What is data literacy and why is it important?
As AI transforms global workplaces, data literacy skills will be in high demand. In fact, 79% of companies 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? Building a data-driven organization with a culture of data literacy makes it possible for everyone in the organization to make better, data-driven decisions that lead to better results.
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.
By applying data in a business context and then reaping insights from that data in an environment with continuous support and training, an organization comes to crave data-integrated workflows. And once people begin making more valuable decisions informed by data, it’s hard to go back.
Almost eighty percent of organizations state that looking ahead twelve months, data will be more important to their organization’s decision-making.¹
Almost thirty percent of organizations cite decision-making skills to translate data analysis into action are their least mature skills relative to others associated with being data-driven.³
If your leadership and teams know how to discuss, collect, use, share and manage data, your organization is set up for success—data will be at the heart of everything that gets done.
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, Vice President, IBM Global Chief Data Office (GCDO). By building a central repository, such as a data fabric, people across your organization can easily store and access data, thereby simplifying data access.
To create democratized data access, IBM’s GCDO 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.
Tips for democratizing data access
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. After all, technophobia is real.
And 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 this 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.
We had this transition in the late ’80s, ’90s and 2000s to get people literate about using computers and tools like email and word processors. I see a similar journey for data literacy. It’s really about the ability to find and understand data, how to evaluate data and how to create insights out of it.
Global CTO for IBM Data Platform Services
Tips for organizing information in a data-driven organization
3. Train data citizens to use and analyze data responsibly and turn data into action
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 that 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
Almost half of organizations taking steps to become more data-driven have invested in improving data literacy and skills.⁴
Data literacy and data training are very, very important in an organization. It’s not just for data analysts or business analysts or data scientists. It has to go all the way to the executive management, right up to the CEO. And the CEO needs to understand the importance of data.
Enterprise Data and Analytics Leader
Tips for training a data-driven organization
4. Lead with empathy to create data champions
Curiosity is at the core of data-driven decision-making and building a data-literate culture. The employees and leaders in your data-literate organization will always be asking “why” and never taking anything at face value. Your job is to be a good listener and figure out with them how data literacy skills can deliver results back to the business. “People need to understand what can be done with data,” says Inderpal Bhandari, IBM’s Global Chief Data Officer. “The cultural change element of the data leader role is concerned with influencing how data is used from within and being the example that others can follow. If they don’t focus on this, how can they expect anyone else to care?”
By ensuring that employees understand how data works across the organization, you are helping them lead with empathy too. This is essential in a culture of data stewardship, which ultimately leads to a network of data champions across your organization, and data literacy becomes part of a virtuous learning cycle.
Tips for leading a data-driven organization
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.
How do you get started?
Building the right data architecture is an iterative process, and it will adapt and grow over time with your business. We’re here to help.