Data Integrity

Data integrity means your data stays accurate, consistent, complete, valid, and timely from the moment it is created to the moment you use it. Think of it like a shared recipe that everyone follows, so the result tastes the same every time.

In depth

Data integrity covers both the structure of your data and the meaning behind it. Structural integrity relies on schema rules, data types, primary keys, and relationships that prevent broken links and duplicate records. Semantic integrity relies on business rules that define what each value means and when it is allowed.

Common integrity dimensions include accuracy, completeness, consistency, validity, timeliness, and lineage. Accuracy means values are correct. Completeness means the records you expect are present. Consistency means the same rules produce the same results across systems. Validity means values fit the allowed formats and ranges. Timeliness means data is fresh enough for the decision at hand. Lineage makes the flow and transformation of data visible.

Integrity is protected by process. You set owners for key datasets, version your metric definitions, review changes before they go live, and document each transformation. You also track freshness targets and monitor for drift, such as a sudden drop in conversion events after a tracking change.

Technical controls support these goals. Examples include database constraints, unique indexes, referential checks, type validation, reconciliation between systems, anomaly detection, and row‑level history that lets you audit what changed and when.

Pro tip

Give each critical metric a single owner and a published definition. Treat changes like code changes, with review and clear version notes. This prevents silent definition drift that erodes trust.

Why Data Integrity matters

When integrity slips, decisions slow down and confidence drops. You spend time arguing about numbers instead of acting. With strong integrity, you make faster calls with less risk.

Shared truth: Everyone works from the same certified metric definitions, not personal spreadsheets.
Fewer errors: Validation rules and checks catch bad records before they spread.
Faster decisions: Clear lineage and freshness let you judge whether a number is ready to use today.
Lower cost: Less rework, fewer custom reports, and fewer one‑off fixes.
Better outcomes: Consistent inputs lead to consistent operational and financial decisions.

Data Integrity – In practice

Define the metric. Write a metric statement that includes name, description, formula, grain, filters, and source systems.
Set acceptance rules. For example, every invoice must have a customer ID, currency, and non‑negative total; close dates cannot be in the past for open deals.
Add validation checks. Use range checks, unique keys, referential checks, duplicate detection, and reconciliation across systems like CRM and billing.
Establish freshness targets. Pick refresh schedules and service levels per metric, such as 15 minutes for product usage and daily for revenue.
Monitor and alert. Track failures, schema changes, and unexpected spikes or dips. Send alerts to the metric owner and channel used by your team.
Review changes. Version definitions, document why a change was made, and communicate the impact window to stakeholders.

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Data integrity and PowerMetrics

PowerMetrics is built for a metric‑centric, self‑serve workflow where integrity is visible and managed.

Metric catalog and definitions: Create shared definitions with names, formulas, dimensions, and descriptions so everyone uses the same meaning.
Certification and tagging: Mark trusted metrics, add tags, and guide users toward the best version of a KPI.
Access control: Manage who can create, edit, certify, or only view metrics. Protect sensitive fields with roles and groups.
Data modelling and formulas: Join sources, calculate fields with familiar functions, and keep transformations documented and repeatable.
Stored history and comparisons: Keep historical values to audit changes, run period comparisons, and spot anomalies quickly.
Freshness and notifications: Control refresh rates from one minute to daily and set alerts for failures or out‑of‑bounds values.
APIs and debugging tools: Integrate with your stack, surface errors early, and maintain integrity as data flows across services.

Related terms

Data Quality

Data quality measures the reliability of your data. High‑quality data is accurate, complete, timely, consistent across systems, standard-conformant, and free of duplication.

Data Lineage

Data lineage maps the journey of your data from origin to destination. It visually shows where data comes from, how it’s transformed, and where it’s used.

Data Governance

Data governance is the system of people, policies, and tools that keeps data accurate, secure, and usable across your company. Think of it like hiring a skilled librarian for a massive library. Every book is cataloged, protected, and easy to find, so readers trust what they pick up and can act quickly. With solid governance, your team works from the same definitions, follows clear rules for access and use, and treats data as a business asset.

Metric

A metric, in the context of analytics, is a calculated value that tracks performance for a business activity. Think of it as a consistent math formula applied to your data over time, like revenue, conversion rate, or churn rate. A metric includes a clear formula, time frame, and rules for how to slice the data. It turns raw numbers into a repeatable signal you can compare across periods, products, regions, or segments.

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