What does it do?
Data Quality Monitor is a system-agnostic product that helps firms embed disciplined monitoring of data integrity across any of their systems. It’s an automated tool that lets you check Elite 3E, Intapp, and other SQL databases for issues such as inconsistencies or incompleteness, encouraging prompt resolutions.
Data Quality Monitor comes with a raft of best practice checks available out-of-the-box, and the ability to author your own bespoke scripts. It runs checks on user-defined schedules and can monitor one or more sources; for example, you can verify that clients in Elite 3E are the same as those in your Intapp DealCloud platform.
When would you use it?
Routinely running data diagnostics in this way is obviously a large part of business as usual, driving consistency and accuracy within and across systems. But consider other use cases too:
- Migration – helping identify potential issues before moving legacy data to a new system
- User Acceptance Testing – validating a migration or checking the effect of software changes
- Ad hoc investigations – identifying differences between systems
How does it make a difference?
Law firms have complex systems models and integrations, migrations, customisations, bugs and user errors combine to generate regular instances of ‘bad data’ – entries that are incomplete, inaccurate, inconsistent, duplicated, incorrectly formatted or out of sync.
Getting early sight of the issue and being able to fix it before it becomes a problem, this has huge value simply because there is such a large hidden cost associated with bad data.
If accounts are out of balance or data is missing, that takes time for someone to come in and sort, and everyone and everything else could be on hold while it gets resolved. Plus, without regular monitoring, poor working practices can go unnoticed and integrations can be allowed to fail, both scenarios often resulting in expensive remedial action.
And as if that wasn’t enough, there’s a heightened risk of compromised decision-making, with misplaced reliance on what is actually incorrect, summarised data.
Think about using it if:
- You need to tackle long-standing data anomalies and avoid any reoccurrence, without the need for extensive dedicated resource
- You want to eliminate the costs, risks and delays associated with a reactive ‘find and fix’ approach to data issues
- You are keen to get your firm ‘data fit’ as you plan for a major project, such as Cloud migration or merger
- You would like to build your own checks to augment data quality monitoring exactly where and how you need it