Data Warehouse Requirements Checklist + Template for SMBs
Small business data warehouse requirements checklist and template: business and technical requirements, source systems, KPI owners, BigQuery scope, and proof.
A small business data warehouse project usually succeeds or fails before the first table is built.
The technical platform matters, but the early requirements matter more.
If the business is unclear about which reports matter, which systems define the numbers, who owns KPI definitions, or what needs to improve first, the warehouse can become another place where confusion is stored.
That is why small businesses should prepare a practical requirements checklist before building the warehouse.
If the likely platform is BigQuery, this checklist is also the input to a useful audit. It helps decide what should be built first, what should wait, and which reporting workflow should prove the value of the warehouse.
A data warehouse requirements document does not need to be long. For a small business, it should be clear enough to define the first reporting problem, the systems that feed it, the KPI logic the business will trust, and the BigQuery scope needed to deliver the first reliable output.
This does not need to become an enterprise architecture exercise. It should be a focused way to answer one question:
What does the business need to trust, automate, and report from one dependable place?
If you are still deciding whether the warehouse itself is necessary, start with Small Business Data Warehouse: When You Need One and When You Do Not.
What data warehouse requirements include
Data warehouse requirements are the business, data, technical, ownership, and acceptance rules that explain what the warehouse must prove before teams build tables or dashboards.
For a small business, the requirements should usually separate three layers:
- business requirements: the reports, decisions, KPI definitions, source-system owners, reconciliation points, and success criteria the warehouse must support
- technical requirements: the source feeds, historical data, refresh cadence, BigQuery layers, security controls, data quality checks, monitoring, and recovery needs
- operating requirements: who approves definitions, who responds when checks fail, what gets documented, and how the warehouse stays reliable after launch
That split matters because many warehouse projects fail when technical work starts before finance, operations, or leadership agree on the reporting workflow the first build must improve.
Start with the reporting problem, not the tool
The first requirement is not BigQuery, a BI tool, an ingestion connector, or a cloud architecture diagram.
The first requirement is clarity about the reporting problem.
Most small businesses start considering a warehouse because something is already painful:
- monthly reporting takes too long
- finance and operations disagree on key numbers
- leadership cannot see performance across systems
- dashboards exist but are not trusted
- one person manually rebuilds the same report every week or month
- board, lender, investor, or owner reporting needs more consistency
Write down the pain in business terms before translating it into technical requirements.
A good requirement sounds like this:
Finance needs a repeatable monthly management reporting pack that combines revenue, margin, customer, and operating data without rebuilding joins in spreadsheets.
A weak requirement sounds like this:
We need a data warehouse.
The second statement may be true, but it does not explain what the warehouse needs to do.
Requirement 1: List the decisions the warehouse must support
A small business warehouse should not try to answer every possible question on day one.
Start with the decisions that matter most.
Examples:
- Which products, customers, or locations are profitable?
- Which operational issues are creating margin pressure?
- How is monthly performance tracking against plan?
- Which sales channels are creating the best customers?
- Where is fulfillment, delivery, or service quality slipping?
- Which KPIs should leadership review every week?
- Which metrics need to be consistent in board or lender reporting?
This step keeps the project anchored in business use.
Without it, teams often load data into a warehouse and then discover that the structure does not actually support the reports leaders need.
Requirement 2: Identify the source systems that define performance
Most small businesses have data spread across tools that were bought for operations, not analytics.
Common source systems include:
- accounting or ERP software
- CRM
- billing and subscription platforms
- ecommerce platforms
- inventory or fulfillment tools
- ticketing and support systems
- project management tools
- spreadsheets used for adjustments, mappings, or planning
For many SMBs, the first useful warehouse scope is accounting plus CRM reporting. If QuickBooks and HubSpot are the systems creating the reporting gap, see the QuickBooks to BigQuery reporting model before expanding the source inventory. If NetSuite is already the ERP, use the NetSuite to BigQuery reporting guide to define the first transaction, account, department, class, location, and reconciliation scope.
For each source system, document:
- what business process it supports
- which reports currently depend on it
- who owns the system
- how often the data changes
- whether historical data is available
- whether export or API access exists
- known data quality issues
This inventory is one of the most valuable early deliverables.
It shows which systems must be centralized first and which can wait.
Requirement 3: Decide which reports come first
The warehouse should start with a narrow reporting scope.
For many small businesses, the first phase should focus on one of these:
- monthly finance reporting
- weekly leadership reporting
- cash runway reporting
- operations performance reporting
- board or lender reporting
- sales pipeline and revenue reporting
- margin and profitability reporting
Trying to build all reporting at once creates unnecessary risk.
A better first phase is specific:
- the monthly management report
- the weekly operations review
- the executive KPI dashboard
- the board reporting data pack
The goal is to build enough of the warehouse to make one important reporting workflow faster, cleaner, and easier to trust.
If cash visibility is the pressure point, make cash runway reporting the first workflow. That scope forces the requirements to name the bank, accounting, AR, AP, payroll, and forecast inputs that must reconcile before leadership can trust the runway number.
If the first workflow is sales-to-cash or revenue-to-cash, use order-to-cash reporting to define how CRM, order, billing, accounting, collections, payment, and exception data should connect before the warehouse build starts.
If finance reporting is the first workflow, use the finance reporting data warehouse guide to define the first source systems, KPI definitions, BigQuery layers, reporting tables, and reconciliation checks before expanding into every department.
Once that works, the same foundation can expand.
Requirement 4: Define the KPIs before modeling them
Small businesses often underestimate how much warehouse work is really KPI definition work.
Before building tables, define the metrics that matter most.
For each KPI, document:
- the business definition
- the source system fields involved
- the date logic
- inclusion and exclusion rules
- adjustment rules
- who owns final approval
- where the metric appears in reporting
This matters for metrics such as:
- revenue
- gross margin
- active customers
- pipeline
- backlog
- churn
- cost to serve
- on-time delivery
- utilization
- rework
If the KPI cannot be explained clearly, it cannot be modeled reliably.
This is also where many dashboard trust problems begin. If the business already has dashboards that people question, read Why Your KPI Dashboard Still Is Not Trusted.
Requirement 5: Separate raw data from reporting-ready data
A useful warehouse usually needs layers.
For a small business, the layers do not need to be overcomplicated, but they should be clear.
A practical starting pattern is:
- raw source data
- cleaned and standardized data
- modeled reporting data
- dashboard or reporting outputs
The raw layer preserves what came from source systems.
The cleaned layer standardizes names, IDs, dates, statuses, and data types.
The modeled layer applies business logic, joins entities, and defines reporting tables.
The final layer feeds dashboards, recurring reports, or exports.
This separation makes the warehouse easier to explain and maintain.
It also reduces the risk that executive reporting logic gets hidden inside a BI dashboard or spreadsheet.
It also sets up the post-launch maintenance work. Once the first BigQuery models feed recurring reports, the requirements should specify which freshness checks, schema-change reviews, KPI definition reviews, and reconciliations continue after launch. The BigQuery data warehouse maintenance checklist is the follow-up scope; if no internal owner can run it, data warehouse maintenance covers that operating layer.
Requirement 6: Decide how current the data needs to be
Not every report needs real-time data.
In fact, many small business reporting workflows work better when the refresh expectation is explicit and realistic.
Document the required freshness for each reporting use case:
- monthly close reporting
- weekly leadership reporting
- daily operations monitoring
- near-real-time alerts
- board reporting
- forecast updates
Monthly management reporting may only need a controlled daily or month-end refresh.
Operations reporting may need daily refreshes.
Some customer or fulfillment processes may need more frequent updates.
Do not pay for complexity the business does not need yet.
Requirement 7: Plan for data quality checks
The warehouse should not silently pass bad data into leadership reports.
Early data quality checks can be simple:
- row counts changed unexpectedly
- required fields are missing
- dates fall outside expected ranges
- duplicate customer or order records appear
- revenue totals do not reconcile to finance
- status values do not match expected categories
- source files or feeds did not arrive
These checks do not need to solve every data quality problem.
They need to catch the problems that would make reporting misleading.
Small businesses often get a large improvement from a small number of checks placed around the most important reports.
Requirement 8: Assign ownership
A warehouse without ownership becomes a shared place for unresolved arguments.
The business should know:
- who owns each source system
- who approves KPI definitions
- who owns warehouse logic
- who owns final reporting outputs
- who responds when data quality checks fail
- who can request changes to reporting definitions
This does not mean one person must do all the work.
It means the handoffs are explicit.
For small teams, ownership can be simple, but it cannot be absent.
Requirement 9: Choose a first platform shape
For companies already committed to Google Cloud, BigQuery is often a sensible warehouse foundation.
A practical first version may include:
- Cloud Storage for raw files or exports
- BigQuery for warehouse storage, modeling, and reporting tables
- scheduled queries or an orchestration tool for repeatable transformations
- Looker Studio, Looker, Power BI, or another BI layer for final reporting
- basic monitoring and documentation
The first phase should avoid unnecessary platform sprawl.
The business does not need a large enterprise data platform before it has a dependable reporting foundation.
If you need help turning these requirements into a build plan, Agile DataWarehouse offers BigQuery implementation for SMB reporting warehouses and BigQuery Audit + Warehouse Build when the first scope is still unclear.
Requirement 10: Define what success looks like
A warehouse project should have practical success criteria.
Examples:
- the monthly reporting pack no longer depends on manual joins
- leadership uses one KPI definition for revenue, margin, backlog, or pipeline
- finance and operations can reconcile their views from the same modeled layer
- the weekly dashboard refreshes from governed warehouse tables
- reporting logic is documented and no longer trapped in one spreadsheet
- the team can identify when a source feed failed or produced unexpected data
Avoid vague goals such as "better analytics."
Define the reporting workflow that will be better, faster, or more trustworthy after the first phase.
Data warehouse requirements checklist
Use this section as a lightweight data warehouse requirements template. It should be specific enough to guide the first BigQuery build without turning the project into a large enterprise documentation exercise.
Before starting the build, a small business should be able to answer:
- Which reporting workflow are we improving first?
- Which business decisions depend on that workflow?
- Which source systems feed those reports?
- Which KPIs need formal definitions?
- Which data needs to be historical, current, or both?
- How fresh does each report need to be?
- What data quality checks matter most?
- Who owns source systems, KPI definitions, and reporting outputs?
- What platform components are needed for the first phase?
- What will prove the warehouse is working?
If those questions are unclear, the project is not ready for a clean build.
That does not mean the business should stop.
It means the first phase should be requirements discovery, not implementation.
FAQ
What should a data warehouse requirements checklist include?
A data warehouse requirements checklist should include the reporting workflow, business decisions, source systems, KPI definitions, ownership, freshness needs, data quality checks, platform scope, and success criteria. The point is to define what the warehouse must prove before the team starts building tables.
What should a small business prepare before building a data warehouse?
A small business should prepare source-system inventory, priority reports, KPI definitions, owner assignments, refresh needs, reconciliation points, and a first-phase BigQuery scope. Those inputs help keep the first build tied to reporting value instead of becoming an open-ended platform project.
Do small businesses need a data warehouse requirements document?
Yes, but it can be lightweight. The document should clarify the reporting problem, required source data, metric definitions, ownership, platform choices, and what will prove the first warehouse phase is working. A short practical checklist is usually more useful than a large enterprise template.
What are business requirements for a data warehouse?
Business requirements define why the warehouse exists. They name the recurring reports, decisions, KPI definitions, source-system owners, reconciliation expectations, and success criteria that finance, operations, or leadership need the warehouse to support.
What are technical requirements for a data warehouse?
Technical requirements define how the warehouse will work. They include source feeds, history, refresh cadence, BigQuery raw and modeled layers, security controls, data quality checks, monitoring, recovery, and the handover evidence needed to operate the warehouse after launch.
How should BigQuery requirements be scoped for an SMB?
BigQuery requirements for an SMB should start with one or two recurring reporting workflows, the source systems behind them, the tables and checks needed to reconcile them, and the commercial reports the business needs to trust first. The first phase should avoid platform sprawl and prove one useful reporting workflow.
Is there a simple data warehouse requirements template for SMBs?
Yes. A useful SMB template lists the first reporting workflow, source systems, KPI definitions, owners, refresh needs, data quality checks, BigQuery scope, and success criteria. It should be clear enough to guide the first warehouse phase without becoming a large enterprise requirements document.
Final thought
A small business data warehouse does not need to be large to be valuable.
It needs to be clear.
The best first version usually centralizes the right source data, models a small number of important KPIs, supports one or two critical reporting workflows, and gives the business a foundation it can extend.
That is enough to reduce manual reporting work and make leadership numbers easier to trust.
Start with the requirements that matter.
Then build the warehouse around them.