During past years we have been helping associations determine
how to use data to make decisions, we have identified 8 primary
goals for an Analytics Strategy & Roadmap process:
- Align analytics objectives with the organizational strategic
- Recognize the importance of data as a key organizational asset
that requires oversight and governance to ensure quality.
- Assess and document the current state of Data, Technology,
Processes and Culture.
- Quantify the direct and indirect costs of the current situation
(data is not clean, accessible, understood and consistently used
- Identify achievable desired outcomes and understand their
- Prioritize these outcomes according to business impact,
technical complexity, and organizational considerations.
- Educate executives and staff about what is possible and what to
expect from an analytics initiative.
- Establish a high level plan for implementing the analytics
strategy, including scope, cost and schedule.
Helping our clients understand their current state,
while comparing it to a desired and realistic future state, has
proven to be a very effective method for building a data
& analytics roadmap. To jump-start this process,
we created a proven framework for assessing an organization’s
data & analytics maturity. Developing an understanding of where
your company is in each of six key areas is a critical first step
in mapping your way forward.
- Strategy: A disciplined process of
developing and managing long-term business objectives, including
the actions that help achieve those objectives.
- Analytics: Effective consumption of
enterprise and ad hoc information with tools that are
capability-aligned to analytic use cases.
- Data: An organization’s ability to
capture, transform, and enrich data assets, including documentation
and maintenance of standardized definitions for master data, rules,
entities, and references.
- Governance: Ensuring the ongoing
relevance, flexibility, and accuracy of data, and developing
application and analytic solutions that fit the company’s
unique needs, culture, and use cases.
- Skills: An organization’s collective
expertise around supporting the full lifecycle of data &
analytics delivery, including data architecture, data integration,
project management, business analysis, data science, user
experience, and training.
- Architecture: Alignment of systems and
infrastructure to support business applications in a secure,
scalable, and flexible manner.
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