategy will be unique to a
company’s data literacy status and needs,
but the process for development is similar
and typically requires the following
actions:

19
03
The Beauty of
Data Visualization
The Best Stats
You’ve Ever Seen
Why Everyone
Should Be Data
Literate
The Power in
Effective Data
Storytelling
Executives can inspire their organizations to lean into
the world of data by sharing stories such as the ones
provided in these TED talks:

20
04Identify Data Champions
WHOARE
THEY?
Confident Data-Fluent
Communicators
Serving in Any Area of the
Business
Ask Challenging Questions
Desire Data-Driven
Answers
Seek Out Answers, Join
Forums, Encourage Sharing
WHO ARE YOUR DATA CHAMPIONS?

05Invest in a Data Literacy Foundation
Educate the organization on data elements
and data use
01
02
03
04
05
Emphasize the importance of reading, writing,
and speaking data in a business setting
Allow employees to practice with data
through data apprenticeships
Create data certification milestones
Communicate the availability of enterprise
data assets
If you aren’t sure where to start, reference The
Data Literacy Project. This site is supported by
data analytics leader Qlik and features courses
that cover data fundamentals, foundational
analytics, data-informed decision making, and
advanced analytics.
Building a foundation of data literacy in an
organization requires an investment not
only in technology, but in education as
well. Employees should be trained to
understand data concepts, work with data,
and make accurate decisions based on
data.
To build your data literacy foundation, you
should consider setting up a Data
University or similar function within the
enterprise data organization that will:

o Build and practice the data literacy concepts. Discuss different types of data,
how to differentiate them, and how to “speak” data in a business setting.
o Make the curriculum a mix of theoretical concept and practical application.
Challenge students to apply what they learn to their daily activities.
o Make it interesting. Incorporate games for competition or integrate storytelling
activities to discuss how people used data to drive business differently.
o Create a tailored experience. Customize the material for different audiences,
their roles, their learning styles, their needs, and the tools they use.
o Develop a mentoring program. Start a “buddy system” to help those who might
be struggling with data concepts.
o Tie coursework into career paths, performance reviews, and incentive structures.
This will ensure more widespread participation across the organization.
06Develop a Customized Data Literacy Curriculum

07Use Data in Decision Making
Convert the business decision to a data question
Evaluate how to bring insights rather than instinct to the business
decision. Evaluate data points that would help inform the decision one
way or the other.
01
02
03
04
05
Collect the data from trusted sources
Identify the underlying data that will be needed and obtain the data
from trusted data sources. That means two things: understanding
the context of the source data and assessing the quality of the data.
Consider the analysis options available
Evaluate the options for how to work with this data, what analytic
techniques and models might be available for use, and how to conduct
the needed analysis. Peel back the layers as you approach the problem
from different angles to prove or disprove your decision. Leverage
defined Key Performance Indicators (KPIs) where available.
Communicate the decision clearly
Utilize storytelling with data techniques to communicate your decision.
Recognize that stakeholders might need varying levels of
communication. Be clear about your deliberation and decision.
Conduct retrospective reviews
Collect new data points and as time goes by to evaluate your decision.
Recognize that no decision is bulletproof and in hindsight certain
decisions might have been made differently. Learn and adjust.
Organizations should think of business
problems in data terms: collect the
required data from trusted sources,
understand how to analyze data, learn how
to communicate the decision based on the
data, and analyze new data to evaluate the
decision.
To do this, organizations should leverage
the following framework to increase
critical thinking and move decision-making
from instinct-driven to insight-driven.

24
08
Big data governance requires special attention, as this
data may move from one setting to another. A
portion of this data might be used in a laboratory
before moving to a factory. The data will need
definitions and a defined process as it moves to the
factory setting and is used for production.

o They include metrics to highlight their accomplishments on their resumes.
o They can explain how to convert business questions to data questions.
o They can talk about and demonstrate their data handling skills.
o They can describe the use of Key Performance Indicators (KPIs).
o They can describe data problems they have solved.
o They can describe challenges that typically exist with dirty data.
09Hire Data Savvy Employees
Here are a few ways for an HR department to identify
data-savvy recruits:

26
Meetings
Set the tone at the start of the meeting by walking through an
agenda and explaining the goal of the meeting. During the
meeting, remind everyone to leverage data-based decision
making where possible. When closing the meeting, check if
the goals were accomplished. By doing this daily across the
organization, the culture will begin to shift.
01
02
03
Company Communications
As the organization highlights insights based on data in regular
company communications, the role and prominence of data
increases. The organization can leverage data-driven
storytelling techniques to communicate the importance of
data to front-line employees or enhance products and service
offerings.
Dashboards and Reports
Track and use metrics to stay focused on achieving goals.
Organizations that use dashboards and reports find there is an
increased awareness around the collection and use of data.
However, it is important to share metrics strategically,
considering how you can use these dashboards to positively
impact behavior.
10Integrate Data into Daily Activities
These activities help
embed data into the
DNA of a company
and start making data
the native language
by which employees
communicate
effectively.

“Do Data Daily”
27
01
Develop a Data Literacy Strategy
02
03
Identify Data Champions
04
Invest in a Data Literacy Foundation
05
Develop a Customized Data Literacy Curriculum
06
Use Data in Decision Making
07
08
Hire Data Savvy Employees
09
Integrate Data into Daily Activities
10
The best way to learn a new language is through immersion and learning to speak data is no different. As you
build your foundational knowledge of data elements, it’s important to practice new and old skills every day.
Through repetition, you move from comprehension to data analysis and decision making. This process is most
successful when those learnings are paired with experts who can offer tricks, insights, and lessons learned.
To accomplish this, we recommend organizations DO DATA DAILYSM .

Thanks For Joining Us
We hope you enjoyed the presentation.
If you’d like to learn more about how to develop and
foster a data-literate workforce,
download our eBook.
https://sensecorp.com/10_steps_to_data_literacy/
DOWNLOAD EBOOK
www.sensecorp.com | marketing@sensecorp.com

THANK YOU
Contact me with Questions or Comments:
ALISSA SCHNEIDER
aschneider@sensecorp.com

10 Steps to Develop a Data Literate Workforce

More Related Content

What's hot

Similar to 10 Steps to Develop a Data Literate Workforce

More from Sense Corp

Recently uploaded

10 Steps to Develop a Data Literate Workforce