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What L&D Leaders Need to Know About Data Fluency

November 2021

You can find the slides here.

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Over the past two decades, digital-first startups such as Uber, Amazon, Airbnb, and Revolut have disrupted vital industries such as transportation, commerce, travel, and banking. Rightfully so, organizations across all industries have recognized the need for digital transformation to compete in the new information economy. Despite massive digitalization investments, the painful truth is that approximately 70% of digital transformation initiatives fail to reach their stated goal (McKinsey).

While there could be many culprits for the failure of digital transformation programs—a crucial reason is not recognizing that having sustainable organization-wide data transformation is a prerequisite for successful digital transformation. It's relatively straightforward to invest in tools and infrastructure that enable organization-wide data transformation, but the most challenging pillar to address is the growing skills gaps in data analytics and data fluency.

In this webinar, we'll address all the key data fluency competency areas within organizations. We will also break down crucial data personas found in every organization, allowing learning and development teams to scale personalized learning paths for their people systematically.

Summary

In an ever-changing digital environment, data fluency has emerged as a critical skill for organizational success. The discussion explored the key role of data fluency in digital transformation, highlighting that despite significant investments, a staggering 70% of digital transformation projects do not succeed. This failure is often associated with the insufficient use of organizational data and the lack of data analytics in business strategies. The speaker emphasized the need for fostering a data-driven culture, where decisions are guided by data insights. By addressing the data fluency skill gap, organizations can improve their capacity to adjust digital initiatives, make data-informed decisions, and promote a more empowered and autonomous work environment. The session also analysed the successful data fluency upskilling initiatives implemented by major companies like Marks and Spencer, Amazon, and Airbnb, emphasizing the positive impact on business performance. Additionally, it introduced the concept of eight key data roles that L&D leaders can use to adjust their upskilling efforts and ensure that employees have the appropriate level of data skills. The discussion wrapped up with practical steps for organizations to expand data fluency programs efficiently and align them with business goals for maximum impact.

Key Takeaways:

  • Data fluency is crucial for successful digital transformation, yet 70% of digital projects fail due to the absence of a data strategy.
  • Organizations face a significant data fluency skill gap, impacting decision-making and employee well-being.
  • Successful upskilling initiatives have demonstrated a positive return on investment by enhancing data-informed decision-making.
  • Identifying and developing key data roles can help adjust data fluency programs for different organizational roles.
  • Building a data-driven culture requires aligning learning and development with business goals and promoting a shared data language.

Deep Dives

The Role of Data Fluency in Digital Transformation

Over the past two decades, d ...
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igital transformation has been a defining force across industries, reshaping how organizations operate and engage with customers. Despite significant investments, many digital transformation projects do not meet their goals, with 70% failing to achieve their intended outcomes. This failure is often attributed to the insufficient use of data within organizations. According to Gartner, 60 to 73% of organizational data remains untouched for analysis, and 50% of companies do not incorporate data analytics into their business strategies. As Adele Neymeh, the speaker, emphasized, "Data transformation underpins digital transformation." To succeed in digital initiatives, organizations must prioritize data-driven cultures, where insights derived from data guide decision-making and innovation.

Addressing the Data Fluency Skill Gap

The data fluency skill gap poses a significant challenge to organizations aiming to become data-driven. A McKinsey survey revealed that 43% of businesses identify data analytics as the most pressing skill gap to fill. This gap affects not only decision-making but also employee well-being. A survey by Accenture and Click found that 50% of employees avoid data-related tasks, and 74% feel stressed by working with data. Upskilling initiatives are key to addressing this gap. Major companies like Marks and Spencer, Amazon, and Airbnb have launched data fluency programs with remarkable success. These initiatives have led to improved performance, increased urgency in addressing skill gaps, and enhanced business outcomes.

Successful Data Fluency Upskilling Initiatives

The positive impact of data fluency upskilling is evident in the success stories of leading organizations. Marks and Spencer launched a Data Academy for over 1,000 employees, creating a data-literate management team. Amazon's Machine Learning University equips developers with the skills needed to expand machine learning applications. Airbnb's Data University offers a comprehensive curriculum for all levels, ensuring data-informed decision-making across the organization. AT&T's ambitious 1 billion dollar upskilling initiative for 140,000 employees highlights the commitment to building data fluency as a strategic priority. These initiatives demonstrate that investing in data fluency yields tangible benefits, including improved business performance and competitive advantage.

Identifying and Developing Key Data Roles

To efficiently expand data fluency programs, organizations must identify and develop key data roles. These roles represent different positions within a data-driven organization, each with unique relationships to data. The eight data roles include data leaders and consumers, business analysts, data analysts, data scientists, machine learning scientists, statisticians, programmers, and data engineers. By mapping these roles to specific job roles, organizations can adjust their upskilling efforts to ensure employees have the right level of data skills. As Adele Neymeh explained, "Data fluency is not a binary skill; it includes a broad spectrum of skills and competency areas."

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