Maximize Data Value: Invest in Python and Excel Training for Employees Over Specialized Programming Languages
In a recent survey by NewVantage Partners, the disparity between data investment and actual data-driven practices within organizations was stark. While a notable 93.9% of executives plan to increase their data investments in 2023, only 23.9% of organizations describe themselves as truly data-driven. This raises an important question: if so much money is being poured into data initiatives, why isn’t there a corresponding shift in organizational operations? The answer lies in the challenges of implementing data-driven practices—primarily, the human element.
Cultural Barriers are a significant obstacle. According to the same survey, 79% of executives identify cultural issues as the primary barrier to embracing a data-driven future. The reality is that while executives may champion a data-driven vision, transforming a company’s culture to support this vision is far more complex. The crux of the problem is that data alone cannot drive change; it requires people who can interpret and act on the data. Hence, the goal should be to use data to support and enhance human decision-making rather than attempting to replace the human element with data alone.
Python and Practical Tools offer a potential solution to this challenge. Gartner analyst Svetlana Sicular once highlighted two key insights: first, that employees who understand their own data are often more valuable than external data scientists; and second, that learning industry-specific knowledge is more crucial than mastering complex data tools. To bridge the gap between data investment and actionable insights, it is essential to enhance programming literacy among employees. Simplifying access to data tools and enabling employees to use them effectively can significantly improve data utilization.
Making Data Tools Accessible is a strategic approach to overcoming cultural barriers. For example, incorporating Microsoft Excel into data analytics initiatives is a practical step forward. Many employees are already proficient in Excel, and expanding its use for data transformation and analysis can leverage existing skills. This approach is likely to be more effective than mandating the use of specialized tools like TensorFlow or Hugging Face, which may be less familiar to the average employee.
Python emerges as a particularly valuable tool in this context. Although languages like R and various specialized tools have their place, Python’s accessibility and widespread use make it a major driver of productivity in AI and data science. Python’s simplicity and extensive libraries have made it the go-to language for a growing number of data engineers. As Nick Elprin suggested, data science is increasingly becoming an enterprise-wide capability, and Python’s broad accessibility makes it a strong candidate for dominating this space.
Fostering Data Literacy Across the Organization involves investing in training and tools that align with employees’ existing skills. By focusing on making data tools like Excel and Python more accessible, organizations can empower their workforce to make better use of data without needing extensive specialized knowledge. This approach not only facilitates a smoother transition to a data-driven culture but also ensures that data-driven insights are grounded in a deep understanding of the business context.
In conclusion, the key to realizing the full potential of data investments lies in addressing cultural and human factors. By equipping employees with practical tools and fostering data literacy, organizations can bridge the gap between data investment and actionable insights, ultimately driving meaningful change and achieving a more data-driven future.