Peter Sprague, VP of Product Marketing, Pyramid Analytics

The Last Mile Implementing Your Data Strategy

Where others see trees, CD&AOs need to see the forest.

• Lead Data Strategy

• Organizational Change Agents

• Open Government Initiatives

Government CDO’s: Many Expectations

The last mile problem…

Legacy systems make this problem much worse.

The organization’s need for:

• Governance

• Compliance

• Transparency

Balanced with users’ needs:

• Agility

• Accessibility

• Self-Direction

All require an analytics strategy.

Build your analytics strategy

around a complete set of shared

enterprise assets. Analytics Repository

An Analytics Repository is Critical to Manage Large Deployments

• Encourage collaboration

• Encourage re-use • Leverage shared organizational logic

• Ensure transparency • Promotes both increased discovery as well as increased trust

Provide for Curated Content

• Both official and self-service content in the same context

• Allow users to understand the source of content

• Seed content for self-service systems

Example: Analytics Repository

Concordia University

Deliver analytics best

practices across the

organization as a service. Analytics-as-a-Service

Analytics-as-a-Service Can Be Transformational

• Accelerate the organization’s analytic maturity

• Make the systems scalable, reliable

• Have your BI resources, IT staff, and business users focused on what they do best

Deploy BI Solution Centers

• Centralize analytics expertise

• Decentralize content creation

• Can also improve and influence the analytics maturity of partner organizations

“Not your father’s BI Center of Excellence.”

Example: Analytics as a Service

Department of Veterans Affairs

ML is another type of

organizational logic that

needs to be integrated.

Operationalized

Machine Learning

Operationalized ML is Critical

• To be useful most algorithms need to be applied at the grain

• End users need to have access to ML algorithms

• ML algorithms need to be a first-class citizen in the analytics repository

Use ML to Realize the Value of Big Data

• ML is the secret to finding the

value in data swamps

• ML and data lake access must be

available to the person that

understands the business

problem—not just those who

understand the technology

• Use in-place analytics to avoid

the “elephant in the room”

Example: Operationalize Machine Learning

Local Paramedics Organization

Summary

Create a separate analytics strategy with:

• A well-managed repository

• Analytics-as-a-Service

• Operationalized ML

Contact Me

Exhibitor’s Table

peter@pyramidanalytics.com

@petesprague

Peter Sprague

VP Product Marketing Pyramid Analytics