Executive Summary
Your historical reporting is broken and the data is a mess after merging two Salesforce instances.
You can't get a clear picture of the business. The board sees conflicting numbers, sales forecasts are unreliable, and customer history is split. This undermines confidence and makes it harder to get value from the acquisition.
The fix isn't to file more data engineering tickets. It's to step back and map the actual business processes to a single data model before trying to merge the two systems.
How the problem usually shows up
The problem often surfaces in a quiet moment. It might be the Monday board meeting, a few weeks after the acquisition has closed. The CFO asks for a simple, combined chart of historical customer acquisition cost. Your Head of Data has a difficult moment. The numbers pulled from the two old Salesforce systems just don't match up.
The data migration might be technically complete and the servers are running, but you've essentially just digitised two different ways of working. You've taken two company's philosophies, captured in years of custom fields and validation rules, and pushed them into the same place. The result is that you can't report reliably, and it gets harder to stand by the numbers.
Why this is an architectural problem, not a data one
This isn't just a data mapping task. It's usually a clash of two different ways of running a business. In my experience, this happens quite often after an acquisition. The deal is done, and the technical team is under pressure to merge the systems quickly. What's often missed is the time needed to translate how each business actually operated.
For example, one company's definition of a 'Qualified Lead' was completely different from the other's. Their sales stages, discount approvals, and how they grouped customers are all built into their CRM setups. Trying to force them together without a translation layer is like trying to merge two different instruction manuals. The result is usually broken reports, lost history, and a real challenge for your CRM Data Integrity.
You haven't just created a technical headache. You've cemented the existing Data Silos, just now in one expensive system. The tool isn't at fault. The issue is often that a clear CTO Data Strategy wasn't part of the merger plan.
A better approach: model the business, then the data
A common reaction is to try and solve this within the CRM itself. In my experience, that doesn't work. The way forward is to step back and see it for the architectural problem it is. You need to create a way to translate between the two before you can make sense of the history.
The way I approach this is methodical. Reporting should be treated like a reliable production process, not just an ad-hoc IT request.
The non-technical part of the work
To be clear, this isn't a quick or easy process. It means telling the business that you need to slow down now in order to speed up later. You will have to explain to two different sales leaders that their way of doing things needs to adapt to a new, shared standard.
This is as much about negotiation as it is about data architecture. The real work is getting everyone to agree on simple definitions: what is a 'customer', what does 'churn' mean, how do we measure 'pipeline'. The code is the easy part. You should expect some resistance, because you're changing how people work, not just updating a few fields in a database.
The outcome of getting this right
Once this foundational work is done, the benefits tend to show up quickly and stick around. The weekly arguments about whose numbers are correct tend to stop. The board can see a single, reliable view of how the combined business is doing. Your sales operations teams can start working from the same set of rules.
As the CTO, your role shifts from managing a broken system to providing real clarity for the business. You haven't just fixed a report. You've put in a foundation that the business can grow on, and helped make sure the value of the acquisition isn't lost in a mess of conflicting spreadsheets.