Data connections
Pulling data reliably out of ERP, CRM, and SaaS platforms — through native connectors, APIs, or a scheduled pipeline — so reports are working from current data instead of a manual export someone forgot to refresh.
Power BI is Microsoft's business intelligence platform for pulling data out of the systems you already run — ERP, CRM, eCommerce, spreadsheets, databases — and turning it into reports and dashboards people can actually explore, instead of static exports that go stale the moment they're generated.
The visual layer — the charts, filters, and dashboards — is the part most people see, but it's the smallest part of what makes a Power BI deployment work well. The real effort sits underneath it: getting data out of source systems reliably, modeling it into a structure that supports the questions the business is actually asking, and keeping that pipeline running as those systems change.
Done well, it replaces the version of reporting where three people have three different numbers because they each built their own spreadsheet, with a single model everyone is looking at.
Every Power BI engagement covers the same core ground, weighted differently depending on what's already in place.
Pulling data reliably out of ERP, CRM, and SaaS platforms — through native connectors, APIs, or a scheduled pipeline — so reports are working from current data instead of a manual export someone forgot to refresh.
Structuring the relationships between tables — fact and dimension tables, DAX measures, handling duplicates and mismatched formats — so the numbers hold up when someone drills into them, not just when they look at the top-line chart.
Building reports around the decisions a team actually needs to make, with the right level of drill-down — not a dashboard that displays every available field because it was easy to add.
Row-level security, workspace structure, and refresh scheduling set up correctly from the start, so the right people see the right data without becoming a maintenance burden later.
Most organizations don't have a shortage of data — they have a shortage of agreement on what the data says. When every team pulls its own export and builds its own spreadsheet, small differences in filters, date ranges, or definitions turn into meetings spent reconciling numbers instead of acting on them.
A properly modeled Power BI environment removes that argument by design: there's one source of truth for a given metric, and everyone looking at a report is looking at the same underlying model, refreshed on a schedule rather than whenever someone remembers to update it.
It also scales better than ad hoc reporting. Adding a new data source or a new business question means extending an existing model, not building another one-off spreadsheet that nobody else can maintain.
A lot of Power BI work fails quietly at the data layer, not the dashboard layer — a connector that breaks silently, a model that doesn't account for how a field is actually used in the source system, a refresh schedule that doesn't match how often the business needs current data. That's the layer we spend most of our time on.
Because our core practice is ERP, CRM, and iPaaS integration, we're not approaching Power BI as a standalone visualization tool — we're building the reporting layer on top of the same systems we already integrate, which means the data feeding your dashboards is handled by people who understand where it actually comes from.
Power BI is Microsoft's business intelligence platform for consolidating data from multiple sources into interactive reports and dashboards. It connects to databases, ERPs, CRMs, spreadsheets, and cloud services, and lets teams explore data visually rather than through static exports.
Three main pieces: getting data reliably out of your source systems, modeling it into a structure that reports can actually be built on, and designing dashboards that answer the questions your teams are asking rather than just displaying every available field.
In most cases, yes, either through a native connector, an API, or a scheduled data pipeline depending on the platform and how much transformation the data needs before it's report-ready. Systems like D365, Business Central, Salesforce, and NetSuite all have established connection paths into Power BI.
Native reporting in an ERP or CRM is usually scoped to that one system. Power BI's value shows up when you need to combine data across multiple systems — sales from a CRM against fulfillment from an ERP, for example — in a single report, which native tools typically aren't built to do.
Not necessarily perfect data, but you do need to know where the inconsistencies are. Part of the data modeling work is handling the gaps, duplicates, and mismatched formats between systems so the finished reports hold up under scrutiny.
Both. Because our core practice is ERP, CRM, and iPaaS integration, we typically handle the full path — getting data out of your source systems, modeling it correctly, and building the reporting layer on top — rather than just the dashboard design on its own.
Tell us what systems you need to report on — we'll tell you what that actually looks like to build.