Use case
Agree definitions first, then assign ownership, then validate at entry. Cleansing before those three is a temporary fix to a permanent process, and the data degrades again within months.
The visible symptom is rarely the cause. These are the underlying reasons we find most often.
One agreed definition per measure, an owner for each, validation preventing the common faults, and monitoring that surfaces degradation before it reaches a report.
Context
Every decision, report and automation downstream inherits the quality of the data underneath. Poor data does not stay a data problem; it becomes a trust problem, and then a decision problem.
How we solve it
Eight to sixteen weeks, of which the definition work is often the longest part.
What counts as an active customer, when an order is complete, which date reporting uses. This is a business conversation, and most quality disputes dissolve once it is held.
A business owner per field or record type, not a database administrator. Unowned data degrades because nobody is responsible for the judgement calls that keep it consistent.
Required formats, constrained lists, duplicate checks at creation. Prevention costs a fraction of correction and it does not have to be repeated.
Completeness, duplication and out-of-range values, checked on a schedule with someone notified. Silent degradation is what turns a working dashboard into one nobody trusts.
Capabilities
How it works
Systems connected by people versus by integration
Before: three systems, each bridged by a person moving data across by hand. After: the same three connected directly through an integration layer, with one declared source of truth per record.
Tools enforce rules; they do not decide what the rules should be. Applied before definitions are agreed, they encode the existing disagreement and make it harder to unpick later.
Usually from integrations that create rather than match, and from entry that allows a near-identical record without warning. Both are preventable at the point of creation, which is where the fix belongs.
Agree definitions first, then assign ownership, then validate at entry. Cleansing before those three is a temporary fix to a permanent process, and the data degrades again within months.
Every decision, report and automation downstream inherits the quality of the data underneath. Poor data does not stay a data problem; it becomes a trust problem, and then a decision problem.
Two reports of the same measure disagree and both are defended; Duplicate customer records exist and nobody knows which is current; Analysts spend most of their time reconciling rather than analysing; and Automations fail on records that do not match the expected shape
Key terms are defined differently by different teams; No one is accountable for the accuracy of specific fields; Data is entered free-form where a constrained list would do; and Integrations create records without checking whether one already exists
One agreed definition per measure, an owner for each, validation preventing the common faults, and monitoring that surfaces degradation before it reaches a report.
Eight to sixteen weeks, of which the definition work is often the longest part.
Connected
If the before state above reads like your operation, the next step is establishing which part of it is actually costing you. Describe it and we will tell you where improve data quality would and would not help.
