Data Modeling and Forecasting
Data models and forecastsfor business planning
Bring financial and operational records together to understand performance and plan what comes next. Build Lean develops custom data models, forecasts, and reporting around the revenue, cost, cash flow, or capacity decisions your team needs to make.
What is data modeling and forecasting?
Data modeling
Data modeling organizes records from your business systems into consistent definitions and relationships. It establishes what a number means, which records belong in it, and how to compare it across projects, customers, properties, or reporting periods.
Forecasting
Forecasting uses those records, current information, and explicit assumptions to estimate future results. Depending on the question, that can mean calculating costs from planned activity, projecting a pattern over time, or testing how a change in demand or payment timing affects the outlook.
What Build Lean builds
Build Lean scopes the solution around your existing environment. The work can include configuring data tools, integrating systems, developing calculations and forecast models, and building dashboards or review interfaces. Each project defines the deliverables, refresh schedule, access, and operating responsibilities.
Source Records
Selected financial and operational data, with reporting dates
Consistent Metrics
Matched records, checked totals, and agreed business definitions
Forecasts and Scenarios
Calculated estimates or statistical models, with visible assumptions
Review and Reporting
Results, limitations, and changes presented to the people making the decision
Exception path
Missing records, failed checks, and stale inputs are flagged before affected results are treated as current.
Source Records
Selected financial and operational data, with reporting dates
Consistent Metrics
Matched records, checked totals, and agreed business definitions
Forecasts and Scenarios
Calculated estimates or statistical models, with visible assumptions
Review and Reporting
Results, limitations, and changes presented to the people making the decision
Any source, check, or forecast can branch to:
Exception path
Missing records, failed checks, and stale inputs are flagged before affected results are treated as current.
Where AI fits
Many forecasts use formulas and statistical methods without generative AI. Where useful, AI can help extract information from documents or draft a summary of results. Those outputs need validation. Forecast calculations, publication rules, and access controls are defined separately.
Technical details: Data models, forecasting, and refresh
- Data preparation and structure
- Pipelines retrieve selected records through available APIs, connectors, or file imports. ELT means extracting and loading those records before transforming them into analysis tables. We define what each row represents, how records join, and how corrections and duplicates are handled.
- Business definitions
- SQL transformations can calculate metrics from agreed source fields. Fact tables hold events or amounts, such as invoice lines or posted costs. Dimension tables provide context, such as the customer, project, or reporting period. Source references and reconciliation checks help explain differences between reports.
- Forecast methods
- Driver-based models calculate outcomes from inputs such as units, rates, workload, or payment timing. Time-series models project patterns over time. Regression models estimate relationships between a target and selected factors. The method depends on the decision and the available evidence.
- Refresh and access
- The scope defines which records can be read, who can view or change assumptions, and when results refresh. Data refresh, forecast recalculation, and model retraining are separate operations. Failed checks and missing inputs need a visible status and an owner.
- Infrastructure
- The solution may use an existing warehouse or a platform such as BigQuery. It may also include transformation tools, scheduled jobs, and a business intelligence dashboard. The architecture is selected for the project.
A clearer basis for planning
Bring the numbers, assumptions, and comparisons into a view your team can use during its regular planning cycle.
Spend less time rebuilding reports
Refresh selected source records and reuse agreed calculations. Reduce repeated exports, spreadsheet preparation, and reconciliation between different versions of the same report.
Understand what drives the forecast
See the source period, key assumptions, and calculations behind an estimate. Give reviewers a way to question the inputs and explain changes from the previous forecast.
Compare different plans
Test changes in workload, rates, timing, or other relevant drivers. Keep the assumptions visible so your team can understand why one scenario differs from another.
Learn from actual results
Compare saved forecasts with what happened. Identify recurring differences and decide whether the assumptions, method, or underlying data need to change.
Forecasting questions across your industry
These illustrative examples show how business records and planning assumptions can support a forecast. The available data and the decision your team needs to make determine the scope.
Estimate cost to complete and review projected margin
A project forecast could combine posted costs, accruals, commitments, approved changes, and the project team’s estimate of remaining work. Shared project and cost codes would connect the records, with checks to avoid counting costs twice.
The resulting view could show cost incurred to date, estimated remaining cost, and projected margin against the agreed revenue basis. Project managers review the remaining-work assumptions and explain changes. Historical comparisons can provide context where project types, scope, and cost definitions are comparable.
These examples describe possible implementations. Source access, consistent definitions, data quality, and review requirements determine what can be built.
Illustrative Workflow
From project records to a reviewed cost forecast
- 1Retrieve
Bring the Records to a Common Reporting Date
Retrieve the selected cost, commitment, change, and revenue records. Match project and cost codes, reconcile agreed totals, and record the reporting cutoff.
Missing sources, unmatched codes, and incomplete periods are flagged for review.
- 2Calculation
Calculate the Current Position and Remaining Work
Calculate cost incurred to date using the agreed treatment of posted costs and accruals. Combine it with an estimate of the cost of remaining work.
Outstanding commitments, expected purchases, and labor estimates need clear treatment so costs already incurred are not included again.
- 3Human Review
Review Assumptions and Compare Scenarios
The project manager reviews remaining quantities, rates, schedule assumptions, and known changes. A comparison view could show how a delay or a revised cost assumption affects the estimate.
Changes to assumptions are recorded separately from source actuals.
- 4Reporting
Publish the Reviewed Version and Track Changes
The reporting view presents the forecast date, source cutoff, assumptions, and review status. It shows how the estimate differs from the previous version and, as work progresses, from actual results.
Refreshing a forecast does not itself change an approved budget or accounting record. Any updates to those systems would be separately scoped.
Calculation
- Estimated final cost
- = cost incurred to date + estimated cost of remaining work
- Projected gross profit ($)
- = agreed forecast revenue − estimated final cost
Who does what
Data connections retrieve the records. Defined calculations assemble the estimate. The project team supplies and reviews assumptions about remaining work. Reporting presents the version selected for use. This example uses a driver-based forecast. It does not require generative AI.
How we build your model
- 01
Define the Decision
Agree the number to estimate, the planning horizon, who will use it, and what action it should inform. Identify the current planning method and the standard a new model needs to meet.
- 02
Assess and Organize the Data
Review source access, historical coverage, missing records, and changes in definitions. Establish record matching, reporting periods, and reconciliation checks.
- 03
Build the Calculations and Forecast
Configure suitable tools and develop the data transformations, assumptions, and model needed for the task. Keep actual results, estimates, and scenarios clearly distinguishable.
- 04
Evaluate the Result
Where history permits, test forecasts using only information available at the time and compare them with later actual results and a simple baseline. Review errors at the planning horizon that matters to the team. For scenario models, test the calculations and sensitivity to key assumptions.
- 05
Deploy and Support the Planning Cycle
Set up reporting, refresh schedules, access, and review responsibilities. Document how assumptions change, how failures are handled, and how model performance will be reassessed after launch.
Start with one planning question
Choose a number your team regularly needs to estimate or explain. We’ll assess the available records and scope a first model, the reporting around it, and how its usefulness will be evaluated.
Your proposal identifies what will be configured, integrated, and custom-built, along with delivery responsibilities and ongoing operating costs.
Data modeling and forecasting questions
What to consider before building a model around your business data.
Which number does your team need a clearer view of?
Tell us what you need to estimate, where the records live, and how your team makes the decision today. We’ll discuss whether a data model, a forecast, or a scenario tool is a useful starting point.
Free · 30 minutes · No preparation required