Analyzing Data Model
When conducting a data model analysis, create an Entity Relationship Diagram (ERD) and document any gaps you identify to highlight potential risks for the project.
C3 Agentic AI Platform offers a dynamic ERD to make it easier to run data gap analyses. Find out more about this tool in Manipulate ERD Views with Object Model.
Performing a gap analysis between the available data and the desired data model involves systematically assessing the current data state and comparing it to the target model. This process helps identify discrepancies, missing elements, and areas requiring adjustments to align with the data model. The following list is a step-by-step guide to conducting a data gap analysis:
Understand your data model — Start by understanding the target data model, including its schema, data types, relationships, and any specific constraints or requirements.
Inventory current data — Catalog all the data sources and datasets available, including databases, files, APIs, and other data stores.
Document data sources in Canonical workbook — In the C3 AI context, a Canonical workbook typically refers to a data model workbook that defines the structure, format, and relationships of Canonical Types within the system. These workbooks are essential for establishing a standard, consistent way of representing data across the C3 Agentic AI Platform, ensuring interoperability between different systems, data sources, and applications.
Using the Canonical workbook, create detailed documentation for each data source, including data structure, schema, types, and any known data quality issues.
Identify data mapping in Canonical workbook — Determine how fields align between your data and the target model.
Assess data quality — Conduct assessments to check data completeness, accuracy, and consistency. Identify any quality issues that need resolution.
Analyze data gaps — Compare your current data to the target model to identify gaps such as missing data, type mismatches, or inconsistent values.
Prioritize gaps — Not all gaps are critical. Prioritize them based on their impact on the data model and business objectives, addressing the most significant issues first.
Propose solutions — For each gap, propose solutions such as data transformation, enrichment, acquisition, or even adjustments to the data model.
Estimate effort and resources — Estimate the time and resources needed to address each gap, considering tools, development time, and team capacity.
Create an implementation plan — Develop a plan that outlines the tasks, timelines, and responsibilities for bridging the identified gaps.
Run the plan — Begin the implementation, which may involve creating new Canonical Types, performing Extract, Transform, Load (ETL) processes, or updating the data model.
Test and validate — After changes are made, test the data to ensure the gaps have been resolved and the data aligns with the target model.
Monitor and maintain — Establish monitoring procedures to ensure ongoing data alignment. Set up data quality checks and alerts to prevent future gaps.
Document and report — Maintain thorough documentation of the gap analysis process, changes made, and the status of data alignment. Provide regular updates to stakeholders.
Iterate and improve — Continuously evaluate and refine the data alignment process. As your data evolves, repeat the gap analysis to ensure consistency with changing business requirements.
Maintaining alignment between your data and the target model is an ongoing process that requires regular review and adjustment. By systematically performing gap analysis and implementing these best practices, you ensure that your data remains reliable and supports effective decision-making in the long term.