„You can have data without information, but you cannot have information without data“
– DANIEL KEYS MORAN
The existing IT systems in your company are suffering from data integrity issues. The data is often neither accurate nor consistent which leads to not correct information presented to customers, wrong statistics and not reliable reports.
From the discussions at the CIO level, the internal enterprise architecture team was mandated to assess what are the ways of stabilizing data quality related problems leveraging the currently established platforms. You are appointed to drive the needed architecture work and coordinate the activities between different contributors.
Information architecture framework
The critical step to succeed with an assignment is to structure the architecture work into different streams. We use AlvSim Information Architecture Framework (AS-IAF) as a reference to demonstrate on how framework helps to describe and understand data architecture and related information architecture aspects.
The picture below visualizes information architecture pillars and sets the scope for overall analysis.
Architecture framework serves as a reference taxonomy scheme for information architecture concepts and supports consistency and completeness, thus helping to define the focus areas which, in our described scenario, are Data Architecture and Data Quality.
Data architecture can be defined as best practices for establishing methods and standards that ensure the implemented data model and data management platform successfully stores and maintains corporate data assets to support business objectives, while Data quality in information architecture are best practices for defining and implementing a collaborative approach for detecting, assessing, and correcting data defects to ensure fitness for intended uses in business operations, decision making, and planning.
As part of the data architecture analysis, we are evaluating the currently used modelling standards, develop conceptual data models and physical model integrity checks, and evaluate the enterprise data model against defined standards and checks. In addition, data management platforms are assessed by visualizing currently existing and missing data management elements. In terms of data quality, we are assessing currently used data quality improvement activities and suggest the data correction routines for overall data quality improvement.
Enterprise architecture benefits
Successfully performed architecture work outputs executable action plan toward data quality improvements, assess the plan impacts to the current project portfolio, formulate an implementation risks and ensure the implementation is steered in the right direction. The business benefits from higher customer satisfaction, more accurate reporting or even the foundation for more sophisticated customer behavior tracking mechanism like data mining.