¿Qué puede hacer para desarrollar la alfabetización y la cultura de datos entre las partes interesadas?

Here its important to understand which data points are crucial for the stakeholders and their primary needs in terms of data, these should be implemented first. The advantage here is since those data points are important it will be easier to drive usage among stakeholders. Rest of the journey will eventually fall into place and be a smoother journey

Assessments should separately target major user groups like business analysts, frontline staff, and senior leaders. The data needs, and gaps likely differ across roles. Comparing results is crucial in designing training that addresses role-specific literacies. Measuring at the start provides a baseline to track program impact over time.

Tying data literacy goals back to business priorities and governance objectives is essential. For example: If poor data quality is an issue, target skills to assess and improve quality. If critical decisions aren't data-driven, focus on analytic and interpretation skills. If data isn't widely used, build data access and visualization skills. If ethics are a concern, emphasize security, privacy, and bias mitigation. The outcomes should align with the role-specific gaps uncovered in assessments. Priorities likely differ for data consumers, data analysts, IT staff, and senior leaders.

Some examples of developing learning pathways: For analysts, you can create a blended curriculum with online courses on fundamentals like SQL, Excel, and Python combined with in-person workshops on query design and visualization best practices. For executives, you can customize one-on-one coaching focused on high-impact dashboards, communicating insights, and aligning KPIs to strategy. For a call center team, you can publish a weekly data-driven tips newsletter with snippets of insights tailored to their challenges and create leaderboards and badges to motivate the usage of self-service reporting. Across roles, you can build a data literacy portal with resources like videos, templates, self-assessments, and live webinars.

Identifying and enabling internal influencers across multiple levels can significantly boost adoption. Senior leaders can share success stories and reinforce data-driven culture from the top down, lend credibility, and signal that data literacy is a strategic priority. Developing data ambassadors passionate about mentoring peers is also powerful. These superusers can lead hands-on workshops, host Q&A forums to share their experience, create user guides and tutorials, and provide 1:1 coaching. Partnerships with internal analytics communities via collaborating on competitions, hackathons, or skills-building workshops are another excellent way to gain traction.

A key aspect is provisioning the technology, systems, and tools that remove barriers and facilitate hands-on data interaction. Providing sandbox environments where people can explore and build skills without risk empowers experiential learning. Equally important is establishing clear data governance guardrails that provide direction without overly constraining. Creating flexible but firm guidelines gives people the confidence to use data appropriately. Incentives and cultural reinforcement are also pivotal. Leadership should actively recognize and reward data-driven decisions and breakthroughs. Tie data literacy assessments and training completion to performance management and mobility.

I recommend gathering ongoing qualitative feedback through post-training surveys, focus groups, interviews, and user testing. Asking stakeholders what knowledge and skills resonated, what was lacking, and where they still have challenges provides actionable input to enhance content and formats. Analyzing usage patterns for online resources, community forums, and self-service support can spotlight what program elements provide the most value. Finally, sharing results, success stories, and case studies across the organization through town halls and internal social channels to sustain engagement by celebrating wins. Programs should evolve on at least an annual basis based on user feedback and technology changes to stay relevant.

Stakeholders should expect jurisdictions to exercise their right to data sovereignty. As they may have the authority and right to request, govern, and control the data generated within its borders. For example, data sharing among state, local, and tribal communities to access real-time patient-level data is critical to effectively conduct case investigation, contact tracing to interrupt infectious disease transmission, and assure testing and treatment, as seen during the COVID pandemic and now to address the rise of Sexually Transmitted Infections (STIs)

While formal training is necessary, engaging and rewarding data literacy is essential to drive lasting behavior change. Interactive elements like competitions, gamification, humor, and storytelling prevent dry one-way training. Tying data skills acquisition to career advancement and mobility also promotes internalization, especially when reinforced during recruitment and onboarding of new hires. Lastly, sustainable culture change requires patience. Leadership commitment is critical, with executives completing training first to signal priority. But shifting mindsets happen gradually. Small steps like increased data discussion should be celebrated along the path. The goal is lifelong data passion, not just imparting skills.