Key actions to successfully build data fluency

Knowing the broad range of areas in which data fluency can create value, it’s essential to determine how to actually build data fluency. So we asked L&D leaders what data skills are most important and what actions they’ve planned to build data fluency.

The first step: Building foundational data skills

Survey results show that establishing foundational data skills is the most important first step for businesses. Seventy-two percent of those who’ve already invested in data fluency recognize this versus 58% of those who haven’t yet invested. After foundational data skills, the next most important competencies are business intelligence and dashboards (descriptive analytics), decision science (prescriptive analytics), and machine learning (predictive analytics), respectively.

These findings indicate that businesses are taking the right steps to assess their data needs, in line with Rogati’s Hierarchy of Needs. Many companies have plans to tackle major initiatives in AI or machine learning right now. But if you don’t have the foundational data skills and infrastructure you need in place to enable these initiatives, they’re bound to fail. Before you can get complex projects off the ground, you need a strong foundation of data skills across your company. Prioritizing basic data fluency needs is a critical step in the right direction.

Establishing a company vision for analytics and a strong data infrastructure foundation

So, how are companies approaching the process of building data fluency? According to those with mature data fluency competencies, the most important actions are, in order of priority:

  1. Establish a high-level company data strategy
  2. Build a strong data foundation
  3. Implement process redesign and culture change
  4. Build strong executive support
  5. Drive value from several use cases

The above chart shows that only 20% of companies with immature data fluency competencies recognize the importance of having a solid data foundation, and even fewer are prioritizing a high-level company data strategy. Both of these are essential for building data fluency at the organizational level. Every company should ensure that their vision for data and analytics is aligned with company strategy. And they must also build out their data infrastructure, including their security setup and data warehouse, to ensure their data is integrated, trusted, and timely (2). Strong data infrastructure is to companies what foundational data skills are to individuals—they’re the tools that enable us to do more with data.

(2) DataFramed podcast, "Data Science at McKinsey (with Taras Gorishnyy)", July 2018

Building data fluency requires major investment—but it’s worth it