Most organizations do not have a reporting problem. They have several reports, each built by a different team from a different extract, that disagree by enough to make a decision uncomfortable. Fortified Data builds the layer underneath the dashboards, so the number on the screen has one definition, one source, and somebody accountable for it.
Built on the platforms you already own. Senior US-based consultants.
Why it matters
Reporting is not trusted or distrusted on the visual. That is decided further down.
Several reports, each built by a different team from a different extract, disagree by enough to make a decision uncomfortable. The cost is not the reporting effort, it is the meetings spent reconciling before anyone can decide anything.
What decides whether reporting is trusted is whether the warehouse models the business the way the business describes itself, whether the pipelines are noticed when they fail, and whether a measure means the same thing in two places.
Working the reporting layer without the database layer produces exactly that. BI delivered by a database firm keeps the query, the model it reads, and the pipeline that fills it in scope of one engagement.
Business intelligence consulting is the work of turning data held across operational systems into reporting and analytics that people trust enough to decide on. Fortified Data covers BI strategy, data warehouse design and modernization, ETL and data integration, semantic modeling, and visualization, built on the Microsoft BI stack, Power BI, Microsoft Fabric, Azure Synapse, Databricks, Microsoft Purview, and cloud data platforms including Microsoft Azure, AWS, and Snowflake.
The engagement is delivered by consultants whose primary discipline is the database layer the reporting reads from, which is where most reporting problems originate.
The visible part of a BI program is the dashboard, and the dashboard is the part that goes wrong last. What determines whether reporting is trusted is further down: whether the warehouse models the business the way the business describes itself, whether the pipelines run reliably and are noticed when they do not, and whether a measure means the same thing in two places. Fortified Data starts there, which is why the strategy stage comes before any tool selection.
Working on the reporting layer without the database layer is what produces the familiar result: a fast dashboard over a slow, incomplete source. The advantage of BI work delivered by a database firm is that the query underneath the visual, the model it reads, and the pipeline that fills it are all in scope of the same engagement.
Six areas. Which of them an engagement needs depends on what already exists, which is what the first one establishes.
The decisions the business needs to make, the measures behind them, and the order of work to get there, agreed before a platform is chosen or a dashboard is designed.
Warehouse migration, modernization, and automation, including the move from a classic on-premises warehouse to a cloud analytics platform, with the model designed against how the business actually reports.
Pipelines that convert, integrate, and distribute data across systems at scale, with failures that are visible and reruns that are safe.
One definition of each measure, held in a governed model, so two reports built by two teams produce the same number and can be reconciled when they do not.
Dashboards and reports in Power BI, designed so that the people who need an answer can get it without raising a request for it.
Access control, data quality monitoring, and the training and handover that decide whether the platform is used a year later or quietly worked around.
The platform, and the definitions behind it. The second is what keeps the first trusted once the consultants have gone.
The measures that matter, the systems they come from, the gaps between the two, and a sequenced plan in which each stage delivers something usable on its own.
The target data model and platform architecture, documented, including what is staged, what is transformed, and where each transformation happens.
Built and documented ETL processes with scheduling, error handling, and monitoring, so a failed load is noticed before the person reading the report notices it.
Measures and dimensions defined once, with the business definition of each recorded alongside the technical one, so the meaning survives a change of staff.
The reporting people actually asked for, built in your platform, with the access model applied so that each audience sees the data it is entitled to.
How the platform is operated, how a new measure is added, and the training that lets your team extend it rather than return for every change.
The query behind the visual, the model it reads, and the source it came from belong to the same engagement, so a slow or incomplete report is fixed where the problem is.
Each measure gets one definition, recorded in business terms as well as technical ones. Most disagreements between two reports are a disagreement about a definition nobody wrote down.
Engagements are delivered on the Microsoft BI stack, Power BI, and the cloud data platforms already in place, so the recommendation is not a license purchase in disguise.
Error handling, monitoring, and safe reruns are part of the build, because a pipeline nobody can see failing is a reporting outage that lasts until somebody complains.
Training and handover are in the engagement rather than sold after it. A platform nobody was taught to extend becomes a dependency on whoever built it.
The people modeling your business are reachable in your working day, which matters more on BI work than on most, because the modeling questions are business questions.
Platform coverage
Most BI proposals name a destination platform. These are the components the work actually touches, and where a client already owns one of them, the engagement builds on it rather than around it.
Data movement and ETL: converting, integrating, and distributing data across applications at scale, with scheduling, error handling, and reruns that do not duplicate what they load.
The semantic layer. Measures and dimensions defined once and served to every report, which is the component that stops two dashboards disagreeing about the same number.
Operational and paginated reporting, including the scheduled and printable output that a self-service dashboard tool is a poor substitute for.
Interactive dashboards, self-service analysis, and distribution, modeled against a governed dataset so self-service does not become each analyst rebuilding the same measure differently.
The unified analytics platform, where a client is consolidating warehousing, pipelines, and reporting onto one service rather than assembling them from separate components.
Warehouse and large-scale analytical workloads on Azure, including the migration path from a classic on-premises warehouse, designed as part of the engagement.
Lakehouse and large-scale data engineering, for estates where the analytical workload has outgrown what a relational warehouse is the right shape for.
Governance and cataloging: where a figure came from, who can see it, and what a measure means, recorded alongside the platform rather than in a document beside it.
Microsoft Azure, AWS, and Snowflake for warehouse modernization and elastic analytical workloads, sized against measured usage, with the migration path from a classic warehouse designed as part of the engagement.
Power BI for reporting and visualization, the Microsoft BI stack including SQL Server Integration Services, Analysis Services, and Reporting Services for integration and modeling, Microsoft Fabric, Azure Synapse and Databricks for analytics and lakehouse workloads, Microsoft Purview for governance and cataloging, and cloud data platforms including Microsoft Azure, AWS, and Snowflake. Engagements are built on the platforms already in place wherever those platforms can meet the requirement.
Not always. Where the reporting is confined to one system and the volumes are modest, a governed semantic model over the source can be enough. A warehouse becomes necessary when reporting spans systems, when history has to be preserved beyond what the source keeps, or when analytical queries are competing with transactions for the same resources. Fortified Data establishes which case applies during the strategy stage rather than assuming the answer.
Business intelligence delivers the reporting and analytics layer and the models that feed it. Data modernization rebuilds the platform and architecture underneath. The two overlap in practice: a BI engagement frequently finds that the source data cannot support what is being asked of it, and that finding becomes a modernization scope. Fortified Data delivers both, so the handover between them is internal.
Your team, unless you ask otherwise. Every Fortified Data BI engagement includes documentation of how the platform is operated and how a new measure is added, plus training for the people who will extend it. Ongoing support is available through on-demand hours, and the databases underneath can move into a managed service plan.
By defining each measure once in a governed semantic model and recording the business definition alongside the technical one. Reports then read the same definition rather than each re-implementing it. Where two existing reports already disagree, the engagement starts by reconciling them, because the reconciliation usually reveals which assumptions the business has been making without knowing it.
Yes, and it is often the cheaper route. Existing reports are a record of what the business genuinely needs, which is expensive information to gather any other way. Fortified Data reviews what exists, keeps what works, corrects what disagrees, and rebuilds only where the current implementation cannot be made reliable.
No. Cloud analytics platforms suit workloads that are elastic, large, or shared across locations, and on-premises platforms remain appropriate for plenty of estates. Fortified Data sizes and prices the viable options against your measured workload, and the recommendation follows that rather than a general preference.
A BI build is a project with an end date. If the pipelines and the platform then need operating, these are the routes.
We run your environment, with 24/7 monitoring, maintenance, and proactive improvement.
A guaranteed 15-minute emergency response plus monthly on-demand hours that bank when unused.
Project-based engagements for migrations, performance tuning, data modernization, business intelligence, and high availability and disaster recovery.
The first conversation is about which decision is being made without good information, and what is in the way of it. The platform choice comes after that, because it depends on the answer.
Let us show you what's possible.