Early on in Steven Spielberg’s seminal 2002 film Minority Report comes a now-iconic scene. Tom Cruise’s character – a Precrime police officer who prevents felonies before they happen based on foreknowledge provided by precogs – is investigating a soon-to-take-place crime in order to figure out its location. Donning magical gloves in front of a giant screen, he begins.
“Show me the crime scene,” he barks, swiping left. An image of the scene appears.
“Zoom in.” He gestures forward with his hand and a small piece of writing on a desk looms large.
Swiping across, he is able to cross-check the information in the writing against local police databases, public maps, school records, and so on. Up against the clock, Tom is able to extract exactly the information he needs from a huge volume of data in seconds with just a few everyday-language commands and hand movements.
As well as predicting the rise of touch screens and gesture-input, the scene sets the tone for representations of advanced technology in film, one that persists even today. Audience members will routinely see characters interact with screens and holograms that provide in detail the information and calculations those characters need, on command. Why? Well, it typically looks cool, and is a useful plot device for screenwriters. But more profoundly it also speaks to our wider, Utopian expectations of technology. Technology – in its idealized form – comes down to convenience.
We want technology to be able to give us precisely what we need in that situation, at that moment, with minimal physical and intellectual effort on our part. We want the full benefits of technology without having to be technologists ourselves. The development of ‘smart’ consumer technology over the last decade – from smartphones to mobile banking to Uber – reflects this deep psychological truth.
What has this all got to do with commodity analytics software? The answer speaks directly to a shift of focus at the forefront of innovation within this space.
The sheer importance of analytics within commodity risk management, and for companies with heavy exposure to commodity markets, is well known within the industry. The world of commodities is inherently a ‘Big Data’ world. (Learn more in "The Driving Need for Commodity Analytics in a Big Data World.") Commodities markets – more so than others – are highly sensitive to an incredible variety of disparate influences that interact in complex ways, from weather forecasts to political situations to industry demand and more. Physical trades are often hedged with financial derivatives that can rely on thousands of data points such as these.
Added to this, commodities markets – already infamous for being far more volatile than other industries – seem to have entered a new age of particularly high volatility. Prices can move very quickly, with little warning.
Thanks to these factors, analytics is now of paramount importance within the world of commodity trading and risk management. No longer a ‘nice-to-have,’ robust analytics are fast becoming a necessary tool for keeping up with the competition. To be clear, older-generation ETRM and CTRM software – although still very important – no longer fits the bill for what is needed here. These are largely transaction-based data capture systems, fantastic (at the time) for providing a clear, holistic picture of an organization’s exposure after the fact.
What the industry needs today however are systems that can provide real-time information that supports decision making. In today’s environment, waiting days or weeks to understand market changes is simply too slow to allow for meaningful adaption, and will translate directly into losses.
On one hand this means building certain crucial analytics to support better, faster, fact-based decision making. These include the ability to analyze information to create predictive models, allowing firms to develop accurate, repeatable formulas that take into account market conditions to identify optimal scenarios. It also includes the ability to simulate market movements, and produce advanced, clear visualizations of voluminous data sets, and so on.
On the other hand, advanced features alone only form part of the picture – equally crucial is the way in which these features are delivered and made accessible to the end user. To go back to our original analogy about technology in film, laypeople, above all, want speed and convenience from technology. They want it to give them precisely what they need, when they need it, for minimal effort. Importantly, they don’t want to have to be technical experts or know anything about the legwork involved behind the scenes to be able to get this information.
The latest commodity risk and trading analytics products are designed with this in mind. Generic business intelligence tools – while they may feature the raw analytical power needed – will often prove counterproductive for those looking at commodity risk and exposure. Commodities markets are very idiosyncratic, and so getting these generic tools to work in a useful way will typically mean many thousands of hours of complex customization. Given that the whole point of commodity analytics is to get the right information and make decisions as quickly and easily as possible, this defeats the purpose.
The best commodity analytics solutions come tailored to the space, and have as much of this customization already built-in as possible. They come with pre-packaged algorithms to run on top of data that, for instance, allow a user to run forward looking scenarios of P&L across the enterprise in seconds and minutes, not hours. They are crafted so that a user can answer a question such as ‘what type of profits or losses will result in the future if I continue with my current trading strategy’ at the touch of a button.
Given that speed is such a priority, the sheer processing speed of the solution is also key – especially given the huge volumes and different types of data relevant to commodity markets in our increasingly complex, globalized world. In response, the latest products are utilizing newly-developed next-generation technology able to process millions of data entities in seconds. They are also created in such a way as to seamlessly bring in data from a variety of data sources, whether internal (an ETRM system for instance) or external (weather data, market curves etc.) – there’s no point having the computing power if you can’t easily input the raw data to begin with.
They are also being designed with the convenience and non-technical expertise of the user front-of-mind. Historically, these sorts of tools were the province of staff in IT and analytics departments. Getting the required answers would typically require some technical knowledge and effort on the part of the user.
By contrast, the latest generation products are specifically crafted to be as user-friendly as possible, with visual and intuitive interfaces allowing users of all stripes across the business – whether traders, risk managers, analysts, supply chain managers, or back office personnel – to quickly get the answers they’re looking for without requiring any knowledge of the technical legwork involved in providing that answer. Once again, this serves the interests of speed: the importance of faster, better decision making in today’s markets means that organizations can no longer afford to waste time waiting for IT to update software. The latest products open analytics to all and allow users to focus their energies on making the most profitable decisions versus collecting, correlating, and keying data.
Last, but not least, it’s all very well and good having these elements within a system that sits installed on your desktop. But again, in today’s world this is not enough to meet the requirements of speed and convenience. We live in a world where we can access our banking information, check our energy usage, read emails and more on the go, on multiple devices in varied locations.
Commodity analytics need to be available on the same basis. Accordingly, the best new products sit within the cloud, and can be accessed instantly through smartphones, tablets, and various other portable devices, wherever the user may be. (The very best products are in fact primarily designed for viewing on such devices.) This also removes the historical hassle that tends to accompany installation of such systems: these next generation products can be activated, connected to your business, and used almost instantly.
Essentially, this shift of focus comes down to the ability to use technology without having to bother oneself with its underpinnings. A modern smart-car driver is far more removed from the vehicle’s mechanical underbelly than an early 1900s driver. And children today are far more comfortable with everyday computer usage than your average user in the early 1980s, though they may have less knowledge about microchips and operating systems. Commodity analytics technology has reached a similar turning point: its future lies in masking and automating the technical legwork as far as possible, and making the ‘surface’ – the level at which information is analyzed, displayed, and actioned – as intuitive, simple, convenient, and non-technical for as wide a range of users as possible.
Tom Cruise’s magical ‘tell me what I need to know and tell me now’ technology may have seem far-fetched in 2002. But in the world of commodity analytics, it has arrived.
Eka's Commodity Analytics Cloud platform is an advanced solution that brings commodity specific analytics to all business users, and enables users to gain unique insights and take action before competitors.
Those who wish to retain competitive edge need to embrace commodity analytics now.