Industry-Tailored Reskilling Accelerates Data Maturity

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Click to learn more about author Merav Yuravlivker.

Companies are increasingly looking for ways to meet the demands of our data-driven world, and developing a data-mature organization is key to success across all areas of a business. In today’s environment, Data Literacy is no longer reserved solely for those who work in IT. In all departments, including marketing, human resources, and business development, employees across an organization must first become familiar with the vocabulary and potential of data. Next, they need to learn how to leverage the right tools, techniques, and strategies to make data-informed decisions and use data effectively. 

So, how do organizations embrace “data maturity” and ensure their team members are equipped with the necessary tools to achieve their goals? 

The first step is understanding the data analytics maturity model and how it can be utilized within the organization. The data analytics maturity model is a measurement of the different stages an organization passes through on its way to becoming data-driven. It’s essential to understand these stages so companies can determine specific, actionable steps needed to enrich their teams’ Data Literacy. Beginning with using diagnostic analytics to understand what has happened in the past, organizations can identify root causes and provide analytics to interpret why something occurred or even predict what may happen in the future. By understanding where organizations fall on this maturity scale and benchmarking their baseline levels for data infrastructure and Data Literacy, leadership can accurately determine any skills gaps or opportunities for improvement and establish a path forward. 

The second step to creating a data-mature organization is reskilling internal teams with Data Science training programs that are tailored to their specific industry. Organizations should use the findings of their initial assessments to inform the content of customized learning programs for their workforce that are based on their objectives. Industry-tailored programs are beneficial because they can directly connect an organization’s real-time data to specific and tangible outcomes for the company. These training programs can equip their workforce with the skills they need to succeed and achieve their goals in the present and future. 

Organizations may think it’s more efficient to try and recruit new employees who already have the data skills they are currently lacking. However, research suggests that reskilling existing employees is more cost-effective than overall replacement costs, which range from 90-200%. For example, if an employee makes $60,000 per year, it costs an average of $30,000-$45,000 just to replace that employee and roughly $54,000-$120,000 in overall losses to the company. Yet, reskilling an employee who has an established presence in the organization and relationships with colleagues can cost less than $10,000.  

Another benefit of reskilling employees instead of hiring externally relates to team members’ existing organizational knowledge. Since they are already familiar with internal data points, circumstances, and goals, employees can quickly apply Data Science skills learned in their trainings to known situations and challenges they are facing in their roles. Additionally, the data skills they gain often lead to increased productivity and the power to imagine new ways of improving their department outcomes.  

At the organizational level, reskilling existing employees can significantly increase enterprise-wide Data Literacy and help to quickly reach data maturity, leading to a more well-rounded, cross-trained workforce with increased effectiveness. In addition, these trainings create an untapped resource to harness internal talent and drive innovation and impact, without high recruiting costs.  

As organizations move through the maturity model, they are now equipped with the knowledge and skills necessary to plan and execute data projects to realize new opportunities and take their careers to a new level. Ultimately, the road to data maturity comes from within an organization and can be seamless if the proper assessments and training methods are along for the journey. 

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