• Generate meaningful business insights.
However, many firms struggle with fragmented or inconsistent data due to:
• Multiple platforms housing overlapping but inconsistent client records.
• Variations in how names, job titles, and company affiliations are recorded.
• Duplicate or outdated records from various data sources.
Unreconciled data leads to inefficiencies, miscommunication, and unreliable insights. As firms integrate more technology, having a standardized data structure is key.
“We found that something as simple as having different spellings and capitalization e.g. Munich, MUNICH, München caused all sorts of problems and in one instance it appeared as if we had three different German clients when in fact, they were the same client.”
Natasha Tucker, Director, Business Development Operations at Bennett Jones SLP.
What is Data Normalization and Reconciliation?
These two processes ensure data is accurate, consistent, and usable across systems:
• Data normalization standardizes data formatting (e.g., names, contact details, industries) to enable consistency across platforms.
• Data reconciliation identifies and resolves discrepancies between data sources, ensuring all systems reflect the most up-to-date information.
Since legal technology stacks often include CRM, financial, and business intelligence tools, data normalization and reconciliation are critical to maintaining accuracy across all platforms.
The Risks of Poor Data in the Age of AI
AI-powered tools only perform as well as the data they rely on. Inconsistent, outdated, or incomplete data can lead to:
• Inaccurate AI insights – If training data is flawed, AI models produce misleading predictions.
• Redundant or conflicting client interactions – Multiple attorneys may unknowingly reach out to the same client.
• Ineffective marketing campaigns – Poor segmentation results in irrelevant outreach.
• Unreliable reporting – Business intelligence dashboards reflect inaccurate KPIs due to bad data.
For AI-driven efficiency to be meaningful, firms must first ensure their data is clean and well-structured.
“We have seen through testing our own AI application how using content that is tagged with well-defined consistent terms and clearly articulated relationships between these terms improve the responses provided and reduce the risk of hallucination and fabrication, where the AI either makes up associations or more interestingly creates connections that are not necessarily true. AI tools are performing vast at scale sampling of content to provide answers and in that process are refining what they are using to respond. By providing context to your data improves the inferences it makes and allows the AI to focus on the most relevant content for your query”.
Grant Newton, Lead Consultant, Atlas by ClearPeople.
Strategies for Improving Data Hygiene
Key Processes for Data Normalization and Reconciliation
To enhance data quality, firms should implement:
1. Routine Data Audits – Regularly review datasets to eliminate duplicates and standardize formats.
2. Data Governance Policies – Establish firm-wide rules for how client and matter data should be recorded.
3. Standardized Data Entry – Train staff to maintain consistent formatting across systems.
4. Automated Data Validation – Use technology to flag inconsistencies before they create larger issues.
Technology Considerations
While legal tech stacks vary, firms should seek tools that:
• Support data normalization across multiple systems for consistency.
• Provide duplicate detection and reconciliation to improve accuracy.
• Offer real-time validation to prevent errors at the point of entry.
• Enable data enrichment to keep records up to date.
Ensuring that all platforms contribute to a unified, reliable dataset will improve system performance and business intelligence.
“The flipside of the risk that an AI application does not provide a relevant response without context is that they can be used to actually help improve data quality and normalise the data where you can outline the logic it needs to apply. Therefore, firms that have defined taxonomies or indeed an ontology that describes the relationships between terms as well, can look to use this data to improve the normalisation and consistency of their data”.
Grant Newton, Lead Consultant, Atlas by ClearPeople.
Maintaining High-Quality Data
Data normalization and reconciliation require ongoing effort. To sustain data integrity, firms should:
• Designate data stewards responsible for quality control.
• Conduct regular system-wide audits to catch inconsistencies early.
• Implement continuous training on data entry best practices.
Firms that embed data hygiene into their broader technology strategy will see long-term efficiency gains and more reliable AI-driven insights.
Conclusion
As AI and automation become standard in legal technology, data quality remains the foundation for success. Without clean, reconciled, and normalized data, firms risk inefficiencies and unreliable AI insights.
To optimize legal technology investments, firms should prioritize data hygiene through regular audits, standardized data practices, and automation where appropriate. By doing so, they can maximize the potential of AI and data-driven decision-making—ensuring that technology works for them, not against them.