Almost every commercial and industrial sector has been affected by dramatic increases in the volume of data that is now available. In commodity management, the volume, variety and velocity of data coming into the organization is unprecedented. Traditional commodity trading and risk management (CTRM) solutions cannot analyze this deluge of data from disparate sources. Effectively managing so much information requires innovative analytics solutions like Eka’s Commodity Analytics Cloud.
Adopting an analytics commodity big-data solution like Commodity Analytics Cloud works best with the right technology. The following six components provide the backbone of any big-data ready solution:
- A distributed storage grid offers many advantages when it comes to storing data – a fundamental requirement in any big data management solution. Its fault tolerance and redundancy all but eliminate downtime, it helps maintain stable performance even when storage load is fluctuating, and it is highly scalable. With a distributed storage grid you can avoid expensive hardware upgrades and installation interruption.
- Schema-on-read-technology allows data to flow into an analytics system in its original form, enabling users to ask questions of the data beyond those baked into a pre-defined model. When working with the large sets of raw data that are typically found in the commodities business, the versatile organization of data offered by schema-on-read is a big advantage.
- In-memory data grids (IMDGs) provide unprecedented processing speeds, enabling commodities managers to get the results of advanced calculations in minutes rather than hours. IMDGs can support hundreds of thousands of in-memory data updates per second, and can be clustered and scaled in ways that support large quantities of data.
- Machine learning is based on the construction of algorithms that can learn from data and adjust their performance accordingly. Machine learning can be used to make accurate predictions on diverse elements within the commodity value chain: from investment in new plant and fleet right-sizing, to cash-flow management based on information on individual counterparties and invoices.
- Predictive analytics is the branch of data mining concerned with discovering future probabilities and trends. Predictive analytics can be used to run complex forecasting models and scenarios to answer essential “what if” questions. Want to know what happens to the bottom line if market prices go up or down? Then predictive analytics can provide the answer.
- Dynamic visualization presents data in easy to use and easy to understand formats. Dynamic visualization enables you to ask iterative questions and get timely answers. Interactive displays enable you to discover patterns in large amounts of data and make faster and more accurate decisions.
This might sound like a prohibitive technology investment, but parallel developments in cloud platforms and cyber-security mean that commodities-specific solutions like Commodity Analytics Cloud are available without investing millions of dollars and untold man-hours. Taking advantage of new technology, bringing advanced analytics into the organization, and being ready for whatever big data brings next have never been easier.