A data migration checklist is the structured set of steps an organisation works through to move data safely from a source system to a target system, covering planning, mapping, quality checks, testing, cutover, and post-migration validation. Skipping steps in this sequence — particularly mock runs and reconciliation — is the single biggest cause of failed […]
A data migration strategy is the plan for how and when data moves from old systems to new ones, and the two dominant approaches are big bang (everything migrates in a single, defined cutover event) and phased (data and functionality move in stages over an extended period, often with both systems running in parallel for […]
What is data migration is the process of moving data from one system, storage environment, or format to another — typically when an organisation replaces a legacy application, consolidates systems after an acquisition, or moves infrastructure to the cloud. It sounds simple: extract data from the source, transform it to fit the target, and load […]
A data quality assessment is a structured process of profiling and scoring your data against defined quality dimensions — accuracy, completeness, consistency, timeliness, uniqueness, and validity — to identify where it falls short of what the business needs. The output is a factual, prioritised picture of data health that tells you exactly which systems, fields, […]
The dimensions of data quality are the measurable criteria used to judge whether data is fit for use: accuracy, completeness, consistency, timeliness, uniqueness, and validity. Each dimension captures a different way data can fail — a record can be complete but wrong, or accurate but outdated — so a full data quality picture requires assessing […]
Data quality management is the set of policies, roles, processes, and tools an organisation uses to make sure its data is accurate, complete, consistent, timely, unique, and valid for the purposes it’s used for — from daily operations to regulatory reporting to AI models. It matters because every downstream decision, report, customer interaction, and automated […]
MDM vs data warehousing comes down to a simple distinction: master data management governs and consolidates the core business entities — customers, products, suppliers — into one trusted, current version, while a data warehouse stores and organises historical transactional and operational data for reporting and analysis. MDM answers “who or what is this, definitively?” A […]
A golden record is the single, consolidated, trusted version of a business entity — a customer, product, supplier, or location — that results from a golden record master data management process. It’s built by matching duplicate and fragmented records from across an organisation’s systems, resolving conflicts between them using defined rules, and publishing the result […]
Master data management (MDM) is the discipline of identifying, cleaning, consolidating, and governing an organisation’s core business data — customers, products, suppliers, locations, employees, assets — so that every system and every person in the business works from one consistent, trusted version of that information. Instead of a customer’s address living differently in the CRM, […]
Data stewardship vs data ownership comes down to accountability versus execution: a data owner is the accountable business leader responsible for a data domain’s outcomes, while a data steward is the person who manages that data’s definitions, quality, and day-to-day decisions on the owner’s behalf. Confusing the two — or leaving both undefined — is […]