Opportunity Data Harmonisation

Challenges Opportunity Data Harmonisation addresses

Done by hand, pipeline management means gathering opportunity records and target model, working through 3 separate passes over the same material, then producing is the set comparable?, harmonisation note and as found → harmonised. None of it is difficult and all of it is exacting, which is the combination people are worst at holding. The errors that matter are the ones a tired reader does not notice, and they surface later — in a reconciliation, or in somebody’s reply. It waits until someone remembers it, which is usually the point at which it has become urgent. As volume grows the work does not get harder, only longer, and the first thing to go is the checking.

Opportunity Data Harmonisation runs that same sequence end to end and returns the result as structured artefacts. What it cannot settle it hands over rather than guesses at, and your correction is kept: it asks “Are these mappings right?” after every run, and those answers become the set it is measured against. Nothing that moves money, alters a contract or reaches a customer executes without human approval, and every action is written to an audit log. The gain is in the volume that no longer has to be read, not in removing the judgement.

How it works

Step 1: Reading the records

First of 3. It works from opportunity records and target model and feeds the step after it.

Key Tasks:

• Locating the material: It works from opportunity records and target model, so nothing has to be forwarded, re-keyed or renamed first.
• Handling the format it arrives in: Scanned pages, native documents, spreadsheets and message bodies are all read the same way, including layouts where the relevant figure sits inside a table rather than a labelled field.
• Pulling the fields that matter: Only the fields the rest of the run needs are extracted. What cannot be read confidently is recorded as unread rather than filled in with a best guess.

Outcome:

• Fields extracted: The fields are available to the steps that follow, with anything unreadable listed rather than silently defaulted — which is what stops a bad extraction becoming a confident wrong answer three steps later.

Step 2: Harmonising the records

Step 2 of 3. It takes what step 1 produced and hands its result to step 3.

Key Tasks:

• Staging the change: What would be written to the source system is prepared in full and shown before anything is committed.
• Writing only what cleared: Records still holding an exception are excluded from the write rather than pushed through with a note attached.
• Leaving an audit trail: Every change records what it was, what it replaced and which run produced it.

Outcome:

• Approved: The change is written and the trail recorded.
• Held: Nothing is written. The staged change stays available for review, so approving it later does not mean re-running the work.

Step 3: Writing the harmonisation note

Last of 3. It takes what step 2 produced and produces is the set comparable? and harmonisation note.

Key Tasks:

• Writing from the run, not from a template: The text is built from what this run actually found, so two pipeline management outputs differ where the underlying records differ.
• Leading with what needs a decision: The exceptions come first and the routine detail follows, because the reader is deciding rather than reading.
• Staying inside the evidence: Nothing appears in the text that is not supported by a record the run examined. Gaps are stated as gaps.

Outcome:

• Artefact ready: A finished artefact, traceable line by line to the records behind it, ready for a person to accept or correct.

Step 4: Your review, and what it changes

The run ends with a person, not with a result being filed.

Key Tasks:

• Asking a specific question: It asks “Are these mappings right?” rather than for a rating. A question about this run is answerable; a score out of five is not.
• Keeping the correction: What you change is recorded against the case that produced it, so the disagreement is retrievable rather than absorbed.
• Building the evaluation set: Those cases become what the agent is measured on. It is scored against your judgement rather than against a general benchmark.

Outcome:

• A measured agent, not an assumed one: The cases Opportunity Data Harmonisation handles well and the cases it does not are both visible, and the second list is the one that decides what changes. Nothing is retrained silently on the back of a single correction.

Why use Opportunity Data Harmonisation?

• Checks are evidenced, not asserted: Each check records what was expected and what was found. A failure can be understood — and argued with — without re-running anything.
• Field mapping you can audit: Every incoming field is shown with what it was mapped to and how confidently. Low-confidence mappings are held rather than applied, which is where silent data corruption otherwise starts.
• Takes documents as they arrive: Scanned pages, native files and awkward layouts are read as they are. Nothing has to be renamed, re-keyed or converted into a template before a run.
• Corrected by the people using it: After each run it asks “Are these mappings right?”. Those answers become the evaluation set, which means it is measured against your judgement rather than ours.
• Reads and reports, does not act: It returns a result for review rather than writing changes back on its own. Anything that moves money, alters a contract or reaches a customer needs human approval first.