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William Inmon
William Inmon
16 Articles0 Comments

William H. Inmon (born 1945) is an American computer scientist, recognized by many as the father of the data warehouse. Bill Inmon wrote the first book, held the first conference (with Arnie Barnett), wrote the first column in a magazine and was the first to offer classes in data warehousing. Bill Inmon created the accepted definition of what a data warehouse is - a subject oriented, nonvolatile, integrated, time variant collection of data in support of management's decisions.

Creating Corporate Database – Managing Spreadsheet Series: 5 of 5

The last step in creating corporate data from spreadsheets is the creation of the corporate database itself. The corporate database is created when –   The spreadsheet has been selected   The spreadsheet has been logged in   The spreadsheet has been run…

The Spreadsheet Mnemonic Dictionary – Managing Spreadsheet Series: 4 of 5

As spreadsheets are converted from their spreadsheet form into a corporate database, a by product of that conversion is the creation of what can be called the “mnemonic” dictionary. The mnemonic dictionary does not contain values of data found on…

Spreadsheet Veracity & Lineage – Managing Spreadsheet Series: 3 of 5

Most corporations wake up and find themselves in spreadsheet Hell. They have huge amounts of data on spreadsheets and none of it is believable. There is a path out of spreadsheet Hell. The path takes selected spreadsheets and turns the…

The Spreadsheet Maturity Cycle – Managing Spreadsheet Series: 2 of 5

Spreadsheets have been around for a long time. In many ways, the common spreadsheet has emancipated the end user from the control of the IT organization. In a word the spreadsheet provides autonomy of processing to the end user. And…

Spreadsheets & Corporate Data – Managing Spreadsheet Series: 1 of 5

For a hundred reasons, the spreadsheet have become a ubiquitous commodity in the corporate landscape. Perhaps the most pervasive of these reasons is that the spreadsheet allows the end user – the non-technician – to have control over his/her technical…

Textual & Spreadsheet Data – Effective Data Science Series: 5 of 5

When the data scientist goes after structured and machine-generated data, experience has shown that there are not many positive results. Instead, the most fertile grounds are textual data and spreadsheet data. Fig 1 depicts textual and spreadsheet data. But –…

Where Data Scientists are not looking – Effective Data Science Series: 4 of 5

In a previous article, it was stated that data scientists are trying to find meaningful patterns and correlations in structured data and machine generated data and were not having much luck. Like the fisherman who has been fishing in one…

Why Data Science Struggles, a counterview – Effective Data Science Series: 3 of 5

When you take an honest look at the great data science experiment that has occurred worldwide, you find that the promises made by the advocates of data science have been far from the reality of what has been delivered. Once…

Corporate Data, The Sectors – Effective Data Science Series: 2 of 5

In a previous article it was seen that corporate data can be divided into distinct sectors. Fig 1 depicts the different sectors into which corporate data can be divided. The sectors are –    Structured data    Textual data   …

Fishing in the Right Pond – Effective Data Science Series: 1 of 5

It is said of fishermen that 90% of the fishermen fish where 10% of the fish are. Conversely 10% of the fishermen fish where 90% of the fish are. The moral to this story is that the biggest factor in…

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