Your data is the business — your intellectual property. You generated it, and for a growing number of businesses it is a product line of its own. Treated as an asset, data compounds. Neglected, data deteriorates.
Different symptoms. Usually the same foundation underneath.
Dashboards everywhere, decisions made on instinct anyway. A report nobody acts on is not analytics.
The pilot worked on the sample. Production data is another story.
Nobody is accountable for the data worth most to the business, and nobody can say who is allowed to use what, for what.
Multiple systems, multiple customer counts, one board meeting. Which one is right?
An old database in the back room, feeding everything, modernized by nobody.
The contracts database that would answer the question does not exist — or exists in a drawer.
Six dimensions, and a program fails on the one that was not checked. Data sits at the base of the ladder, and everything above inherits what is here.
Form after the AI Hierarchy of Needs — Monica Rogati, 2017
Enough to represent what you are asking the data to describe. Enough to train, test and run.
The gaps known and documented, rather than discovered downstream.
Lineage that can be shown — origin, and every hand that has touched the data since.
Ownership and stewardship assigned by name. Classified, access-controlled, answerable to someone.
Not collected and left. Curation is the standing work of keeping data fit for the decisions it serves.
Fresh enough for the decision at hand, and observed closely enough to know when freshness lapses.
Data governance is not a prerequisite you clear once and move past. It is a stage gate on the project, and a standing discipline forever. A project proceeds on assessed data; governance sits in the delivery path, not alongside.
Governance works when named people are accountable. Data owners and data stewards, accountable by domain, for the data worth most to the business. Executive sponsorship for data quality and for data ethics — both.
Data, information, knowledge, wisdom. Everything above inherits what is at the bottom — and generative AI is now layered inside every system the business runs, inheriting and amplifying whatever quality and accessibility issues are already there.
Nine honest questions scope the work — governance, owners and stewards, sponsorship, master data, sources, volume, where critical data lives, the infrastructure underneath, and where the pain is. Most organizations already know which ones they cannot answer.
Where the organization sits in data modernization and warehouse maturity. Structured and unstructured. Anonymized or not.
Centralized or federated, warehouse or enterprise brain — the structure follows the decisions the business needs to make.
Six dimensions measured, owners and stewards by name, a gate on every project.
Core entity definitions, reconciliation across systems, standards and stewardship — so consolidated reporting returns answers people trust.
Metric definitions, reporting and dashboards, and adoption by the people who decide.
What trains the model, what tests it, what holds before release, and what is continuous quality. Watch for drift.
Twenty-one applications and instances into two platforms produced a single view of the services the company delivered. A single CRM carrying a hundred thousand fields, added one leadership change at a time, is the case for master data before consolidated reporting.
Foundational readiness first. It tells you what your data is actually worth — and what it is exposing — before anything else gets built on top of it.
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