Data Warehouse Design and Build
Dimensional modelling, data mart design, and data warehouse implementation on BigQuery, Snowflake, Redshift, and Azure Synapse. The single source of truth layer that makes BI dashboards trustworthy.
ARTIFICIAL INTELLIGENCE · BUSINESS INTELLIGENCE
Business Intelligence That Produces Decisions, Not Dashboard Collections
Enterprise BI environments accumulate dashboards. Most are consulted rarely because they were built to answer questions that mattered two years ago or because the data quality is too poor to trust. DAM Networks designs BI infrastructure around the decisions leadership needs to make, not around the data that is available to report on.
THE PROBLEM
The enterprise BI graveyard is full of dashboards that were built, used briefly, and then abandoned when the decision they supported moved to a spreadsheet.
Most enterprise BI environments were not designed — they accumulated. A request for a sales dashboard. A request for an operations report. A request for a marketing attribution view. Each one was built by a different team, from a different data source, with a different definition of the metrics it contains. Three years later, the organisation has 80 dashboards, four different definitions of revenue, two definitions of a customer, and no single source of truth for any of the numbers that appear in the monthly board pack. Critical commercial decisions are being made from numbers extracted manually from source systems because nobody trusts the BI environment.
The fix is not more dashboards. It is a data model. A single, governed data warehouse layer that defines the organisation's metrics consistently, sources them from authoritative systems, and updates on a known schedule. The dashboards built on top of a well-designed data model are used because the numbers are trustworthy. The dashboards built on raw extracts are not used because the first time a number was questioned and could not be explained, the trust was lost.
DAM Networks designs and builds BI environments from the data model outward. The metrics dictionary, the data warehouse layer, the transformation logic, and the governance framework are built before any dashboard is created. The dashboards that follow are used because the data they display has been designed to answer the questions leadership asks.
CAPABILITIES
What DAM delivers across business intelligence engagements
Metrics Dictionary and Governance
Documented metric definitions, ownership, update frequency, and data lineage for every metric in the BI environment. Prevents the conflicting definitions of revenue, customer, and lead that make dashboard numbers impossible to reconcile.
Dashboard Design and Development
Looker, Power BI, Tableau, and Metabase dashboard design and development for commercial, operational, and financial reporting. Designed around the decisions the dashboard audience needs to make, not around the data available to display.
Self-Service Analytics
Semantic layer design, self-service query environments, and analyst training for organisations where the leadership team needs to answer ad hoc questions without depending on the data team for every new report.
DAM APPROACH
BI engagements begin with the decisions that need to be made and the metrics that inform them — not with the data sources that are available.
Before any data modelling work begins, DAM conducts a decision audit with the leadership and operational teams that will use the BI environment. The audit identifies the specific decisions made on a weekly, monthly, and quarterly basis, the metrics that inform those decisions, the current source of those metrics, and the confidence level each stakeholder has in the numbers they currently use. The audit reveals which metrics have conflicting definitions, which are being extracted manually from source systems, and which do not exist in any reportable form.
Data quality is assessed before transformation logic is built. A data warehouse built on poor-quality source data produces confident-looking dashboards with unreliable numbers. DAM audits the data quality of each source system before designing the ingestion and transformation pipeline — and recommends data quality remediation in the source systems where the issues originate, rather than applying compensating transforms in the warehouse layer that mask the underlying problem.
Dashboard adoption is tracked from launch. Dashboards that are not being used after 60 days are reviewed — the reason for non-adoption is almost always either a data quality issue that was discovered post-launch or a disconnect between what the dashboard displays and the actual decision it was supposed to inform. DAM's post-launch review addresses this before the dashboard joins the graveyard.
RELATED SERVICES
Services that work alongside Business Intelligence
WORK WITH DAM NETWORKS
If commercial decisions are being made from spreadsheets rather than the BI environment, the BI environment was not built around the decisions that need to be made.
DAM Networks designs and builds business intelligence environments for enterprise organisations. Engagements begin with the decisions that need to be made — not with the data that is currently available to report on.
FREQUENTLY ASKED QUESTIONS
Questions about enterprise business intelligence
The tool choice depends on three factors: the data warehouse platform, the technical sophistication of the users, and the existing Microsoft or Google ecosystem footprint. Power BI integrates tightly with Azure and Microsoft 365 and is the natural choice for Microsoft-stack organisations. Looker's semantic layer model is the strongest for organisations that need governed, consistent metrics across multiple dashboard consumers and are on BigQuery. Tableau has the strongest data visualisation flexibility and is preferred when analysts need to build complex, exploratory visualisations. All three are capable of meeting most enterprise BI requirements — the tool matters less than the data model underneath it.
A semantic layer is a business representation of the data warehouse that translates technical data models into business terms — metrics, dimensions, and hierarchies — that non-technical users can query without writing SQL. It enforces consistent metric definitions across all dashboards and self-service queries, preventing the situation where different teams calculate revenue differently because each is writing their own SQL against the raw tables. Tools like Looker, dbt Metrics, and Cube implement semantic layers. For organisations with more than one BI consumer team, a semantic layer is the structural control that keeps metric definitions consistent as the BI environment scales.
Consolidation begins with a dashboard audit that identifies which dashboards are actively used, by whom, and what decisions they support. Unused dashboards are decommissioned — not migrated. Active dashboards are reviewed for metric definition conflicts and data source dependencies. A unified data model is designed that can serve all the active use cases from a single, governed source. The dashboards are rebuilt on that unified model, not migrated from their existing data sources. Migration of existing dashboards to a new tool without rebuilding the underlying data model replicates the inconsistency problems in the new environment.