Make your data move between systems without anyone copying it by hand.
I help growing businesses whose tools do not talk to each other connect them, so data reaches the right place in the right form and failures are seen at once.
Where the problem shows up
When systems do not connect, most businesses move the data by hand. Someone exports a file, adjusts a few columns and uploads the result to the next tool. For a single report this is a sensible shortcut, and because it works, it becomes part of the weekly routine.
Every manual step is also a place where meaning can change. A column is renamed, a date format is misread, a duplicate is created, and a record appears in one system but not in another. The errors are small and are found late, usually when two numbers that should match do not. Meanwhile the people who move the data become the integration layer of the business, so deals wait for updates, finance does not trust operational figures, and customer handoffs depend on memory. What happens when the person who knows the export routine is away? And how many decisions are made on data that was correct when exported and out of date when read?
The better principle is to decide which system owns each important record, agree what the other systems may do with it, and monitor every transfer. That is the work I do.
What I have changed for others
Client reporting: ad, call, profile and CRM data in one funnel
Each client's marketing data lived in Google Ads, call tracking, Google Business Profile, analytics and the CRM, and each source was reported on its own. I built a Looker Studio system that connects these sources so acquisition metrics sit beside their downstream outcomes. Each client now sees one funnel from visibility to conversion, with lead sources, opportunity stages and lost reasons. Read the case study.
60+ websites: one shared schema
Performance data from more than 60 websites was defined and stored differently on each one. I designed a shared analytics schema and centralised the data in BigQuery, so that any property can be compared with any other. Read the case study.
26 ledgers: one set of rules for categorisation and intercompany balances
Twenty-six company ledgers categorised transactions inconsistently and could not explain their intercompany balances. I designed standardised categorisation rules and a formal intercompany procedure that all 26 ledgers follow. Balances between entities can now be compared on the same terms. Read the case study.
What changes for your business
Data arrives where it is needed in a consistent form, and nobody has to remember an export routine. Each important record has one authoritative source, and a failed transfer raises an alert and has a recovery path. The work can include mapping systems and ownership, data contracts for the key records, integrations with monitoring and retries, reporting that reads from a single source of truth, and documentation of what moves and why.
Where this leads next
Liquid data is what lets automation work without fragile copy-and-paste steps and what gives predictive analytics history it can trust. The mechanics of integration are described on the IPaaS services page, and if your systems are already connected, we can begin with the next layer.