What's your data architecture review process?

1 Identify your stakeholders

The first step of the data architecture review process is to identify the key stakeholders who are involved or affected by your data architecture. These may include business users, data analysts, data engineers, data scientists, data governance teams, and IT managers. You should communicate with them to understand their needs, expectations, and challenges regarding the data architecture. You should also collect feedback and suggestions from them on how to improve the data architecture.

2 Define your criteria

The next step is to define the criteria that you will use to evaluate your data architecture. These criteria should adhere to the standards and best practices of data architecture, as well as the specific requirements and objectives of your organization. Common criteria include data quality, security, accessibility, scalability, integration, and usability. Data quality encompasses accuracy, consistency, completeness, and timeliness. Data security focuses on protection from unauthorized access, modification, or loss. Data accessibility examines the ease with which data consumers can find, access, and use the data they need. Data scalability looks at how well your data architecture can handle increasing data volume, variety, and velocity. Data integration evaluates how well your data sources are connected and harmonized across your data architecture. Finally, data usability examines how well your data is structured, documented, and standardized to support data analysis and decision making.

3 Conduct your assessment

The third step is to conduct your assessment of your data architecture based on the criteria you defined. To measure and compare your data architecture against the expected outcomes and benchmarks, a combination of quantitative and qualitative methods should be used. Data audits can be used to check the quality, security, and compliance of your data. Data surveys can collect feedback and ratings from data consumers. Data metrics can track and analyze the performance, availability, and usage of your data architecture components. Lastly, data interviews can help understand stakeholders' pain points, needs, and expectations regarding the data architecture.

4 Analyze your findings

The fourth step is to analyze your findings from the assessment and identify the strengths and weaknesses of your data architecture. Patterns, trends, gaps, and opportunities should be identified in order to affect business outcomes and user experience. Prioritizing issues and areas that need improvement based on their impact and urgency is essential. Examples of findings include data quality issues such as errors, duplicates, missing values, or outdated information; data security risks like inadequate encryption, authentication, or backup policies; data accessibility challenges such as poor cataloging, metadata management, or data lineage documentation; scalability bottlenecks due to insufficient storage, processing, or networking capacity; integration gaps from inconsistent data formats, schemas, or standards; and usability problems from lack of data modeling, governance, or quality rules.

5 Recommend your actions

In the fifth step, you should recommend actions to address the findings and improve your data architecture. These solutions should be specific, feasible, and measurable, and should be aligned with your business goals and priorities. Examples of such actions include data quality improvement by implementing rules for accuracy, consistency, completeness, and timeliness; data security enhancement by applying encryption, authentication, and backup policies; data accessibility improvement by improving cataloging, metadata management, and data lineage documentation; data scalability enhancement by upgrading storage, processing, or networking capacity; data integration improvement by standardizing formats, schemas, and standards to harmonize data sources; and data usability improvement by adopting modeling, governance, and quality rules to structure and document data for analysis and decision making. Estimate the costs, benefits, and risks of each solution before proceeding.

6 Implement and monitor your actions

The final step is to implement and monitor your actions to improve your data architecture. You should plan and execute your actions according to the agreed scope, budget, and timeline. You should also monitor and evaluate the results and impacts of your actions using data metrics, surveys, audits, and interviews. You should communicate and report your progress and outcomes to your data stakeholders and solicit their feedback and suggestions. You should also review and update your data architecture regularly to ensure that it meets your changing business needs and best practices.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?