Find out what data literacy actually means, who should care, and how organizations can improve the value of data across all core business functions.
Have you ever accidentally channeled your inner Jan Brady, but instead of feeling overshadowed by your cooler, more popular older sister, you feel overwhelmed by copious amounts of information that you don’t know what to do with? We brush shoulders with data every day. In the workplace, on the news, while scrolling our social media feeds…it hits us from every angle in the form of graphs, charts, statistics, and those annoying (yet occasionally impressive) targeted advertisements aggregated by our own behaviors. Be it working on your department’s 2022 forecasts or placing a waiver claim for your fantasy football team’s bench, most of us cannot escape data as we make decisions in all aspects of life.
What does this so-called “data” really mean?
How can we be sure that we are interpreting and using it correctly?
Is this what it means to be “data literate”?
What 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.
In lay terms, we see data literacy as the backbone of digital transformation. More specifically, literacy isn’t just the ability to understand but also the yearning to use data as a tool to improve processes and decisions in everyday life. As such, data literacy is not exclusive to an organization’s IT department. Like developing the basic ability to operate a computer, data literacy has quickly become a required skill for employees across all departments. Without a working knowledge of what data means and how data can be used at all levels of business to make decisions and improve processes, organizations cannot effectively align strengths with priorities or identify potential challenges that could derail business roadmaps.
Staying Ahead of The Data Literacy Curve
For most organizations, the issue isn’t recognizing the importance of data; it’s figuring out where to begin improving data literacy across the organization. The idea of technical education can quickly become as overwhelming as the data itself, but if leadership teams break down the objective into three actionable tasks, it is possible to get workforces up to speed and ahead of the data literacy curve.
Step 1: Use Your Tech Roadmap to Evaluate Staff Skills
A technology roadmap is a governing document that dictates specifically how technology will support the business strategy and help drive businesses priorities over the next 3-5 years. These blueprints can be used to analyze overall strategy, develop timelines, list improvement opportunities, and justify budgets for upcoming projects. In other words, they represent an essential planning tool for identifying the future needs of your business. To that end, we recommend using them to assess the current data literacy levels of your organization’s staff to determine where skills must be strengthened to drive the progress and efficacy of all digital transformation efforts. With a better understanding of the skills you have versus the skills you need, tech road-mapping will open up a larger conversation amongst leadership on the need for data literacy skills across your organization and how to reach the next level of literacy to expand the company’s visibility across all functions.
Step 2: Make Data Literacy an Enterprise-Wide Priority
As one of your most valuable assets, data is a precious resource that the company cannot afford to squander. One cannot develop accurate insights in any aspect of the organization without the ability to understand, interpret and utilize the immense volume of data at their fingertips. Forrester’s latest analytics survey shows that, on average, companies make only 48% of decisions based on quantitative information and analysis. This number hasn’t shifted much over the years, which is likely due to a lack of data literacy skills at all levels of the organization. Whether it’s the HR team using data to design a benefits program that aligns with employee preferences, marketers using data to connect with the right prospect at the right time, or finance using data for predictive business modeling, all departments will eventually require a working knowledge of how to parlay information into strategic decisions.
Step 3: Invest in Data Literacy Training That Cultivates Skills Within the Context of the Business.
It’s no secret, building L&D programs can have a broad and powerful impact on many aspects of the organization.
- Effectively close skills gaps
- Boost morale
- Reduce employee turnover
- Groom future company leaders
- Increases employee loyalty & productivity
- Support innovative thinking
The list goes on. What many organizational leaders don’t realize is that upskilling programs are alsoa more affordable and effective route to filling talent gaps and meeting an organization’s needs compared to the costs and risks of hiring new talent. From recruiting fees and competitive salary negotiations to slower time-to-productivity and the ever-present risk of turnover, creating opportunities for the employees you already have makes far more business and financial sense.
When building an internal data literacy program, look for program solutions that focus on learner engagement and seek to understand the current skill levels of your workforce. While training solutions must cover all aspects of data literacy, including analysis and engineering, to ensure that learners cultivate the skills to read, write, communicate, and visualize data, the pace and format in which a curriculum is delivered will largely determine learner success. More specifically, use the following six attributes to qualify potential solutions for technical education:
1. Meaningful lessons that are relevant to how skills will be used in the workplace.
2. Advanced learner and program assessments that effectively benchmark progress and performance.
3. Classroom autonomy and collaboration to engage learners and expand discussions.
4. Training elements that focus on the long-term mastery of skills, not short-term success.
5. Experiential learning activities that build real-life experience and confidence.
6. A process for analyzing program performance and using insights to improve learning outcomes.
Interested in building a training program for data literacy to bridge skill gaps across your organization? Not quite sure where to start? Instead of channeling your inner Jan Brady, check out Stage 3 Talent to learn more about how we build and deploy full-scale data literacy programs that deliver job-ready data skills for immediate utilization.