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Foster a culture of data literacy
Without data literacy, there is no ROI from data and AI
As AI transforms global workplaces, data literacy skills will be in high demand. In fact, 79% of organizations state that looking ahead twelve months, data will be more important to their organization’s decision-making.¹ But what exactly is data literacy?
Gartner® defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use-case application and resulting value.²
Why do these skills matter? To lead an organization with AI-powered, data-driven decisions, data literacy is a competency everyone needs, not just data scientists. Whether a person is just starting their career or in the C-suite, the ability to understand, interpret and communicate using data with it is a crucial skill for all employees.
In an environment where training is available to help teams understand the value of data to their daily responsibilities, teams can more easily get and apply insights from data and begin to crave data-integrated workflows. Over time, this can lead to more confidence and willingness to delegate decisions to AI because they understand the underlying data that shaped the recommendation.
The 4 foundations of a data-literate culture
What does it take to get data literacy right?
1. Democratize data access across your enterprise
Many people think of data science training programs as the first step to becoming a data-driven organization, but it really all starts with making data more accessible. Think about a call center system. Most of the time that data is locked into the application and not made available to the rest of the organization. But if it were shared with client consent, call center data analysis could help with training and education, overall efficiency and better communications for that part of the organization.
“Sometimes you need to help people appreciate the value that different types of insights can bring, especially at scale and outside of individual functional areas and domains,” says Tim Humphrey, Chief Analytics Officer at IBM. By building a central repository, such as a data fabric, people across your organization can easily store and access data, thereby simplifying data access and opening the door for technologies like data analytics and AI to streamline workflows.
To create democratized data access, the GCDO at IBM implemented a unified data platform that provides a central source of governed data and allows users to load, transform and analyze data. Since its launch, the platform has quickly improved business outcomes for the GCDO. In about 18 months, the office generated USD 1.3 billion in business benefits and a 10x ROI from data and AI-based transformation initiatives.
IBM GCDO generated 10x return on on investment from data and AI-based transformation initiatives.
Implement an architecture that enables quick and simple access to data across a disparate data estate.
Take care in cleaning existing data and preserving data privacy, security and compliance measures as you combine data sets to ensure data is meaningful.
Assess relevant data-access rights, licensing and sharing permissions as you integrate data across sources, ecosystems and silos, so insights aren’t trapped at a functional level and can be scaled across the enterprise.
2. Organize information in a clear and transparent manner
Once you’ve established a platform for governed data access, it’s important to help decision makers understand how data moves throughout the pipeline. So, communicate data’s value, origin and quality with clarity and respect for every level of expertise. This is the fastest way to data empowerment for technical and non-technical users alike and to inspire trust in AI initiatives (after all, technophobia is real). When data is organized in a transparent and explainable manner, people can more easily understand the data before and after AI is applied.
While not everyone needs to have the knowledge of a data scientist, everyone should have an understanding of data, its lineage and how it flows within end-to-end processes—not just one part of a process. Achieving that understanding requires asking a few key questions.
- What is the source of the data and is it trusted?
- What are the metadata, rules and compliance policies behind it?
- What does the data generated from this algorithm mean to its intended users?
- How can I explain the business value of this data to deliver better business outcomes?
Your teams should be able to search for data, get access to all the data that they’re supposed to get access to, and then enable business applications with it.
Use metadata and standardize the definitions and terminology associated with data across business functions.
Find KPIs that show how data literacy contributes to business objectives. Surface meaningful insights, track data use, and test and optimize a few initiatives at a time.
Help teams track and understand data lineage and ensure that these are consistent across the organization.
3. Train data citizens to use and analyze data responsibly and turn data into action with AI
Data literacy training helps your organization read, decipher and use data (especially when sourced by a model) for better decision-making. But it also empowers teams to use data as a competitive differentiator. To apply their training and connect data to business outcomes, your teams need a good understanding of the data tools they have and how they can be used to accomplish their goals. Ultimately you need experts who can humanize data and AI by making data more meaningful to people. A data literacy program is successful when your teams can translate the data into compelling, visual stories that stick with people and transform data into actionable knowledge and concrete business results.
Johnson & Johnson is supporting its employees by educating them on how to best leverage advanced and emerging technologies, including AI. “In partnership with IBM, we created an AI-driven skills inference model for the Technology function that married de-identified external data with skills data from our internal data sets,” says Jim Swanson, Chief Information Officer at Johnson & Johnson.
“We were able to take the data on employee skills that resides in tools that my IT organization uses and feed it to the model. The AI was then able to determine everyone’s maturity level in each of the skills that we wanted to highlight creating a comprehensive view of individual strengths and weaknesses,” says Swanson.
Like Johnson and Johnson, organizations can build data literacy by starting with a highly connected business strategy at the executive stakeholder level and mapping it across the stakeholder domains.
“When stakeholders complain data endeavors ‘failed’ or didn’t deliver what they were expecting, it is often because the executive strategy is not clearly defined and the data literacy of the stakeholders is not aligned across the domains and the team,” says Jennifer Kirkwood, Partner, Global Head of Talent Data, IBM Consulting.
Ensure professionals at all levels of the organization can use data visualization and storytelling techniques best suited to their strategic business objectives, and root this training in a communication effectiveness curriculum.
Be sure your education programs reflect the real-world needs of different roles and connect data to the day-to-day value stakeholders.
Recruit hires with technical certifications or P-TECH program degrees to close skills gaps. Use dashboards that define metrics and KPIs to track how your organization is evolving to be more data-driven.
4. Lead with empathy to create data champions
Curiosity is at the core of data-driven decision-making and building a data-literate culture. Data-literate employees and leaders are always asking “why” and never taking anything at face value; adopting this attitude becomes crucial to ensuring that recommendations given by AI continue to accurately serve the needs of your organization.
Your job is to be a good listener and figure out with your teams, based on their unique roles, which data literacy skills can deliver results back to the business and put a training plan into place.
These data advocates are fully empowered within IBM in the sense that if they find a like-minded group in accounts receivable or supply chain, for example, and want to move ahead with data and AI capabilities, they can move forward without having to come back for permission or funding. By ensuring that employees understand how data works across the organization and where AI fits into the mix, you are building a culture of data stewardship. This ultimately leads to a network of data champions across your organization so that data literacy becomes part of a virtuous learning cycle.
Take a use-case-first approach that reinforces the value of data literacy for cross-organizational leaders and gets senior stakeholder buy-in.
Encourage open conversations at every level and include diverse perspectives to generate better outcomes. Continuously clarify the value that data can deliver back to the organization.
Model ideal behavior, like not taking data at face value and challenging teams on data insights that raise questions. Encourage teams to network in and outside the organization so diverse perspectives are represented in all aspects of work.
In the age of AI, data literacy is data empowerment
As data and AI become core to every aspect of running an organization, data literacy is foundational to building a data-driven culture. As a data leader in your organization, you are promoting change and supporting larger business goals by instilling a common language that’s based on data. Your efforts may be challenging, but those ambitious ideas fill a much-needed gap, and the investment is worth it. The future of your enterprise depends on it, in fact.
Don’t stop now. Continue to foster development of the right data literacy skills based on your business objectives, and establish yourself as a teammate in the C-suite and across the entire workforce. “To truly be data literate, this way of thinking should transcend all roles, not only be evident at the bottom, top, or middle,” Humphrey says. In other words, data literacy is a cyclical journey for every level of the organization.
Above all, remember that you are the model. As a data leader, your example sets the tone and ensures that your teams are comfortable speaking about data and letting data drive better business outcomes. With your advocacy and data literacy framework in place, you’re turning data insights into action—and laying the groundwork for a culture of data champions and data-driven decision-making for years to come.
Learn how to build a strong data foundation for scaling and governing enterprise AI.
Footnotes
¹ Voice of the Enterprise: Data & Analytics, Data-Driven Practices, 451 Research, 2022
² How to Create a Balanced Data and Analytics Organizational Model, Gartner, 10 May 2022. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
³ 2023 Chief Data Officer Study: Turning data into value, IBM Institute for Business Value, 2023
⁴ Voice of the Enterprise: Data & Analytics, Data-Driven Practices, 451 Research, 2022
⁵ Voice of the Enterprise: Data & Analytics, Data Management and Analytics, 451 Research, 2021