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Blogs Article Struggling to Allocate a Budget for Data Quality Management? Think Pyramid

Struggling to Allocate a Budget for Data Quality Management? Think Pyramid

Martin Doyle November 12th, 2020 Article, Data Quality, Master Data Management

Everyone wants quality data. But, when the time comes to budget for data quality management operations, many businesses fall short.

It’s easy to see why they hesitate. After all, we’re surrounded by data. It’s available by the gigabyte on our computers and phones and piped to us at 20Mb per second down our broadband connections. Why bother to maintain the data we’ve got already? Surely we can just load new data to take its place…

While data professionals recognize such thinking as wrongheaded, they often have trouble communicating the nature of the error to their management colleagues. At DQ Global, we explain it using a classic aide-mémoire — the DIKW pyramid.

Since this may be your first encounter with the pyramid, we’ll begin by spelling out its core insight, which is that data isn’t an end in itself. Indeed, in the multi-tiered DIKW pyramid, data is just the beginning.

Let’s work through the tiers of that pyramid one-by-one to see how that plays out.

Data, comprising the bottom tier, is the raw stuff of computing — a stream of ones and zeros. Although it is essential for IT operations, it is so abstract as to be completely inaccessible. No-one engages directly with data.

Information, the next tier, represents data brought to the human scale. Information appears when the bits and bytes on the computer disk are re-expressed as letters and figures.

Knowledge, occupying the third level, is information given context, enabling letters and figures to be interpreted as words and statistics.

Wisdom, the very top of the pyramid, is the product of knowledge and insight.

The pyramid model is simple, but it communicates two very important points, both deserving further attention.

The first is that a great deal of data must be distilled to garner a small amount of wisdom. DIKW pyramid veterans illustrate this using the famous tomato scan example. Let’s work our way up the pyramid again, this time following a specific input.

On Tier 1, the data level, we see that input as a mass of undifferentiated bits and bytes.

On Tier 2, the information level, we re-appraise it as a series of hexadecimal codes in a grid pattern.

Arriving at Tier 3, the knowledge level, we decode those numbers and recognize an 8-bit scan of a juicy, red fruit.

Tier 4, the wisdom level, brings insight. “Hey, that fruit is a tomato! Don’t put it in the fruit salad!”

The second lesson of the pyramid is equally important, but harder to assimilate. If the lowly data on the base tier is flawed, the entire structure will be out of whack.

Working with bad data is always a false economy. It compromises every business operation, costing you time and effort and degrading your ability to plan and manage. Data quality management is like a site foreman, ensuring that your pyramid will last.

If you need advice on building your own pyramid from the ground up, contact DQ Global today.

With over 25 years’ experience in the data quality industry, we have worked with countless organisations to build solid data foundations.

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Written by Martin Doyle

Martin is CEO and founder of DQ Global, a Data Quality Software company based in the UK. With an engineering background, Martin previously ran a CRM Software business. He has gained a wealth of knowledge and experience over the years and has established himself as a Data Quality Improvement Evangelist and an industry expert.