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InformationArchitectureMaturityModel
Data, Platform, and Process Maturity
Without
Established
Improved
Optimal
Beginning
Evolving the Information Architecture
A foundational step in building information architecture that empowers leadership to quickly respond to changes and execute
strategies is objectively assessing the company’s IA environment to identify opportunities, performance gaps, and areas for
improvement. While each company has specific priorities and unique requirements, a tool such as the Information Architecture
Maturity Model can be used as a yardstick to assess and measure the overall leverage of a company’s IA capability.
When beginning to assess the impact of market shifts and
events on the business, the maturity model provides a way
to identify how and where the IA must evolve to increase
and maintain responsiveness to the business. Key
questions to be answered include:
The maturity model is applicable from very early stage
organizations to mature, established ones. As with most
maturity models, capabilities are ranked with the “optimal”
state representing the ideal mix of capabilities and best
practices a mature organization would possess. At one
end of the spectrum, an early stage company may work
with only a few data sources in a highly manual fashion
that provides information to a select few within the
organization. Conversely, at the other end, a high-
performance, optimized organization would have a broad
range of data integrated in a highly automated environment
that provides users and stakeholders throughout the
enterprise with timely, reliable information.
Key Measurements of the Maturity Model
What new data and information must be
integrated to meet new business challenges?
What types of key performance indicators
are required?
Who will use the information – how often,
how much, in what form?
How is the information accessed and
protected?
What changes are required to support
new policies and processes?
• Support and alignment to business imperatives
• The scope (breadth and depth) of information
• Completeness, accuracy, and periodicity of the data
currently
• Utilization and leverage by business users
• How widely the data is used and relied upon as the
“single version of the truth”
• Level of integration – internal systems, business partners,
third-party logistics, supply chain, marketing, etc.
• Sophistication and reliability of analytics and indicator
models
• Maturity of governance processes and administration
• Levels of automation
• Operational efficiencies, ease of support
• Cost effectiveness