Interoperability - the often overlooked sibling in the FAIR data family
Updated: Nov 6
On the 11th December 1998, a Delta 7427 launch vehicle
lifted the NASA Mars Climate Orbiter on its journey to study the Martian atmosphere and landscape. Nine months
and eleven days later, as it approached its destination, it burned up in the atmosphere and crashed into Mars, turning anticipated celebration into bereavement for the engineers and teams who had work
ed on the project for the past three years. And the cause? Someone used the wrong units of measure.
Well, the tabloid headline would have you believe that "Someone used the wrong units of measure.", but that's like saying driving on the left-hand side of the road is the "wrong" side of the road. What happened is that the navigation team at the Jet Propulsion Laboratory (JPL) used the metric system in its calculations, while Lockheed Martin Astronautics in Denver, Colorado, which designed and built the spacecraft, provided crucial acceleration data in imperial units.
Two important insights arise; firstly that at least two systems of measurement exist and, like driving on the left or the right side of the road, the systems in which they operate work fine. - Traffic flow and driving perform just as well in France where they drive on the right, as it does in Japan where they drive on the left. The second insight is that when these systems do interact then, how they interoperate, has to be given the highest priority. Without this, mistakes can be very expensive, and could even be fatal. Imagine the problems that could happen if designers hadn't considered how vehicles will exit the channel tunnel or ferries as cars arrive in a traffic system that uses the other side of the road.
The data in the Mars Climate Orbiter is the lifeblood of its decision support systems. And telemetry decisions for orbiting assumed that those data were compatible. When it comes to data integration there are two architectural approaches, which tend to emerge. The first approach where some can easily envisage a single standardised world where all participants in a system follow the same rules. Say, we all use metric units, or we all drive on the right, and no other options are allowed. That way we don't have to worry about conversions, and all the effort and expense that goes with it. This works well if you have ultimate control over all the domains that work within a system, a sort of benevolent dictatorship. The other approach is one which anticipates collaboration amongst systems. It accepts that different players may have different ways, means, and concepts within their data systems and our systems architectures enable diagnostics and decision making to happen easily in the heterogeneous environment of collaboration. Essentially one can direct data architecture efforts at creating uniformity or at enabling interoperability. In the case of the Mars Climate Orbiter acceleration calculations, neither happened and so it crashed and burned.
Providing sustainable solutions for the farmer requires data to cross boundaries between collaborators, companies, and countries. Operating in such a diverse ecosystem where players have established data systems that cannot be changed gives few, if any, opportunities to take up the first approach of benevolent dictation of data standards.
An approach where the agri-food sector anticipates collaboration across systems managed by different players allows us to design into our data systems the ability to connect information and knowledge across organisational, cultural and technical boundaries. An example of this approach arose from the demise of the Mars Climate Orbiter. NASA recognised that anticipating the need for interoperability was critical in their operating model and commissioned an ontology to describe the sets of concepts for Quantities, Units, Dimensions and Types (QUDT) that are known and used throughout the world. The ontology describes not only what these concepts are but how they relate to each other and can be converted. openPHACTS, a pharmaceutical collaboration, was one of the first industrial collaborations to make use of QUDT outside of the space industry.
Ontologies that describe relationships between our business concepts (in the same way that we can describe the relationships between millimetres and inches) are a fundamental part of a capability in data interoperability, and enable our ambition for FAIR data for growers. Tools like QUDT help us deal with the increasingly diverse data we come in contact with to achieve FAIR data for agri-food.