Foster a culture of data literacy

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Foster a culture of data literacy
01 Tips for democratizing data access
01 Tips for democratizing data access
02 Tips for organizing information in a data-driven organization
03 Tips for training a data-driven organization
04 Tips for leading a data-driven organization
What is data literacy and why is it important?
As AI transforms global workplaces, data literacy skills will be in high demand. In fact, 98% of companies that have been most successful with data and analytics initiatives have taken steps to improve data culture. But what exactly is data literacy?
Gartner defines data literacy as the ability to read, write and communicate data in context.¹ These skills depend on understanding data sources and constructs, as well as analytical methods and applied techniques. The ability to describe data use cases, applications and the resulting value is also key.
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 competence everyone needs—not just data scientists,” says Mara Pometti, IBM’s Global AI Strategist. “We need experts who can challenge algorithms by asking critical questions of the data and interpreting it correctly. But to have these types of experts, we must increase data literacy. We need to democratize the skills that allow everyone to read, understand and communicate with data.”
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.
Ninety eight percent of companies that have been most successful with data and analytics initiatives have taken steps to improve data culture.²
Half of companies most successful with analytics projects in the past two years have invested in improving employee data literacy and skills.²
4 foundations to establish a data-literate culture: 1. Democratize data access; 2. Organize information to empower technical and non-technical users alike; 3. Train teams to be responsible, analytical data users; 4. Lead with empathy to create data champions.
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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
Create access to the right data at the right time
Leverage an architecture that enables quick and simple access to data across a disparate data estate.
Prepare data set before integration
Take care in cleaning existing data and preserving data privacy, security and compliance measures as you combine data sets to ensure data is meaningful.
Check permissions
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.
Reinforce the value of data access to stakeholders
Connect the value of self-service data access across the organization to each senior stakeholder's business goals to create excitement, reduce likelihood of disconnected ecosystems and help secure support.
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.
Mehdi Charafeddine
Tips for training a data-driven organization
Teach people to tell data stories
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.
Design training to solve for everyday problems
Design training to solve for everyday problems. Be sure your education programs reflect the real-world needs of different roles and connect data to the day-to-day value stakeholders.
Close gaps
Recruit hires with technical certifications or P-TECH program degrees to close skills gaps and increase the quality of your outputs.
Assess skills and upskill as a practice
Continuously track how your organization is evolving toward a data-driven organization and support the efforts with dashboards that define metrics and KPIs.
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
Create C-suite partnerships
Take a use-case-first approach that reinforces the value of data literacy for cross-organizational leaders and gets senior stakeholder buy-in.
Provide the opportunity for critical thinking
Open conversations at every level (for feedback about systems and processes to gauge resistance) generate better outcomes and clarify the value data can deliver back to the organization.
Model data literacy skills
Model ideal behavior, like not taking data at face value and challenging teams on data insights that raise questions, so data literacy becomes second nature.
Encourage data competence
Whether it’s groups well-versed in legacy or modern systems, different demographic and socio-economic backgrounds, or different roles and responsibilities, encourage teams to network in and outside the organization so diverse perspectives are represented in all aspects of work.
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.
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