Designing a commercial model for data
By Jake Verma, Digital & Data senior consultant
Energy Systems Catapult (ESC) has been promoting the greater use of data and digitalisation since the launch of the Energy Data Taskforce (EDTF) in 2018.
Since then, there has been a huge amount of progress across the energy sector to open data so the UK continues towards its net-zero targets. The presumed open principle and open data triage provide a robust framework for assessing the risks associated with publishing data, but the value of data to organisations is a different matter and is worth consideration in its own right.
In this article I’m going to discuss some of the ways that the Catapult opened data, has triaged data to make it safe for sharing and developed commercial models for data.
What is the value of data anyway?
A phrase that has now become cliché is that “Data is the new oil”, but in my view, this has been widely misinterpreted. Coined by David Humby in 2006 to describe the fact that “unrefined, it cannot really be used”, a 2017 article in the Economist compared the massive growth of tech companies to the early 20th century emergence of oil monopolies.
In this article, data are not described as raw materials to make products and services out of, but simply a comparable source of great commercial value to a few large firms. We have often found, however, that organisations do treat their data like barrels of oil, although they understand there is value to be created from products, they are keeping them in storage until the price is right!
In fact, we think that the Open Data Institute’s definition is more accurate, that data is infrastructure that requires maintenance and investment. Data is more analogous to a road network, it increases in value from connections to other types of data and information and we can get different utility and insight by mixing it with other data sources.
This poses a question; how can we measure the value of data to an organisation? Data is economically nonrival, which means two people can have it at the same time with no detriment to either of them. In a zero-sum view, this can lead to an attitude that giving away data may be giving unrealised value to a competitor.
This may not always be appropriate considering privacy, commercial interests and security considerations, but it does alter the way in which data are treated. We discovered during EDTF that organisations tended to hoard data as if it were a valuable physical commodity, rather than treating it as a nonrival part of infrastructure.
Whilst it is true that there are costs associated with renting server space to host data, labour costs associated with collecting and cleaning the information, and costs associated with maintaining data portals, these are not often factors in assessing the value of data. The real value is in how that information is leveraged to create insights that you can act on, but the problem with hoarding data and not maintaining it (or using it) is that the value is unlikely to be realised by anybody.
ESC’s Data Platform
In 2019 ESC procured USmart as their strategic data platform to live our values and promote the principles of EDTF. ESC holds many different interesting and useful datasets from a range of projects, public and private, across a range of funded schemes. We’ve been working on making these datasets available for the innovation, academic, and policy community in a number of different ways. The key datasets discussed here are the Living Lab datasets, the Consumers Vehicles and Energy Integration trial data, and the Electrification of Heat programme data.
Here’s a quick introduction to our data platform that I did for colleagues earlier this year:
You can access the data platform here.
When we started to upload data to the platform, the first thing we had to do was an open data triage on all of the data that we were going to publish. The open data triage is part of the Presumed Open principle and can be used for any dataset! This is a walkthrough of the open data triage process if you’re unfamiliar with it, it’s part of Energy Data Best Practice.
As you can see from the walkthrough, there’s objective justification for commercial interest which you can use to apply your own business model where the data collection isn’t part of a regulated activity. How you decide on the commercial case for data is up to you, but considering the economically non-rival nature of data discussed above and the unrealised value from keeping the data closed we have settled on a number of principles when considering the commercial model.
- Data is of most value when shared and mixed with other data
- Data that is derived from publicly funded projects should be open
- If data is being maintained, costs should be recouped
- The intellectual property used to create the data should be considered
You can access a copy of the Open Data Triage Canvas here.
Living Lab case study
The Living Lab winter trial data was the first dataset to be considered. There is a very large amount of data available from the trial that we determined would be useful for SMEs or innovators looking for insight into domestic heating behaviours. There was an added value to this in that the data would also demonstrate what kind of information an organisation wanting to run a trial in the Living Lab would have access to. The original Smart Systems and Heating project received substantial public funding and there was already a precedent set for the publication of data. Finally, the data was being held in a production server that was to be decommissioned, previously the data from the trials had been archived and it made sense in this case for the archiving to be on a data portal where it would be more accessible.
You can hear more about the Living Lab here:
You can learn more about the Living Lab here.
You can download the Living Lab data here.
CVEI case study
The Consumers, Vehicles and Energy Integration (CVEI) project was run from the Energy Technologies Institute with a range of partners. In this case, there was significant intellectual property associated with the development and running of the project, but we also determined that there was significant value in the data for the transport sector. There is very few large-scale electric vehicle (EV) datasets available and this dataset could be particularly valuable to promote the uptake of EVs and to understand the potential of smart charging. For these reasons, when we ran the open data triage we settled on a tier system, that some data would be public and some would have a set of value-added services attached to them that would allow the transport team to provide a range of additional insights to interested users.
You can hear more about CVEI and the data here:
You can learn more about the CVEI project here.
You can access the CVEI data here.
Electrification of Heat case study
The Electrification of Heat (EoH) project is a BEIS funded programme focussing on the heat pump consumer journey; as a publicly funded project, it was an ideal candidate to undergo an open data triage to prepare data for a wider release. The companies involved in the programme have submitted the data to USmart and ESC have been using the data in their role as management contractor. We engaged with programme partners and industry experts to outline use cases for the data that were then used as the basis for the triage process. The resulting data will be published imminently, providing data and vital insight for innovators and researchers in the decarbonisation challenge.
You can hear more about EoH here:
As we have demonstrated, there is no one size fits all policy for how you treat data. We have started out with an open data triage alongside a set of publication principles and worked through the data on a case-by-case basis, always considering that data is more useful when combined with other data than sat in a closed database! You must think about what value the data could be to someone else in the sector, think about the users and their needs, and think about how you can get value back. In the Catapult’s case, we have used the open data triage to understand the risks, but also made it a clear objective to publish data that we think would be valuable to the sector to get closer to net-zero.