.png){kind=logo}
Would you regard your ability to understand, analyze, work with, and communicate with data as a response to the question, "What languages do you speak?" in a job interview? You would have a valid point. Data literacy, which Gartner has called the "second language of business," is predicted to be the most in-demand ability by 2030.
An organization with data literacy is not only more adaptable, organized, and creative but also has a higher retention rate. Because there are insufficient opportunities for upskilling and training in data literacy, 35 percent of employees have changed occupations in the past year.
Becoming a data-literate organization, however, requires more than just training! It also involves changing people's behaviors and perspectives. This will enable them to actively and naturally examine and challenge the data in front of them to generate instant insights and more intelligent decisions and actions.
What, then, is knowledge of data? The definition is the ability to analyze, understand, and work with data meaningfully. There are several degrees at which this can be accomplished, ranging from very basic to quite specialized and complex.
A foundational understanding of statistics and mathematics is necessary for data literacy since data analysis can be challenging, especially when working with huge datasets. Many businesses use data scientists for such tasks.
Assessing the skills gap between what your employees now possess and what they need to succeed in a data-literate environment will help you identify what training, resources, and skills you need to create a data literacy program.
Using the skills gap analysis, you can ascertain:
What competencies does your company require to achieve its objectives, like 40% of employees?
The kinds of instruction that you must offer
Who ought to be obliged to acquire each skill?
A strong blend of advocates and internal champions who can maintain the momentum of your program should be on your task force. As you assemble the team, consider who within the company would be most qualified to respond to certain inquiries about this initiative.
A one-size-fits-all strategy is a major contributing factor to the failure of data literacy programs. When developing your data literacy program, aim to develop a tiered curriculum that considers the needs of individuals and groups.
Acknowledging the disparities in experience and expertise within your teams is critical. In addition, if you train your data workers in analytics, you should also consider educating them in communication and concept presentation.
Selecting a training approach and tools shouldn't be a one-size-fits-all process, just like choosing a curriculum. You must choose appropriate training techniques, materials, and formats for your teams if you want your data literacy program to have the best chance of success.
The aim is to encourage and motivate people to embrace the program and learn. To accomplish this, people require formats that fit their learning styles.
Although starting an effective data literacy program is not simple, it can pay off through improved employee happiness, increased process efficiency, and useful business outcomes.
