A business-intelligence dashboard brings selected measures together so managers can see what changed and where attention is required. Many dashboards become collections of charts because they are designed around available data rather than management decisions. Marketways defines the questions, measures and routes for investigation before designing the view. The dashboard then helps a manager move from a signal to the relevant detail and a timely action.
The decision this method supports
We use Business Intelligence & Dashboards to help clients answer: What should leaders be able to see, compare and investigate from one management view?
How the method works
Business intelligence brings selected measures together so leaders can monitor performance and investigate change. A dashboard should organise attention rather than display every available field. The design must show what changed, where the change occurred and which underlying detail a manager can inspect next.
A business example
A regional manager may see that overall sales are stable while one product category is declining in two locations. A useful dashboard allows the manager to move from the overall result to the relevant locations, products and periods without treating the dashboard itself as an explanation.
How the client uses the result
A useful dashboard shortens the distance between a change in the business and a management response. Leaders can see where performance moved, identify the part of the operation that needs attention and decide when deeper analysis is required.
What we deliver
We produce a documented dataset, quality assessment, measure dictionary, analytical-readiness decision or management view. A manager should be able to see what the information represents, which limitations remain and what action the resulting view can support.
Limits and complementary methods
A dashboard is good for monitoring and investigation. It rarely explains why a result changed and should not replace analysis.
Selected methods and techniques
We select from these established methods according to the decision, evidence and operating conditions.
- Anomaly detection: Identify observations or patterns that depart materially from a defined reference using statistical, rule-based or machine-learning techniques. State the reference population, features, threshold and expected error trade-offs, then route flags for interpretation. This is the general method family; unusualness alone does not establish harm, fraud, error or cause.
- Asymmetric-loss analysis: Compare choices when the consequences of different errors or outcomes are unequal. Identify the affected parties, magnitude, timing and reversibility of each loss rather than treating false positives, false negatives or upside and downside as equivalent. The analysis informs the decision rule; it does not supply probabilities unless those are estimated separately.
- Benchmark maintenance: Keep a reference test set and its scoring rules up to date, recording versions so results can still be compared. Replace outdated or exposed questions that a system might have memorised instead of learning to solve the task.
- Benchmark testing: Evaluate a system on a versioned reference set with defined inputs, expected outputs, scoring rules, baselines and acceptance criteria. Keep test cases separate from development and report subgroup, class and severity results where relevant. Benchmark performance describes the covered task set and can be inflated by contamination or repeated tuning.
- Benchmarking: Compare a defined measure or practice with the same organisation's history, another unit, a peer group or an external standard. State the decision, comparison group, metric definition, population, period and data source, then check whether question wording, scales, sampling, collection mode, customer or case mix and operating conditions are sufficiently comparable. Adjust or qualify material differences and show the range and uncertainty rather than relying on one rank. A benchmark provides context for performance; it is not automatically a target, an explanation for a gap or evidence that copying the comparison organisation will improve results.
- Competitor benchmarking: Select relevant competitors or substitutes and compare them on consistently defined dimensions such as offer, price, service level, reach, capability and observed performance. State the comparison period, geography, source and any adjustment needed to make unlike offers comparable, and distinguish verified evidence from marketing claims. Benchmarking shows relative positions and possible performance gaps. It does not estimate total demand, explain the structural causes of industry profitability or prove that copying a competitor will work for the focal business.
- Condition monitoring: Collect and review repeated measurements or inspections that describe an asset's condition while it operates or at planned checks. Define the asset and component, indicator, sampling and calibration rules, operating-load adjustment, reference range, persistence rule, data-quality checks and response to a material change. Condition monitoring can reveal deterioration or abnormal behaviour for investigation; it does not by itself diagnose the failure mode, estimate a calibrated failure probability or choose the maintenance action.
- Cross-location benchmarking: Compare performance across locations using consistent metrics and adjustments for material differences in the populations or cases served.
- Cross-site benchmarking: Compare performance across sites, assets, projects or operating units using measures that have the same meaning and a defensible peer basis. Define the question, peer set, metric formula, units, period and exposure; check data completeness and differences in workload, product or service mix, asset age, operating conditions and accounting rules; then standardise, stratify or qualify material differences. Show raw and adjusted results, uncertainty, ties and sensitivity to reasonable comparison choices. Benchmarking supplies context and identifies gaps for investigation; a rank is not a cause, target or instruction to copy another site.
- Drift monitoring: Continuously or periodically track defined changes in input data, output distributions, relationships and realised performance against versioned references. Set persistence, uncertainty and response rules that account for delayed labels and seasonal change. Drift is a signal for diagnosis and retesting; it does not automatically mean performance has deteriorated or that recalibration is the correct remedy.
- Econometric analysis: Use statistical models informed by economic reasoning to estimate relationships, test hypotheses or make forecasts from economic data.
- Leading-indicator analysis: Evaluate whether a measure changes before a defined outcome, consistently enough and with sufficient lead time to support action. Test timing, stability, false alarms, missed events and whether the indicator adds information beyond current conditions. Precedence is necessary for warning but does not by itself establish cause.
Parent method family
Data Foundations & Business Intelligence explains how this method connects to adjacent methods and relevant services.
Related service families
These service families contain business questions supported by this method. Service pages link to the wider method family so readers can understand the complete analytical approach.