Ideas for justifying your data quality existence

Conference season is hotting up in the UK, and there are no doubt lots of practitioners putting the finishing touches to their data quality presentations.

One interesting observation I’ve encountered is a high churn rate amongst data quality professionals, particularly within the leadership community.

Their decision to quit is not always voluntary.

Many data quality presenters focus on the tactics of delivering a data quality project, but fail to explain what the organisation gained in terms of tangible business benefits. Whilst the presenter may just be aiming to impart their knowledge to a captive audience, I suspect a lack of robust performance measurement is often at fault. Read More »

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Treat your data steward like a rock star

Every day of the year, there's a holiday celebrating one thing or another. In fact, you probably didn't know that Oct. 22 WAS CAPS LOCK DAY. Whoops. Or, if you're like me, you completely spaced on Oct. 26. It was Mother-in-Law Day. Boy, we'll be hearing about that for the next few months.

A few years ago, some of us in the data management community noted that there was an under-appreciated group that deserved more recognition. That's why we started International Data Stewards Day – a once-a-year celebration of the people who care about, obsess about and fuss with the data that drives your organization.

This year, it's back – and bigger than ever. The theme for the 2014 International Data Stewards Day is that data stewards are the rock stars of your organization. OK, maybe they're not larger-than-life characters who command the stage and play extended guitar solos. Read More »

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Data-based television

This isn't 1950. Half of the population is not crowded around a TV at night watching three shows. For a long time now, traditional TV networks have been struggling. This is no blip. More people than ever are cutting the cord. Traditional media outlets are scared—and they should be.

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Big data and omission neglect

In my previous post, I used the book Mastermind: How to Think Like Sherlock Holmes by Maria Konnikova to explain how additional information can make us overconfident even when it doesn’t add to our knowledge in a significant way. Knowing this can help us determine how much data our decisions need to be driven by.

Another important concept Konnikova described is what is known as omission neglect.

“We fail to note what we do not perceive up front,” Konnikova explained, “and we fail to inquire further or to take the missing pieces into account as we make our decision. Some information is always available, but some is always silent—and it will remain silent unless we actively stir it up.” This is why noise is sometimes necessary. Read More »

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Master data management – models, time and synchronization

I thought it might be worth taking a short break from discussing metadata and instead cycle back to an idea that has been challenging me recently in a few of our current MDM consulting engagements. I have been examining patterns for master data use, and one of the common recurring themes is the pattern in which a master index is used to establish a system of reference among enterprise systems of record containing data for a master data entity.

For example, consider a master domain for vendor data. In this case the master data model for an index/registry may contain some number of data attributes that are either necessary for unique identification or have been selected as common data attributes that are shared frequently among business application consumers. There may be areas of the business that manage sources of record such as the Credit business function, the Finance and Accounting business function, and the Fulfillment business function. Read More »

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Creating sustainable change in analytics-driven organizations

What is it about change that seems so difficult?  Even if we are considered a flexible, creative or adventurous person, a first reaction is often to push back at change imperatives forced upon us by others.  Part of this is probably a sign of the times. Self-determination is something that most of us feel is a fundamental part of our character.

Certainly, this sense of the individual is a key concept that the budding "millennial" generation feel is a key part of their rights as professionals and as adults. Yet, to work as part of a larger organization, which has its own principles and governing structures (not to mention politics), we all need to sacrifice what we deem to be “ours” for the greater good.  In other words, just because you've always done things your way doesn't necessarily mean it is the best way.

To be a truly analytics-driven organization means to be willing to interpret data collected as part of ongoing business processes with a degree of objectivity.  Sometimes the data or the trends reveal unpleasant truths about business practices and productivity.  Accepting analytic truths often means that employees need to rethink some of the built-up comfort levels that have been developed (and refined) over time. This also leads to changes in the organization to respond to new findings. Read More »

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Data quality - one dimension at a time

I was recently asked what I would focus on given limited funds and resources to kickstart a data quality initiative.

This is a great question, because I’m sure many readers will find themselves in this position at some point in their career.

My answer is to become ruthlessly focused on managing one data quality dimension - completeness. Read More »

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On data, breaches, names and faucets

A few months ago, I wrote a piece on this site about generic error messages and how they reflect an organization's data-management practices. I believe that they say quite a bit about how an organization values data management and, more generally, data.

In the post, I skewered Enterprise Rent-A-Car. Make no mistake though: plenty of companies communicate in a way that makes customers question the extent to which they value data.

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Big data and the treadmill of overconfidence

In her book Mastermind: How to Think Like Sherlock Holmes, Maria Konnikova discussed four sets of circumstances that tend to make us overconfident:

  1. Familiarity — When we are dealing with familiar tasks, we feel somehow safer, thinking that we don't have the same need for caution as we would when trying something new. Each time we repeat something, we become better acquainted with it and our actions become more and more automatic, so we are less likely to put adequate thought or consideration into what we're doing.
  2. Action — As we actively engage, we become more confident in what we are doing. In one study, individuals who flipped a coin themselves, in contrast to watching someone else flip it, were more confident in being able to predict heads or tails accurately, even though, objectively, the probabilities remained unchanged.
  3. Difficulty — We tend to be under-confident on easy problems and overconfident on difficult ones. This is called the hard-easy effect. We underestimate our ability to do well when all sign all signs point to success, and we overestimate it when the signs become less favorable.
  4. Information — When we have more information about something, we are more likely to think we can handle it, even if the additional information doesn't actually add to our knowledge in a significant way. Read More »
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Reference data harmonization

We have looked at two reference data sets whose code values are distinct yet equivalently map to the same conceptual domain. We have also looked at two reference data sets whose values sets largely overlap, though not equivalently. Lastly, we began the discussion about the guidelines for determining when reference data sets can be harmonized. In this last post of this month’s series, let’s look at some practical steps for harmonization. Read More »

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