The Myth of Primary Data

Any sufficiently advanced technology is indistinguishable from magic - Arthur C Clarke

There is a common understanding that the major obstacle to widespread adoption of lifecycle measurement is the lack of quality primary data. And some sustainability consultants and academics over the past 5 to 15 years have sought to perpetuate this thinking for one overwhelming reason – it helps maintain the mystery and magic of lifecycle measurement that keeps the customers calling and the revenue flowing. (It needs to be said, that not all consultancies or academics are so single-minded and many are seeking more practical and affordable ways of doing lifecycle analysis by using tools like Carbonostics).


Primary data – data that is measured and gathered in-person and on-site – is often considered the holy grail of good quality lifecycle data. The argument is easy to understand - how can a company truly know their environmental footprint if they don’t measure the impacts of every material and process in their lifecycle? However, the accuracy of primary data is a closely guarded myth that some consultancies and academics have conspired to prolong. And, like all good myths, this one is born out of - and fed by – fear. In this case fear of saying something not quite accurate that could damage your company or brand reputation. Unfortunately, companies have inadvertently perpetuated the myth by handing over their business to these experts in exchange for dozens of impressive-looking spreadsheets with hidden formulae and pages of data in teeny tiny fonts that are very intimidating and can be fiercely defended by empirical evidence - just in case anyone ever asks.

The truth, however, is much easier to understand and much less costly. The majority of lifecycle analyses and carbon footprints being measured and published contain a large amount of secondary data – i.e. data that has been derived from averages, statistical projections, and spot-checked primary sources. For example – if one of your raw materials is delivered on a train from Germany, do you need to go to find that train and measure its environmental impacts or can you use data published about the average impacts of trains in Western Europe (or specifically Germany if that data is available). And consider, for example, the water footprint of a wheat field. Is it more accurate to measure the rainfall and irrigation needs of the wheat grown at the top of the hill or the bottom of the hill? Or, is it more accurate to take an industry average of wheat production in that region/climate/agricultural type.

All consultants and academics in the LCA field know that secondary data is relatively abundant and can be of very high quality. Additionally, a few of the larger established lifecycle consultancies and universities have done enough primary data gathering (getting their unsuspecting clients to pay for their time and effort) to develop proprietary databases of their own so that they can now combine with other private and public data sources to derive solid data models that can be applied to new lifecycle measurement studies. And as many will tell you, realistically less than 10% of data needs to be gathered firsthand to calculate accurate and defendable environmental impact measurements if the goal is to publish the data. (If the goal is for internal business knowledge and resource allocation decisions, primary data becomes even less relevant).

And let’s not forget, that there is no doubt that the biggest benefit of doing lifecycle analysis for any company is not in the PR of publishing a number on a product label or in a press release – it is much more often the big ah-ha moments that occur when examining the entire lifecycle of a product, process or business. Why are we buying and storing so much of this raw material? Because of the deal worked out 5 years ago when commodity prices were a lot lower? Why are we spending so much time and resources trying to reinvent the nature of plastic? Because we’ve been assuming that packaging is our biggest environmental impact. It is the inherent examination of the lifecycle that often yields the most illuminating results – and cost and waste savings too. Remember, more often than not, lifecycle analysis is a revenue-generating activity!

So, let’s bust the myth and confidently espouse the value and accuracy of peer-reviewed and audited secondary data in our lifecycle analyses. Let’s demystify the process of lifecycle measurement by choosing methods and tools that are transparent, intuitive, and flexible. Let’s boldly embrace practical and affordable lifecycle analysis as a smart business practice. And let’s require all our consultants and tool providers to meet these standards and not act as if lifecycle measurement is simply magic.

Onward and Upward!