Data

Data you can trust

Data is the layer that turns the chaos of distributed data into a governed and reliable asset. When a client is the same client across all your systems. When an order means the same thing in sales, operations, and accounting. When the numbers you see on a dashboard are the same ones that appear in the monthly report.

The data problem isn’t technical. It’s organizational. Each system creates its own version of entities. The CRM has its clients, the ERP has its own, the billing system has its own. The same client can appear three times with three slightly different names, three addresses, and three email addresses. And nobody knows which one is correct.

What Data does

Data establishes a unified data layer on top of your existing systems. It doesn’t replace your databases. It doesn’t force you to migrate. It creates a governance layer that ensures key entities are consistent, complete, and reliable.

Master Data Management: Your business’s key entities (clients, products, suppliers, employees, locations, projects) have a governed master record. Each system syncs with the master. When someone updates a client’s address in the CRM, the update propagates to all systems that need that information.

Entity resolution: Algorithms that detect and resolve duplicates across systems. The “Juan Garcia” in the CRM, the “J. Garcia” in the ERP, and the “Juan Garcia Lopez” in the billing system are the same person. The system unifies them, maintains the links to the original records, and ensures future operations use the correct entity.

Data quality monitoring: Automated checks that detect quality issues in real time. Empty fields that should have values. Inconsistent formats. Out-of-range values. Orphaned entities. Broken relationships. You don’t wait for someone to discover the error. The system detects it and escalates it.

Data lineage: Complete traceability of the origin and transformations of each data point. When a number on a dashboard doesn’t add up, you can trace exactly where it came from, what transformations it underwent, and where it broke. No data archaeology. No asking three teams.

Why it matters

Data is the fuel for all other Engine layers. Orchestration executes workflows based on data. Control calculates KPIs with data. Design builds applications that consume data. If the data isn’t reliable, nothing built on top of it is reliable.

The cost of poor data quality is enormous and mostly invisible. It’s not just the errors that get detected. It’s the decisions made with incorrect data without knowing it. It’s the time teams spend reconciling instead of producing. It’s the distrust generated when every report says something different.

Typical implementation

Data is implemented in phases, starting with the most business-critical entities. The first phase usually covers clients and products, and is completed in 3-4 weeks. Subsequent phases add entities according to the roadmap priority.

Implementation includes defining quality rules, configuring automated checks, the initial resolution of duplicates, and documentation of the governed data model. The Kaizen team maintains quality on an ongoing basis, responding to alerts and adjusting rules as the business evolves.