How I Built Enterprise Solutions on Power Platform

Microsoft Power Platform — Power Apps, Power Automate, and Dataverse — is genuinely powerful for rapid enterprise capability delivery. It is also genuinely dangerous if deployed without governance. The organisations I have seen get it wrong follow a predictable pattern: enthusiastic early adopters build solutions quickly, leadership is impressed by the speed, adoption spreads, and then the governance problems arrive. Uncontrolled data flows, DLP violations, apps built on personal data sources, environment sprawl, no ALM, no rollback path. The platform that was supposed to accelerate becomes a liability.

The Power Platform solutions built in this context were designed governance-first: ALM (application lifecycle management), environment strategy, DLP (data loss prevention) policy, and Dataverse modelling were all defined before the first app was built. The result was a set of solutions that could scale, be maintained, and be updated without risk to the data they contained.

Environment Strategy and ALM

The environment strategy follows a three-tier model: development, test, and production environments with no overlap in data or user access. Solutions are packaged and promoted through the tiers using Power Platform managed solutions, which track dependencies and prevent unmanaged changes in production. This structure means that a change to a production app follows a defined path — development → test → production — with approvals at each gate. It adds process overhead. It also means that a broken deployment can be rolled back in minutes rather than requiring a rebuild.

Dataverse Modelling

Dataverse was chosen as the data layer for all solutions rather than SharePoint lists, which are the default for many Power Apps deployments. Dataverse provides proper relational modelling, row-level security, auditing, and a consistent API. The data models were designed with the same rigour as a traditional database: entity definitions, relationship cardinality, field types, and security roles were all specified before app development began. This investment pays back when the solution scales beyond the initial pilot — the data model does not need to be rearchitected to handle more users or more data.

DLP and Governance

Data loss prevention policies were configured at the tenant level to prevent connectors that could exfiltrate data from the organisation from being used in production solutions. The connector classification — business, non-business, blocked — was defined based on the organisation's data classification policy, not on default settings. Custom connectors were reviewed before approval. These governance decisions were made once and applied consistently, rather than being retrofitted to each solution.

Capabilities and Outcomes

The solutions built on this foundation cover approval workflow automation (replacing email-based approval chains for procurement, leave requests, and document sign-off), canvas apps for field data capture (replacing paper forms and spreadsheet entry), and Dataverse-backed dashboards for operational reporting. The governance framework meant that solutions deployed in this context have a clean audit trail, zero DLP violations, and a defined support and update path. Process cycle times for the automated workflows improved from days to hours. The platform is now the standard delivery mechanism for internal process digitisation across the organisation.