How to extend the completeness dimension

If you’re involved in some way with data quality management then you will no doubt have had to deal with the completeness dimension.

This is often one of the starting points for organisations tackling data quality because it is easily understood and (fairly) easy to assess. Conventional wisdom has teams looking for missing values.

However, there is a problem with the way many practitioners calculate the completeness of their datasets and it relates to an over-dependence on the default metrics provided by software. By going a little further you can deliver far more value to the business and make it easier to prioritise any long-term prevention measures required. Read More »

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In defense of the indefensible

"Data doesn't matter. I know what I know."

It's a refrain that we've heard in some form for years now.

Some people want what they want when they want it, data be damned. It can be very tough to convince folks who already have their minds made up, a point that Jim Harris makes in "Can data change an already made up mind?"

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Measurement and disestimation

In his book Proofiness: How You’re Being Fooled by the Numbers, Charles Seife coined the term disestimation, defining it as “the act of taking a number too literally, understating or ignoring the uncertainties that surround it. Disestimation imbues a number with more precision that it deserves, dressing a measurement up as absolute fact instead of presenting it as the error-prone estimate that it really is.”

Are you running a fever?

You know, for example, that normal body temperature is 98.6 degrees Fahrenheit (37 degrees Celsius). Using the apparent precision of that measurement standard, if you take your temperature and it exceeds 98.6 degrees, you assume you have a fever. But have you ever wondered where that measurement standard came from? Read More »

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Reference data lineage

There are really two questions about reference data lineage: what are the authoritative sources for reference data and what applications use enterprise reference data?

The criticality of the question of authority for reference data sets is driven by the need for consistency of the reference values. In the absence of agreed-to authoritative sources, there is little or no governance over the sets of values that are incorporated into different versions of the reference domains. The impact is downstream inconsistency, especially in derived information products such as reports and analyses.

For example, reports may aggregate records along reference dimensions (especially hierarchical ones like product categories or geographic locations). If there are different versions of the hierarchical dimension data (sourced from a reference domain), there will be differences in the derived reports, potentially leading to confusion in the boardroom when the results of those reports are shared. Read More »

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How to improve your data quality history taking

Whilst it’s nice to imagine a world of perfect data quality the reality is that most organisations will be dealing with data quality defects on a daily basis. I’ve noticed a wide variation in the way organisations manage the life cycle of defects and nowhere is that more apparent in the initial information gathering exercise that initiates the start of the data quality improvement cycle. Read More »

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Facebook and the myth of big data perfection

When it comes to using Big Data, Facebook occupies rarified air along with Amazon, Apple, Netflix, and Google. It's a point that I've made countless times before in my talks, books, and blog posts. But does that mean that the company has perfected its use of vast troves of mostly unstructured data?

Hardly. Read More »

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The Chicken Man versus the Data Scientist

In my previous post Sisyphus didn’t need a fitness tracker, I recommended that you only collect, measure and analyze big data if it helps you make a better decision or change your actions.

Unfortunately, it’s difficult to know ahead of time which data will meet that criteria. We often, therefore, collect, measure and analyze any data that might help us achieve our goals. Along the way we sometimes make dubious connections between the results we are seeing and the data we are analyzing, leading us afoul of smart thinking. Read More »

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Achieving persistent data governance, pt. 3: find a visionary

In the first two parts of my series (part 1 and part 2), I talked about common ways data governance projects can (and do) fail and offered collaboration between teams as a key to achieving success.

In this post, we’ll examine the importance of realizing the full value of your team members. With just a little thought and effort, you can leverage your team’s existing talents to ensure your project’s success.

Strategy #3: Select a data governance leader who has a long-term vision and who can execute that vision in a step-wise fashion while delivering value to the business at each increment. Read More »

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Re-thinking the design choices of application data quality

If we look at how most data quality initiatives start, they tend to follow a fairly common pattern:

  • Data quality defects are observed by the business or technical community
  • Business case for improvement is established
  • Remedial improvements implemented
  • Long-term monitoring and prevention recommended
  • Move on to the next data landscape

Ok, I know not all projects follow that path but for most projects there is a definite sense of resolving the issue many years after the application was originally conceived. Read More »

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