Archive for the ‘Pre-emptive Data Quality’ Category

Data Quality: Is Right First Time the Right Way to go?

Monday, January 25th, 2010

The new KPI (key performance indicator) for data quality is “Percent Right First Time”. Organizations have recognized that strategic data quality initiatives should focus on avoiding data errors rather than fixing them.

Right First Time is the core philosophy underlying Clavis Data Quality Steward and so it’s something that we talk about on a daily basis – but the other day an experienced data quality practitioner challenged me on whether Right First Time was  really a realistic objective. He questioned it on two levels: Firstly, can data be defined as ‘right ’ or ‘wrong’. After all isn’t it true that data can be right for one use case, but wrong for another. For example a product length expressed in millimetres to two decimal places may be right for use in a European sales application, but wrong for use in a US engineering application where inches may be more appropriate. Isn’t the proper measure of data quality “fit for purpose,” he asked. (more…)

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Data Quality in the Supply Chain

Monday, January 18th, 2010

I just picked up on a recent blog by Gartner Analyst Andrew White titled “The Importance of Data to Supply Chain – Master Data and All That” – actually it’s from a while ago but I’ve been busy. He was commenting on an interesting case study presented by Kristen Daihes, Wrigley’s Senior Manager of Global Sourcing, at last year’s IBM supply chain management event in Chicago, IL. The case study described the use of IBM’s LogicNet Plus to help with strategic supply chain network design issues.

A couple of things struck me in the piece, one was the fact that Wrigley apparently spent 60% of the process/project time “identifying, collecting, and validating data” – as Andrew points out the time spent on data was essential to avoid the problem of “garbage in, garbage out”. (more…)

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Preventative Data Quality: Vaccinating against the virus

Tuesday, November 3rd, 2009

All over the world governments are hurriedly stockpiling quantities of vaccine against the H1N1 Virus. They are doing this because the medical profession understands that when it comes to stopping viruses, prevention is by far the best strategy. The benefits of disease prevention – over cure – are many:

  • There is no recovery time for people who are vaccinated so the economic cost for a country in terms of lost work days and medical treatment is largely eliminated.
  • The risk of serious illness or death from the H1N1 virus is very real while there is negligible risk associated with the vaccine.
  • If the vaccine strategy is executed effectively, i.e. at the earliest time, then the spread of the disease will be contained in smaller areas and the number of people affected in any way will be minimized.

    • There are very strong parallels in the world of data and data quality:

  • The economic cost of “recovery time” for people who contract a virus is equivalent to the cost of rework or fixing bad data once it has entered a business’ IT system. These costs can be huge.
  • Ok it would be very rare or, though not unknown, for people to die from bad data, but the risk of business failure is high. While catastrophic failure of a business due to data quality is thankfully also fairly rare, almost all companies today suffer from multiple ongoing, day-to-day problems and inefficiencies caused by low quality. It’s a bit like a persistent cold which stops your business performing at its best.
  • The parallels between the spread of a virus through a community and the spread of bad data through a company’s IT systems are also very strong. One of the big challenges with data is a single data error in a single system can quickly become multiple data errors in multiple systems. And as more systems are connected and integrated for real time data sharing this problem grows exponentially.

    • View bad data as millions of potential viruses threatening your business – and work on your prevention strategy.

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