On Bloatware and Data Quality

Mar 21, 2013 by Phil Simon

I often think of the reasons that so many organizations suffer from such poor data quality. To be sure, many of the culprits are internal–careless employees, broken business processes, cultures that don’t value data, decisions routinely based upon hunches in lieu of information, and the like. Still, I’d argue that many software vendors should probably receive at least some of the blame.

Big Data and My Simple View of the World

Feb 28, 2013 by Phil Simon

For a while now, I’ve had this working theory of the business world. In short, there are three types of people: 1) those who get it; 2) those that don’t get it but want to get it; and 3) those that don’t get it and don’t want to get it.

The Politics of Data Whistleblowing

Aug 09, 2012 by Phil Simon

In his recent post The Unsung Heroes of Data, Jim Harris explores the role of the potential data whistleblower. He compares whisteblowers to data stewards. The latter are:

Why Doesn’t My Data Listen to Me?

Apr 19, 2012 by Phil Simon

One of my favorite Porcupine Tree songs is “I Drive The Hearse.” Here are some of the song’s haunting lyrics:

ERP and Data

Sep 08, 2011 by Phil Simon

The more things change, the more they stay the same.

– Alphonse Karr (1808-90)

The Indian and the Arrow

Apr 21, 2011 by Phil Simon

When asked recently about his new putter a few hours before teeing off on Sunday, professional golfer Matt Kuchar responded, “It’s usually the Indian, not the arrow.”

One could write a book about that statement.

Data Quality Incentives, Part II: When Incentives Lack Teeth

Jan 20, 2011 by Phil Simon

In my previous post, I discussed whether organizations utilized sufficient positive and negative incentives with respect to data quality and management. While there are exceptions, the vast majority of companies could do a much better job handling employee carrots and sticks.

In this second post in the series, I’ll address what happens to organizations that lack sufficient data management and quality incentive structures throughout the organization.

Returning to the Scene of the Crime

Nov 04, 2010 by Phil Simon

This week marks an interesting anniversary for me. One year ago, I started a consulting assignment for a fairly large hospital implementing an ERP system. This was, quite frankly, the most dysfunctional organization with which I have ever worked – and I’ve seen more than my fair share. On so many levels, this hospital had absolutely no business going live when it did. (For more on this, check out this link. It’s a long read but it’s like a traffic accident: you just can’t look away.)

In this post, I’ll return to the scene of the crime and provide a few status updates. If you take nothing else away from this post, remember this: data quality issues not addressed at the beginning of IT projects exacerbate ten-fold after ill-advised system activations.

Note that this hospital is anonymized as NEP in the case study. For the sake of consistency, I’ll keep that moniker.

Employee Data Quality Issues, Part III: Date Challenges

Apr 22, 2010 by Phil Simon

In the third part of my series, I’ll explore DQ challenges related to employee dates. Part I addressed employee addresses and Part II dealt with employee social security numbers.

I can’t count the number of times that I have seen a seemingly inexplicable error in an CRM or ERP system. I would poke around the system’s setup, run queries, and eventually identify the problem. More often than not, the issue would stem from an incorrect date. (Consultant’s tip: If in doubt, claim that it’s a date issue. You’re probably right.)

Now, allow me to geek out for a minute. When confronted with these issues, in the back of my head, I would usually think about the quote from Raiders of the Lost Ark, “Bad dates.”

Types of Employee Dates

How many employee dates might an system hold? Let’s look at a just the HR part of an ERP to keep the scope of this post manageable.

The answer depends on the application but most apps contain the following (or derivations thereof):

• Date stamps–e.g., system-generated times and dates reflecting when the data appeared in the application.
• Hire date
• Seniority date
• Anniversary date
• Last paid date
• Benefit eligibility dates
• Benefit begin and end dates
• Deduction start and dates
• Transaction dates for pay increases, promotions, etc.
• Custom date fields

I could keep going, but trust me: dates are huge. Note, however, that I’ve never seen an organization use every date field in an app.