What this work changes for the business
Data engineering is a means, not an end. This page skips the architecture and focuses on what changes when the numbers become dependable: how you report, how you decide, and how much time your team stops losing to Excel. Meles works from the business process inward — finance, procurement, inventory and logistics first, tables second — because a report is only right when it matches how the process actually runs in Dynamics 365.
Reporting you can act on without double-checking
Reports built on one governed data model, reconciled against the ERP and the ledger. When two dashboards show revenue, they show the same revenue — because they use the same definition, not two different exports.
Less manual work at month-end
The recurring exports, lookups and copy-paste steps that feed your reporting are replaced by automated pipelines with monitoring. The work happens overnight; your team reviews results instead of producing them.
Operational visibility, daily instead of monthly
Orders, stock, margin, open positions — refreshed on a schedule you choose, not when someone has time to rebuild a file. Problems show up while they're still cheap to fix.
Finance and ERP data that's clear, not just present
Dynamics 365 holds the data, but its raw tables aren't reporting-ready. The data model translates ERP structures into business language — so "open orders" means the same thing to finance, operations and the reports.
Better decision support
Once the foundation exists, questions get cheaper to answer. New reports take days rather than projects, and a scoped data agent can handle the recurring "quick question" traffic that otherwise lands on your analysts.
Lower risk in data and AI initiatives
AI on top of unreliable data produces confident nonsense. AI on top of a governed, documented model — with read-only, scoped access — produces answers you can trace. The foundation is what makes AI initiatives safe to try.
A typical engagement, sketched honestly
Meles doesn't publish client case studies. The example below is illustrative — a composite of the kind of situation this work addresses — and it's labelled that way on purpose.
A trading company runs Dynamics 365 Finance & Operations. Month-end reporting takes four days: exports from the ERP, three linked Excel files, and a controller who knows which numbers to correct by hand. Two Power BI reports exist, built by different people, and they disagree on margin.
The work: analyse the sources and the manual steps, design one data model with agreed definitions for revenue and margin, build Fabric pipelines that land the ERP data nightly, add monitoring so failures alert someone before month-end, and deliver rebuilt Power BI reports on the shared model — reconciled line-by-line against the ledger before go-live.
The result isn't magic: it's that the four days become a morning of review, the two reports become one answer, and the next question ("can we see this per customer segment?") is a small change instead of a new project.
Deliverables, not dependencies
Documentation
Every definition, mapping and assumption is written down in plain language. If Meles disappeared tomorrow, your team — or another partner — could pick up the work.
Working systems in your tenant
Pipelines, models and reports live in your Microsoft environment under your access controls. There is no proprietary layer between you and your data.
A team that understands it
Handover sessions are part of the scope. Your people should be able to run, extend and question what was built.
Bring one concrete frustration
A report nobody trusts, a month-end that takes too long, a question that always needs three exports to answer. That's enough to start a useful conversation.