Having just come back from a trip to the USA, we noticed many cultural differences which we had to get used to:

  • Use of the word ‘pavement’ to describe the surface cars drive on, whereas in England ‘pavement’ is the surface at the side of the road that pedestrians walk on (sidewalk in the US)
  • The majority of road signs being text whereas in Europe they tend to be symbols
  • Having to remember to add sales tax onto the price of items being purchased
  • Having to show ID to purchase alcohol, despite being well over 21

Amongst the many cultural differences, it was interesting to note some of the different approaches to recycling. Since waste minimisation, like good data quality, may take more effort than sending waste to landfill or providing poor quality data updates we need to ensure that people have the right motivation and awareness both to minimise waste and to maximise data quality.

Different approaches to waste minimisation provide a few useful comparisons of the approaches needed to improve approaches to data quality

Waste minimisation

We all recognise that we live on a finite planet, therefore, we should all be trying to ensure that the resources of the world last as long as possible.

The four R’s relating to waste minimisation are:

  • Reduce – Reduce the amount of resource used to the minimum possible
  • Reuse – When an item has finished being used, can it be reused. For example many countries have bottle reuse schemes. In Denmark, beer bottles are generally a uniform size to allow easy reuse
  • Repair – If an item no longer functions, can it be repaired to allow further use
  • Recycle – Separate waste to allow anything which can be recycled to be easily sorted and extracted

The final option is landfill, which effectively prevents any further use of the resources that have been discarded. In many countries the availability of suitable landfill sites is running out which can lead to greater encouragement to recycle and/or the use of incineration or other waster destruction technologies.

Recycling rates vary greatly between countries and sometimes within countries from almost 0% to over 50%, so clearly there are a number of different factors influencing recycling.

How to minimise waste?

The following are examples of waste management approaches I have observed in different countries and some thoughts on what they may teach us when looking at data quality improvements.

  • One US county we visited operated a system that allows you to put as much rubbish as you want into landfill for free, but if you want to recycle you have to take it to the council depot yourself and pay $10 for the privilege! If you repeat this approach to data, then there is a positive incentive to supply poor data and only the truly dedicated will correct data they find is wrong
  • One popular New York museum has a ‘Food Court’ where you can purchase a range of tasty food, mostly served in recyclable containers. There are many mixed waste bins around the area where you can throw away all your rubbish, however, it is only when you are leaving the Food Court (and will by then have thrown away your rubbish) that you come across the recycling bins. To make a process work, you have to have the right things in where they are needed – when processing data, is it easy to correct data and supply updates at the point of data entry, or is this a separate process?
  • Most plastic packaging is now marked with the type of plastic used in the container and local community schemes state the types they collect. Where we live in England, the council collect all plastic, so you don’t even need to find the symbol. One community in the US allow Type 1 plastic bottles to be recycled, but do not allow other types of Type 1 waste, e.g. salad containers. Again, when looking at what can be learned from a data perspective, make the process easy for everyone to increase recycling rates and make sure that you are not missing opportunities to improve data by making the update process too ‘narrow’
  • In Denmark we noticed that most of the beer bottles are the same size and shape and have a deposit on them. This provides a positive incentive to return bottles, and the uniform sizing means they can be reused far more efficiently. Do you provide a positive incentive to staff for correcting and updating data?
  • In Switzerland, any waste that goes to land fill has to be in a council approved plastic bag which has a tax paid on it to minimise waste. Anyone throwing waste away in unapproved containers is likely to find their waste examined to find addressed envelopes, for example, so that the waste can be returned to you! Would a financial disincentive or penalty work in your organisation to encourage good quality data input?
  • One project I am working on at the moment is helping to set up the London Reuse Network, a pan-London initiative to greatly increase the levels of resource reuse. The network will reduce the amount of resources going to landfill and similarly reduce consumption of raw resources. Taking a similar theme, how good is your organisation at reusing its data for different business purposes? Or do you waste time and effort collecting similar data each time that new need arises?

What other parallels between waste minimisation and data quality processes are you aware of?

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