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Archive | 2011

Data Quality: Objective or Subjective?

Data quality cannot be looked at and defined objectively unless you know the context in which it is to be applied, i.e. the Business Functions the data is meant to support. All other definitions are subjective and, as such, prevent Data Quality being achieved.

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Logical Data Model for Customer & Supplier

The logical data model below shows the data structure needed to support Customer, Supplier and Employee.  Pardoxically, none of these appear as an enitity on model! How can that be? There is a very good reason for this, which is that Supplier, Customer and Employee are all deriveable data entities and, as such, ought NEVER […]

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Customer is Not a Master Data Enitity.

Having Customer and Supplier as Master Data Entities is about as sensible as having Creditor and Debtor. Why? Because all of these terms refer to derivable relationships and implementing them as Master Data Entities is both a modelling and business error. If this is true, why are they so often modelled as such? It all […]

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Will Best Practice Keep Your Data Quality Boat Afloat?

Some of attendees at a recent workshop that I ran told me that data quality was assured in their enterprise because they always followed industry best practice.  On the surface this seems to be a compelling argument.  If you are following best practice all must be right with your data quality.  Or is it? If […]

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Data Quality: Driven by Function and Form or Glue and String?

Is the data quality in your enterprise driven by function and form or held together by string and glue?  How did you map and build the data structures required to support the functions of the enterprise? Did you start with Business Functions, build a fully normalised, convergent Logical Data Model (LDM) and then base all of […]

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Data Quality: Dead Crows Kill Customers!

Dead Crows also Kill Suppliers! While recently doing a webinar on with Dylan Jones, of Dataqualitypro, I was the describing the essential role of Logical Data Model plays in Data Quality.  During our discussions, Dylan asked me to give examples of how the Model helped and, of course, I had to tell him how Dead […]

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The Pitfalls of Data Re-Use

The reuse, though seemingly an eminently sensible practice, is fraught with danger. The only data that is truly safe to re-use is genuinely raw data. All other data should carry a health warning.

Read on on to find out what these dangers are and how to avoid them.

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Name That Place

There is one aspect of Data Management and Data Quality that causes the greatest proliferation of duplicates across the globe, is the confusion that exists regarding the Unique Identifiers (UIDs) of data entities. I previously addressed UIDs in the posts such as One Version of the Truth. and The Power of QUACKs and UIDs However, last week […]

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