When data isn't sticky enough to keep customers

A few years ago, at the urging of my accountant, I switched from a single-person LLC to an S corp. Sure, I'd have to do my own payroll from that point forward, but the tax benefits easily justified the move. Every quarter, I would now process payroll for all Simon, Inc. employees—and, by that, I mean yours truly.
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Data steward is a tough role to play

In my previous post I explained that even if your organization does not have anyone with data steward as their official job title, data stewardship plays a crucial role in data governance and data quality.

Let’s assume that this has inspired you to formally make data steward an official job title. How should you go about finding good candidates for such an important role? You could take inspiration from some of the examples noted in the bestselling book Rework by Jason Fried and David Heinemeier Hansson. Read More »

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Master data access use case #1: The unique record

Last time I suggested that there are some typical use cases for master data, and this week we will examine the desire for accessibility to a presumed “golden” record that represents “the single source of truth” for a specific entity. I put both of those terms in quotes because I think they are both mistaken. A record that is cobbled together by pulling data values from an assortment of sources to create a record that is inconsistent with almost all of the sources could hardly be called “golden.” The interpretation of what is (or is not) “truth” is based on the use of the data, and it would be presumptuous for an IT-project to dictate what is to be considered the truth.

That being said, the desire for a unique representation of a master entity remains, and there are reasonable expectations that any application’s search for an entity’s information will return one and only one record. This is particularly true when the master domain is employed as part of a reporting or analytics activity in which queries are aggregating values associated with each unique entity. Read More »

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Cracking the code to successful conversions: Prototype or not!

To perform a successful data conversion, you have to know a number of things. In this series, we have uncovered the following about our conversion:

  1. Scope of the conversion
  2. Infrastructure for the conversion
  3. Source of the conversion
  4. Target for the conversion
  5. Management for the conversion
  6. Testing and Quality Assurance for the conversion
  7. Governance and stewardship requirements
  8. Data management standards and guidelines
  9. Technology for the conversion
  10. Target security requirements
  11. Data requirements

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The causal link between education and data quality

Here on the Data Roundtable we've discussed many topics such as root-cause analysis, continual improvement and defect prevention. Every organization must focus on these disciplines to create long-term value from data quality improvement instead of some fleeting benefit.

Nowhere is this more important than the need for an appropriate education strategy, both in relation to data quality and the underlying systems and local policies. It is an area that often gets ignored in the quest for technological advances, so I wanted to recount a simple story that outlines the importance of factoring education into your root-cause discovery. Read More »

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Mission impossible: Understanding healthcare

Imagine that an employee in your organization transferred from one department or division to another. Say, Sarah in Finance took a job in Marketing.

Behind the scenes, a clerk in HR or a manager (via employee self-service) would process the transaction. Sarah would report to her new desk and manager without really missing a beat. Put differently, no one has to rekey in Sarah's entire vacation, benefits, job, and payroll histories. From a data perspective, her transfer is simply a matter of changing her current department from Finance to Marketing while allowing her history to be maintained.

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Demonstrating master data accessibility

I have probably touched on this topic many times before: accessing the data that has been loaded into a master data environment. In recent weeks some client experiences are really highlighting something that is increasingly apparent (and should be obvious) for master data management: the need to demonstrate that it really works. Read More »

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Cracking the code to successful conversions: Enterprise data modeling

There are multiple types of data models, and some companies choose to NOT data model purchased software applications. I view this a bit differently. I think that any purchased application is part of our enterprise, thus it is part of our enterprise data model (or that concept is part of the enterprise!). An enterprise data model is very conceptual in nature, and it relates to how a company does business.

Most enterprise data models are split up by subject areas.  For example, Customer, Product, Facilities and Sales would each have their own model. The relationships between those subject areas are shown in a diagram, so that it is easy to see and understand. Read More »

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Looking at application procurement through a data quality lens

When you examine where most data quality defects arise from, you soon realise that your source applications are a prime culprit.

You can argue that the sales team always enter incomplete address details, or the surgeons can't remember the correct patient type codes but in my experience the majority of issues stem from underlying flaws in application design.

The challenge is that business models are constantly evolving. One year Amazon is selling physical books and DVD's, the next year it's e-books and streaming video. You can bet they've had to adapt their information systems radically to cope with the huge shift in their business model.

And the same is happening in your business, all the time. It may not be as profound a shift as Amazon, but the shifts are there all the same, and they're putting pressure on the quality of your underlying data. Read More »

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