Don’t Wait: What 300 L&D Leaders Have Learned About Data Fluency

WebinarNovember 2021

Data science and AI are impacting many industries globally, from healthcare and government to agriculture and finance. Everybody needs to be able to work with data the way everybody needed to start using email 20 years ago. As we wrote in Harvard Business Review, “Very few companies expect only professional writers to know how to write. So why ask only professional data scientists to understand and analyze data, at least at a basic level?”

But what value can data fluency actually add, what are the best practices to build it into your organization, and what are the biggest challenges that businesses encounter in data-driven transformations?

To answer these questions and more, we conducted a survey of over 300 Learning and Development leaders from diverse industries including healthcare, technology, consumer goods, government, and finance. Join this webinar with Dr. Hugo Bowne-Anderson, a data scientist and educator at DataCamp, to find out what we discovered and what 300 L&D leaders have learned about building data fluency.

You can find the slides here.

Summary

In a rapidly advancing digital environment, the need for data fluency is increasingly apparent across industries. Data has evolved from a disruptive element to a fundamental technology that is crucial to business strategies. Companies that effectively build data fluency witness substantial improvements in critical performance indicators such as revenue growth, market share, and employee satisfaction. Hugo Bowne Anderson, a Data Scientist at DataCamp, emphasizes the significance of a data strategy in constructing resilient business models and warns that organizations failing to utilize data effectively risk becoming obsolete. The discussion explores effective methods for creating data-driven cultures, the vital role of executive support, and the necessity of developing basic data skills within organizations. Major challenges, like the talent gap in data science, highlight the need for companies to develop internal talent through continuous learning and upskilling. The webinar also probes the practical aspects of implementing data strategies, offering insights into business intelligence, machine learning, and decision science. Finally, the discussion mentions the potential obstacles in data transformations and proposes innovative solutions to overcome them, including cultivating a culture of continuous learning.

Key Takeaways:

Data fluency is vital for competitive business strategies and avoiding becoming obsolete.
Organizations with advanced data fluency outperform those with less developed competencies in critical business metrics.
Effective methods include executive support, basic data skills, and a strong vision for data use.
Attracting top talent is challenging; companies should focus on developing internal talent through upskilling.
Continuous learning and adaptation to technological advancements are essential for sustainable growth.

Deep Dives

The Importance of Data Fluency

Data fluency is becoming an essential element of ...
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contemporary business strategies. Companies that fail to adopt data-driven approaches find themselves at a significant disadvantage, as illuminated through the case study of Blockbuster's downfall in contrast with Netflix's data-centric model. Hugo Bowne Anderson asserts that industries are now experiencing a shift where data, machine learning, and AI are fundamental technologies. "Data science doesn't only predict the future, it causes the future," says Cathy O'Neill, emphasizing the transformative power of data. Organizations that prioritize data fluency are equipped to leverage this potential, achieving higher revenue growth, market share, and customer satisfaction. The urgency to build data fluency is highlighted by statistics showing that nearly 89% of companies recognize its importance, although a disparity exists between those with advanced and less developed data competencies.

Effective Methods for Creating Data-Driven Cultures

Creating a data-driven culture requires strategic alignment and support from top executives. Successful data transformations are often led by strong C-level advocacy, a clear vision for analytics, and a solid data infrastructure. Jacqueline Nolas categorizes data science into descriptive, predictive, and prescriptive analytics, each playing a vital role in business intelligence. Organizations must cultivate a data culture where skills are distributed across the workforce, enabling data-driven decision-making at every level. Angela Bassa from iRobot emphasizes the importance of making data exploration a collaborative and enjoyable activity through initiatives like data parties. Additionally, early demonstration of the impact of analytics is vital to securing ongoing support and investment in data initiatives.

Obstacles in Hiring and Developing Data Talent

The talent gap in data science presents a significant challenge, with many companies struggling to attract top talent. Hugo Bowne Anderson suggests that organizations should focus on internal talent development instead of relying solely on external recruitment. By investing in reskilling and upskilling current employees, companies can overcome the scarcity of data professionals. Online learning platforms have emerged as a vital tool in this process, enabling employees to acquire necessary skills flexibly and efficiently. The shift from content creation to curation in learning and development reflects the growing importance of continuous learning in today's workforce. As Anderson notes, the paradigm shift in professional life demands ongoing skill acquisition to keep pace with technological advancements.

Overcoming Obstacles in Data Transformations

Data transformations face numerous hurdles, including the complexity of business operations and the scarcity of skilled data professionals. Companies must adopt innovative strategies to overcome these challenges, such as cultivating a culture of continuous learning and leveraging online education resources. The ability to demonstrate quick wins with data analytics is vital in gaining support from stakeholders and encouraging further investment in data initiatives. Taras Karishni from McKinsey emphasizes the need for executive support and a clear vision for analytics to drive successful data transformations. By aligning incentive structures with data goals and investing in basic data skills, organizations can create a resilient data culture that supports long-term innovation and competitiveness.