Nate: So let's say you have clean commodity business data, you did that leg work. What are the possibilities?
Laurent: Quite a few.
Most importantly, you must define exactly what problem you want to solve. That is something AI is not (currently) capable of doing for you.
However, looking at we already do for clients. We provide global insights on purchases and sales on a daily basis. … stocks, derivatives, hedging, market data, mark to market, plant performance and so on. All using clean, normalized harmonized data. So not only is this a valuable business function… I call it an ‘AI enabler.’
And the longer a client uses RadarRadar, the more clean, historical data you end up having. This sets you up very well to train a model.
Nate: And those possibilities?
Laurent: Well to start we’re really not that far off from a client being able to ask, “what is my soybean position in Brazil” and get an answer. But we see a lot of potential in ‘outlier detection’ as well. Where the model would flag a trade or contract that doesn’t fit for some reason -> possibly alerting clients to either a mistake or a trend of some kind.
Building on that, we could possibly enable more complex chat queries like “Is my position off?”, “What is the performance of this desk vs another desk? Or how effective were our hedges?”
Nate: Ok, so you two have laid out clear reasons for this mental gap between data management and AI and that AI is essentially inevitable for this industry. What about in the near term? Is data management a long term investment only?
Norbert: What is interesting about AI/ML’s future applications in commodities is that it pays to invest in the infrastructure today. It’s that rare win-win where future proofing also gives you a distinct competitive edge today. Data management alone has a sizable ROI day-to-day… the moment it’s implemented well. So, in a sense investing in this theoretical future has tangible benefits immediately.
Proper data management is a no-brainer for any company, small or large, in any industry. More acutely so for commodities given the complex nature of that industry. The current status quo of a messy data lake or no lake at all is very expensive.
It takes mountains of labor to paint a picture for one – multiple Full Time Employees – And its error prone. Plus, it’s not standardized across orgs. Different controllers, risk managers, traders use different tools, models, etc… There is no clarity…and it’s expensive, slow, and often wrong.
Laurent: I would like to put a long tail perspective on that point Norbert made. Good data management will reduce manual workload right away, but the potential for automating countless time-consuming manual tasks specifically in terms of deep analytics in the future is much greater. The AI revolution that we're on the cusp of is exponential.
Nate: So this isn’t like if a person started to go to the gym, working hard today for a result in the future. There is instant gratification if you get on top of your data management that also happens to set you up for AI in the future.