Data source assessment
Identify systems of record, spreadsheets, exports, APIs, manual inputs, quality problems, and ownership gaps.
Data analytics and decision support
Give management one reliable view of what is happening.
865 Works consolidates operating data, defines useful metrics, builds management reporting, and creates decision support that reflects how the business actually runs.
The business case
Analytics fails when the metric is detached from the operating decision.
A dashboard is useful only when the numbers are trusted, updated at the correct frequency, and tied to a decision someone is responsible for making. Attractive charts do not correct inconsistent definitions, missing records, stale data, or unclear ownership.
865 Works begins with the management question: what decision should improve, how is it made today, what data supports it, and what action should follow from an exception? The implementation then addresses sources, definitions, transformation logic, refresh schedules, permissions, and presentation.
AI can help summarize trends, explain variances, forecast demand, and answer questions about approved data. Those capabilities become valuable only after the underlying data model and business definitions are controlled.
Implementation scope
From disconnected records to operating intelligence.
The work can range from a focused daily scorecard to a multi-system management reporting platform.
Metric and semantic design
Define revenue, volume, labor, conversion, capacity, exceptions, and other measures so every user sees the same calculation.
Dashboards and scorecards
Build role-appropriate views for owners, managers, departments, locations, and daily operating reviews.
Forecasting and AI analysis
Add scenario modeling, demand forecasts, anomaly detection, variance explanations, and controlled natural-language questions where the data supports them.
How the work is controlled
Build trust before adding sophistication.
The reporting system advances from source reliability to decision support.
- 01
Define decisions and users
Document the management questions, operating cadence, thresholds, and person responsible for action.
- 02
Reconcile the sources
Compare totals, dates, identifiers, missing values, and duplicate records until the system of record is clear.
- 03
Create governed calculations
Implement tested transformation logic, metric definitions, refresh monitoring, and auditability.
- 04
Design for exceptions
Highlight what requires attention instead of forcing managers to search through every number.
- 05
Add forecasting and AI carefully
Evaluate prediction error, confidence, seasonality, drift, and the human decision process before relying on advanced analysis.
Data freshnessReconciliation rateForecast errorReporting timeDecision latencyException closure
Practical questions
What business owners usually need to know.
Implementation decisions should be based on the workflow, risk, data, users, and economics—not a predetermined AI product.
Can you work with spreadsheets?
Yes. Spreadsheets may remain part of the process, be consolidated into a controlled model, or be replaced when they create material quality and ownership problems.
Do we need a data warehouse?
Not always. The architecture should match data volume, sources, history, refresh needs, security, and the number of users.
Can managers ask questions in plain English?
Yes, but the system should restrict questions to governed data, show sources or calculations where practical, and avoid presenting uncertain interpretation as fact.
How do we know a forecast is useful?
Compare predictions to actual results over time, measure error by operating segment, and determine whether the forecast changes a decision enough to justify its complexity.
Start with the operating problem
Which recurring workflow should work better?
Request an AI implementation assessment. 865 Works will review the business problem, systems involved, users, constraints, and the most practical next step.