Pnw Data analytics

Pacific Northwest

Data Analytics
Leadership Board

The Pacific Northwest Data Analytics Leadership Board is comprised of a select group of leaders in analytics from the largest and most innovative companies in Seattle. The mission of the group is to spread leading practices in data management and analytics enabling companies to drive better decision-making with data.

Analytics Adoption Best Practice Themes

Throughout 2021 and 2022 we completed case studies with input from our board members on the topic of analytics adoption. From these case studies six main themes surfaced. An article was written show casing each theme. The themes are as follows:

Engage Stakeholders and aligning analytics to business value

Gain executive / leader commitment to drive usage of data and insights for decision-making within their teams. Engage executives / leaders in forums to share progress and business impacts and make/direct investments in comprehensive data capabilities.

Create a Data-centric Culture

Enable a culture of analytics through data storytelling, literacy training, and tools. Create targeted communities (data science, data engineering, etc.) to engage a broad reach of teams within the organization.

Establishing team composition and organizational structure

Implement a structure that allows for data analytic resources to be integrated with their business counterparts during use case concept development, design, experimentation, and initial development yet have accountability back to a centralized core team that both manages data preparation to reduce duplication of efforts and standardizes solutions for cross-organization scalability.

Driving stakeholder and analytic team engagement

Drive collaboration between stakeholders/users and data analytic teams to enable understanding and expectations of stakeholder personas and their use cases. This early engagement develops the foundation for trust and understanding that encourages adoption of insights after implementation.

Curating data and making it accessible for self-service

Curate data so it can be accessed and used consistently across organizations with transparency about how it has been transformed. Surface data along with supporting resources (e.g., data catalog, data lineage, starter kits, etc.) such that it lowers the data and tool-related barriers and encourages self-service use.

Treating data as a product

Apply best practices from product development philosophies and principles, from defining product requirements (including identification of user scenarios and expected business outcomes) to go-to-market strategy.

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