Develop a KPI Dashboard Specification
ChatGPT Prompt
Specify a KPI dashboard for {audience} at a {business_type} supporting {decisions}. Start from the decisions the viewer makes, then work back to the minimum metrics that inform them — most dashboards fail by inverting this. For each metric define: the precise calculation, the source system, the grain, the refresh frequency, the comparison basis (versus target, prior period, or same period last year), and the threshold that should prompt action. Separate the three or four headline metrics from supporting detail, and include leading indicators rather than only lagging outcomes. Specify layout priority, the chart type per metric with the reasoning, and the annotation mechanism for explaining anomalies. Add what deliberately does NOT belong on this dashboard.
💡 Pro Tip: Replace placeholders in [BRACKETS] with your specific information for best results.
How to Use This Prompt
- 1Click the copy button above to copy the prompt to your clipboard
- 2Open ChatGPT, Claude, or your preferred AI assistant
- 3Paste the prompt and replace any [PLACEHOLDER TEXT] with your specific details
- 4Review the AI's response and iterate if needed for better results
Related Tags
Related Prompts
Create a Data Cleaning Procedure
Write a repeatable data cleaning procedure for {dataset_type} from {source}. Cover in order: profiling before changing anything (row counts, distributions, null rates, cardinality), schema and type validation, duplicate detection and the matching rule for near-duplicates, missing value handling with the decision rule for drop versus impute versus flag — and the bias each introduces, outlier detection separating genuine extremes from errors, standardising formats (dates, currencies, categoricals, text casing and whitespace), and referential integrity. For every step, log what changed and how many rows were affected, so the cleaning is auditable and reversible. Add the validation suite that runs afterwards and the thresholds that should halt the pipeline rather than passing degraded data downstream.
Write a Statistical Analysis Interpretation
Interpret the results of {analysis_type} on {dataset} for a non-technical audience making {decision}. Translate each statistic into a plain sentence about the real world, then state the practical significance separately from the statistical significance — a result can be highly significant and commercially irrelevant. Give the effect size with its confidence interval in units the audience uses. State the assumptions the method requires, whether they hold here, and what it means for the conclusion if they do not. List the alternative explanations you cannot rule out with this data, and the specific additional data that would settle it. End with what you would and would not decide on this basis.
Design a Customer Segmentation Analysis
Design a customer segmentation for a {business_type} with {customer_count} customers, to be used for {intended_use}. Choose the approach that fits the use — RFM for retention and lifecycle, behavioural for product decisions, value-based for prioritisation, needs-based for messaging — and justify it rather than defaulting to k-means. Specify the features, the treatment of outliers and missing data, and how the number of segments is chosen and validated. For each resulting segment give: size, share of revenue, defining characteristics, and the specific action it enables. Apply the usability test — a segmentation nobody can act on is an academic exercise — and add the refresh cadence and the drift signals that mean it needs rebuilding.
Create a Predictive Model Framework
Design a predictive modelling framework for {prediction_target} at a {business_type}. Start with the framing: what decision the prediction changes, the cost of a false positive versus a false negative, and the baseline (often a simple rule) that any model must beat to justify its complexity. Then cover: target definition and the prediction window, feature engineering with explicit leakage checks, a train/validation/test split that respects time ordering, model selection starting simple, and evaluation using metrics chosen for the business cost rather than accuracy — which misleads on imbalanced targets. Add calibration, interpretability requirements, the deployment and monitoring plan including drift detection and a retraining trigger, and the fairness review where the prediction affects people.
Write a Data Governance Policy
Draft a data governance policy for a {company_size} {industry} organisation. Cover: data ownership and stewardship with named roles and real accountability, a classification scheme (public, internal, confidential, restricted) with the handling requirement for each, access control and the request and review process, retention and deletion schedules by data type, quality standards and who enforces them, a business glossary resolving the definitional disputes that make two reports disagree, lineage documentation requirements, and privacy obligations under the applicable regimes. Include the governance forum, its decision rights, and an exception process — because a policy with no legitimate exception path gets routed around entirely. Keep it operational rather than aspirational.
Develop a Data Quality Assessment
Design a data quality assessment for {data_domain} at a {company_type}. Assess across six dimensions with a concrete measure for each: completeness, accuracy against a trusted reference, consistency across systems, timeliness relative to the decisions it supports, validity against business rules, and uniqueness. For each, define the test, the acceptable threshold justified by downstream use rather than an arbitrary percentage, and the measurement frequency. Produce a scorecard by source system, a prioritised issue list ranked by downstream business impact, and root-cause categories distinguishing collection problems from pipeline problems from definitional disagreements. Add the ongoing monitoring and the alerting rules that catch degradation before a report is published on bad data.