Cleaning up Dirty Data: Mundane Chore or Potent Accelerator?

by Ian Quest, QR_ director of consulting

Where there’s muck there’s brass

For me, this phrase applies to many of the activities ensuring engineering programmes have complete, clean and current data to build on. A few apparently mundane and unglamorous activities, when done rigorously, hold the key to significantly accelerating and de-risking a programmes and unlocking the real potential of your engineers and creatives.

How are you measuring your data quality?

Typically, 20–30% of parts have errors at point of order

In a sample from across a number of both large and small automotive OEMs, we found that on average, at the planned ordering point for parts for prototypes or early vehicles, 20–30% of the parts have data errors or omissions, most of which will impact their on-time delivery.

To be clear, this isn’t that engineering work hasn’t been completed or there are unresolved technical issues. Instead, this is necessary product and programme data which is either absent or does not reflect the engineering and/or business intent. In a BoM of 2,000 parts, this means 400–600 parts with errors, many of which will result in late or non-available parts to build.

The secret weapon

How do you know how thorough your data validation is?

The Wholesale Clean-up

Millions have been spent trying to fix this with the majority of the spend in two areas; system upgrades and administrative support. Both of these are ‘big’ solutions which, from a birds eye view, look appropriate for such a big and widespread issue.

In truth this problem, which exists in the detail, must be tackled in the detail to have any real and lasting impact. We have seen many different error types, each of which has multiple causes and only some of which are materially affected by a system update or change to someone’s R&Rs.

Building Solid Data Foundations

1. Understand and believe in the power of good data. Unless you believe it is important, you won’t convince anyone else. You need to experience what happens when you have good data vs bad. Try offering the best possible support to one function group and see how they fare versus the others. Do some detailed analysis on a selection of the parts which were late to build and map how better, more visible data might have changed your trajectory

2. Measure Data Quality and add it to page 1 of your dashboards. Very few organisations do this well and are left either drawing conclusions from information of unknown quality or simply not trusting the data and are left guessing. As an analogy, you need to know the cut, colour and clarity of your diamond, not just the carats.

3. Set your target level of data quality based on the true costs and benefits. Once you understand data quality and its consequences, set targets on quality and track it as you go, ensuring it remains at an acceptable (and trustable) level and applying increased efforts whenever it isn’t.

4. Simplify and automate. There are many tools and processes which will help make this process faster and more effective. Routines, automated comparisons, regular dip-checks and the ability to home in on the areas most likely to have issues will all make the process easier. Just beware dumbing it down too much. Once it’s a mindless routine, you risk the ‘impartial, intelligent eyeballs’ failing to catch the big non-routine mistakes.

Foundations for the future

I’ve been asked a few times recently how programmes can be managed in a more agile way; an important question with so many major technological challenges requiring solutions quickly. The full answer is a long one with a number of options and nuances, but one thing is absolutely sure: you will never be able to be agile if you don’t have up to date data you can trust.

Ian Quest is director of consulting at Quick Release_.

Based in London, Ian operates internationally as he leads QR_’s growing consultancy arm. His focus is on unlocking competitive advantage by bringing products to market faster and more efficiently. An early career in aerospace engineering led to senior leadership roles with several prominent manufacturing consultancies, culminated in the directorship of Newton Europe’s Air, Land & Sea business. Ian joined QR_ full time in 2017, having previously provided non-executive advisory to its founders.



We’re product data professionals helping automotive companies get better products to market, faster, to meet the growing demands of the world.

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Quick Release

We’re product data professionals helping automotive companies get better products to market, faster, to meet the growing demands of the world.