Everyone is talking about Big Data these days: how to capture it; how to manage it; how to store it; how to use it. Businesses that deal in physical commodities, such as oil and gas, are no exception. But what is interesting about managing commodities is that it has always been a big data business - long before the term became fashionable.
By its very nature, commodity trading creates thousands of individual data points.
Behind all these data points are decisions to be made that affect short-term profitability and long-term stability. If we look at the oil and gas supply chain as an example, simply moving oil from point A to point B involves selecting the right storage, the right transport solution and the right route. That’s after deciding on the right quantity to move, the right market to send it to - and of course the right source to acquire it from in the first place. In rapidly moving energy markets, any of those decisions may need to be reconsidered before the commodity arrives at its final destination.
Then there’s the processing and refining to consider.
The risk management aspect of the business adds a whole new layer of data points too. Prices move, markets change, regulations get updated. For example, in the energy markets, oil and gas companies with exposure to commodities need to stay abreast of these developments and react accordingly. They also have to decide on risk policies that take into account the counterparties, geographies, and commodities they wish to be exposed to, and the limits they wish to put in place on those exposures. Again, the ability to react as these limits are approached or breached - deliberately or otherwise - is essential.
Taken together these individual dimensions create a significant amount of complexity - and produce huge volumes of data. But if industries such as oil and gas have always been a big data business, they haven't always been the best at using that data.
Most commodity exposed firms have reasonably strong transactions systems along the supply chain to capture the data that surrounds each transaction or engagement. But as margins tighten, and conditions get tougher, it is becoming increasingly clear that systems need to do more: they need to be able to correctly interpret the data so that it can support real-time decision-making. This requires next-gen ETRM software and CTRM software.
This is a big step on the evolution of commodity management systems: from data capture to data analytics. The ability to analyze information to create predictive models allows firms to develop accurate, repeatable formulae that take into account market conditions to identify optimal scenarios.
A system designed for the Big Data era can remove much of that uncertainty. By incorporating and analyzing historic yield or geological information for example, the system can create a far more accurate picture of the likely outcome of any given well or mine. And with the ability to predict both quality and quantity of output, the commodities business is in a better position to decide which producers to deal with, find an appropriate buyer, enter into advanced agreements, and negotiate better pricing, as well as optimized logistics planning.
In this way, commodities businesses can make better use of the information they produce themselves as well as externally available data sets. Using advanced visualization techniques coupled with user-controlled, predictive analytics, commodities companies can optimize trading and supply chain operations.
The right system will be able to integrate all of these internal and external data sets, extracted from multiple sources, and bring them together in a central repository. It will then visualize that data to enable firms to pinpoint where they and their resources need to be at any time.
Using powerful analytical capabilities, the new generation of commodity management systems, such as Eka's InSight CM, enable commodity exposed businesses to develop predictive capabilities that facilitate actual decision-making, rather than just monitoring activity. In other words, it’s about looking forward, rather than back: making accurate forecasts about the future, rather than just monitoring what’s happened in past. (To learn more of the benefits of next-gen ETRM and CTRM software solutions, see "4 Reasons to Give Up Spreadsheets for CTRM Software.")
The world has changed. KPIs and static graphs are great - but they are not enough. The better solution allows commodities businesses to embrace both Big Data and the future without being overwhelmed by either.