Data Literacy: How to boost your ROI from (existing) data solutions/products?

Introduction

As data-driven decisions become the cornerstone of business efficiency, organizations are rapidly expanding their data and analytics initiatives. This expansion includes the delivery of new data products and insights from existing data assets. However, a critical challenge remains: effectively leveraging these data assets to maximize success. Reflecting on the findings from the Gartner & Analytics Summit 2023, a striking revelation emerged: a significant number of data and analytics teams are not delivering optimal value to their organizations. Analysts found that the primary barriers to success in Data & Analytics are human-centric, with inadequate data literacy being a significant obstacle. Here, I’ will explore how fostering a robust culture of data literacy can not only overcome these barriers, but also propel organizations to new heights of success.

Meaningful Data Products: A Personal Reflection

Before I dive deeper, let’s explore the concept of “data as a product”. In the traditional way, organizations added new data sets to their ecosystem – usually driven by a new reporting need. In most cases, the focus was only on the scope of the report itself, without a holistic understanding of the dataset. The data was simply the by-product of a new dashboard. The result of this approach was that there were multiple data integration processes scattered throughout the organization with no understanding of the underlying processes. This inherently limits the number of people who can truly benefit from the insights derived from the data. In addition, such a strategy can become a barrier to repurposing data for alternative uses or adding new capabilities to the data product in the future, as institutional knowledge about the data product dissipates over time.

In my experience, I’ve seen projects where starting from scratch was the only viable option. This is not only inefficient, but also a missed opportunity to build on existing knowledge and resources. The challenge, then, is to develop data products that are not only useful in the immediate term, but also designed with adaptability and scalability in mind.

A meaningful data product goes beyond serving a single purpose; it is integrated into the fabric of the organization, accessible and understandable to all relevant stakeholders. This requires a fundamental shift toward treating data as a shared asset, fostering cross-functional collaboration, and ensuring data literacy across the organization.

In addition, the lifecycle of a data product should include mechanisms for feedback, review, and continuous improvement. This iterative process ensures that data products evolve in response to changing business needs and the insights gained from their use. It also fosters a culture of learning and innovation, where the knowledge embedded in data products becomes a cumulative asset rather than a transient tool.

To overcome the limitations of traditional data product approaches, organizations need to prioritize:

• Data accessibility: Ensure that data is easily accessible to all who need it, breaking down silos that restrict data flow.
• Cross-functional collaboration: Encouraging teams across the organization to work together, leveraging different perspectives for more comprehensive data solutions.
• Ongoing education: Invest in ongoing data literacy and technical training to empower employees to use data effectively.
• Feedback Loops: Implementing systems to gather feedback on data products to facilitate regular updates and improvements.

Incorporating these principles into the development and management of data products can transform them from isolated tools to integral components of an organization’s data ecosystem. The goal is to create a dynamic environment in which data products contribute to a continuous cycle of learning, adaptation, and growth.

A case study on overcoming data silos

The Essence of Data Literacy Success

As I reflected on the success stories from our data projects, it became clear that these successes are deeply rooted in a persistent effort to close the data literacy gap. It’s not just about technical skills-although those are clearly critical to building robust data solutions. It’s also about how those solutions are communicated and understood by business users. Analytics engineers are the architects of our data infrastructure, but their technical skills are only part of the equation.

The real magic happens when those technical foundations are translated into the language of business-a language that is accessible, relatable, and actionable for all stakeholders. It’s about ensuring that every team member, not just the technically savvy, understands what data is available, what it means, and how it can be used to drive decisions and actions.

This is where the non-technical skills of communication, presentation, collaboration, and critical thinking come into play. They enable the transformation of complex data structures into strategic business insights that are intuitive to understand and easy to apply across teams. They bridge the gap between data creation and data consumption, enabling a seamless flow of understanding across the enterprise. I would argue that communication and presentation skills are actually more important than the specific data points being discussed.

The implications of this dual focus are many. By empowering the entire team to understand and use data effectively, we not only increase the productivity of our data and analytics teams, but also improve the entire organization’s ability to make data-driven decisions with confidence. This comprehensive approach to data literacy mitigates the risk of misinterpretation and ensures that the quality of work is continuously improved.

In essence, building a data literate workforce is like building a bridge. The technical skills lay the foundation, providing a solid and reliable structure. At the same time, the non-technical skills pave the way, ensuring that everyone can confidently cross, understand the landscape, and contribute to the journey to data-driven success.

Conclusion

Engagement with data products is steadily increasing, signaling a notable shift toward self-service analytics as more teams seek to create customized dashboards. This trend underscores not only the ongoing evolution of data products, but also the imperative to evolve with the demands of the consumer base. Bridging the gap between the inherent potential and actualized value of these data assets is critical, as it leads to improved quality, risk mitigation, and accelerated problem resolution – factors that are hidden yet integral to business success. In parallel, Szymon Winnicki’s insights on digital empowerment within the field force complement this narrative and underscore the necessity of data-driven approaches in today’s business landscape.