Business Intelligence
What Does Business Intelligence Delivery Include?
Our Business Intelligence service covers decision and KPI requirements analysis, data source assessment, data quality evaluation, reporting and dashboard specifications, data model and integration requirements, acceptance testing, and adoption and data literacy support. We produce decision requirements maps, KPI definitions with calculation logic, data quality assessments, dashboard wireframes with metric specifications, data model definitions, end-to-end test scenarios, and adoption measurement frameworks. Whether the solution is a self-service analytics platform, an executive reporting suite, embedded BI, or a full data warehouse implementation, every BI initiative starts with the same discipline — rigorous business analysis that connects business questions to data answers.
Key Facts
Why Do BI Projects Need Business Analysis?
BI Vendors Sell Platforms, Not Decision-Support Solutions
BI vendors sell platforms. Data engineering teams sell pipelines. Neither starts by asking what business decisions the organization needs the data to support, whether the source data is trustworthy enough to base those decisions on, or whether the people who need insights can actually interpret and act on what the dashboards show them. That's why most organizations are increasing BI adoption but only 29% of employees are actually using the tools — and why 67% of business leaders don't trust the data behind their dashboards.
Dashboards That Show Available Data Instead of Decision-Relevant Data Get Ignored
The consequences of a BI initiative built without business analysis are predictable and persistent. Dashboards that show available data instead of decision-relevant data get ignored. Reports that answer operational questions but miss strategic ones don't reach the executives who funded the project. Data warehouses built without quality assessment inherit every error from every source system and compound them into a single “source of truth” that nobody trusts.
Self-Service BI Without Governance Produces Conflicting Metrics
Self-service BI deployed without governance produces conflicting metrics across departments — each team telling a different story with the same data. Without a semantic layer that enforces consistent definitions, “revenue” means one thing to finance and another to sales. “Active customer” has three definitions across three departments. Each failure doesn’t just waste the BI budget. It erodes the organization’s willingness to trust data at all.
Business Analysis Connects Business Questions to Data Answers
Business Analysis Canada solves this by treating every BI initiative as a decision-support problem first and a data engineering problem second. We identify what decisions the business needs to make, what data those decisions require, where that data lives and how trustworthy it is, and what form the insights need to take for decision-makers to act on them. The result is BI that people actually use — because it answers the questions they’re actually asking.
Who This Is For
Business Analysis Canada’s Business Intelligence practice is built for organizations investing in analytics capabilities that need analytical rigour between business decision-makers and the data engineering team.
By Organisation Type
large organizations implementing Power BI, Tableau, Qlik, or Looker across multiple departments where dashboard requirements vary by business function and nobody has documented what decisions each stakeholder group needs the data to support.
organizations with 200–2,000 employees moving from spreadsheet reporting to structured BI — where the data lives in disconnected systems, nobody has assessed data quality, and the risk of building dashboards that show data nobody trusts is high.
technical teams that can build data pipelines and configure BI platforms but lack the requirements layer — decision mapping, KPI definition, dashboard specifications, and adoption planning — that determines whether what they build gets used.
companies that have invested in BI tooling but usage has plateaued at a fraction of the licensed users — where the problem is not the platform but the disconnect between what the dashboards show and what decisions they need to support.
By Scenario
if the dashboards are live but the executive team still makes decisions from spreadsheets, the root cause is almost always a requirements gap — the dashboards show available data instead of decision-relevant data. A decision requirements analysis reconnects the BI investment to business value.
if leadership doesn’t trust the numbers in the dashboards because source data has known quality issues, a data quality assessment and cleansing specification is needed before any additional BI development adds value.
if finance, sales, and operations each report different numbers for the same KPIs, the problem is a missing semantic layer — consistent metric definitions that enforce a single source of truth across all downstream reporting.
if the board has mandated data-driven decision-making but nobody has mapped what decisions need data support, what data those decisions require, or what the organization’s data maturity level actually is — the initiative needs requirements before it needs technology.
BI Approaches
Platform Expertise
Microsoft Power BI
Semantic model design (DAX measures, relationships, row-level security), dataflow requirements, gateway configuration, workspace governance, deployment pipeline rules.
Tableau
Data model structure, calculated fields, dashboard interaction design, Server/Cloud governance, data source certification.
Zoho Analytics
Connections to Zoho CRM/Books/Projects, query tables, formula columns, KPI widgets, dashboard sharing rules.
How Does a Business Intelligence Engagement Work?
What Does Business Intelligence Delivery Include?
Most BI projects start with data sources and work forward to dashboards. The data engineering team builds pipelines, configures the platform, and creates visualizations from whatever data is available. The result is a wall of charts that impresses in a demo but doesn’t answer the questions leadership is actually asking. Six months later, the executive team is back in spreadsheets and the BI investment is shelfware.
Business Analysis Canada starts with business decisions and works backward to data. We define what decisions need to be made, what data those decisions require, and what form the insights need to take — then we specify the dashboards. We don’t resell BI licences or earn referral fees. Our value is in the analysis that determines whether the BI investment delivers insights people act on.
Our Advantages
What You Get
Frequently Asked Questions
We provide the analytical layer: decision requirements, KPI definitions, dashboard specifications, data model requirements, and acceptance testing coordination. The technical build — dashboard development, data pipeline engineering, and platform configuration — is handled by your internal BI team, a data engineering partner, or the platform vendor. We remain embedded throughout to maintain requirements traceability.
That's a deliverable we provide. We evaluate platforms against your validated requirements: Power BI, Tableau, Qlik, Looker, SAP BusinessObjects, IBM Cognos, Sisense, and others. The recommendation traces to documented decision requirements, data landscape, user profile, and organizational context — not vendor marketing.
Low adoption is the most common BI problem we address. We assess the root causes — typically a mismatch between what the dashboards show and what decisions they need to support, data quality issues that erode trust, or insufficient data literacy among target users. We then produce the requirements and adoption plan needed to reconnect the BI investment to business value.
Not necessarily. Many effective BI implementations connect directly to operational databases, cloud data sources, or application APIs. A data warehouse becomes valuable when you need to combine data from multiple systems, maintain historical data for trend analysis, or enforce consistent metric definitions across the organization. We assess your data landscape and recommend the right architecture based on your actual requirements.
Data quality assessment is a standard deliverable. We document source data issues, define severity levels, specify cleansing and transformation rules, and recommend whether quality improvements should be addressed before or during the BI implementation. We don't build data cleansing pipelines, but we produce the specifications your data engineering team needs.
Timelines vary by scope. A focused executive dashboard project for a single business function might take six to eight weeks for requirements and specification. An enterprise-wide BI program across multiple departments with data warehouse requirements can run twelve to twenty-four weeks. We scope realistic timelines during the initial conversation.
Yes. Post-launch support includes adoption monitoring, usage analysis, metric refinement, and requirements for iterative dashboard improvements. BI solutions are living products that need ongoing analytical support as business questions evolve and new data sources become available. This maps to our Support & Optimization service.
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