The world is changing faster now than at any time in human history. Take, for example, the telephone. Alexander Graham Bell patented the telephone in 1876. It took landline telephones 45 years to get from 5 percent to 50 percent market penetration among U.S. households. In contrast, it took mobile phones 7 years to reach that threshold and people have started dropping landline telephones completely. The world is changing at an astonishing rate, and companies need to stay current or risk falling behind. No one wants to become the next Blockbuster Video.
Why big data analytics?
More than 2.5 billion gigabytes of data are produced every day, providing companies with an unprecedented amount of information. But an abundance of information isn’t useful without the right tools to make sense of it all. You need big data analytics to analyze the volume, velocity and variety of data generated each day. And it’s important; you may choose to ignore big data but your competitors may not. 79 percent of users believe that companies that do not embrace big data will lose their competitive position and may even face extinction while 83 percent have pursued big data projects in order to seize a competitive edge.
What does this mean for commodities?
The commodities industry has always been a big data business. Physical trades that are hedged with financial derivatives create thousands of individual data points and each of those data points represents multiple decisions to be made before the trade is eventually closed. Prices move, markets change, and regulations get updated.
Choosing the right big data solution for commodities is critical, because it must analyze all data necessary to answer the right questions in the right timeframe.
- Big data analytics for commodities must analyze information from disparate systems, including CTRM, ERP, CRM, treasury and spreadsheets. The only way to gain a complete picture is to evaluate all available data.
- Analytics must process large sets of data quickly. Commodity transactions are complex and have huge volumes of data. Extremely fast processing speed is required to traverse all the data to deliver timely answers to users.
- Commodity-specific intelligence is required. Generic business intelligence tools do not provide answers to the questions commodities companies ask, like "what type of profits or losses will result in the future if I continue with my current trading strategy?" Only commodity-specific solutions answer the right questions.
- Predictive analytics are essential. Analysis that provides trends in oil prices last week are not useful to an oil trader trying to decide whether or not to book an order, but analysis that compares different alternatives and forecasts how to make a better deal is.That information is actionable. Oil traders can choose to lock in prices today based on that analysis, securing a lower price and potentially gaining an advantage over slower competitors.
- Business users need to create their own analytics. For truly useful analytics, business users should be able to create their own analytics, then be able to customize those analytics when the needs of the business change. In fast moving markets, delays caused by relying on the IT department or other specialists to create analytics render the results useless.
- Analytics must be accessible on mobile devices. Workers can’t be tied to a desktop - they need decision support wherever and whenever they are working.
Commodity analytics solutions like Eka’s Commodity Analytics Cloud provide a generational shift in capability and value to all commodity business users and consumers of commodities. These commodity-specific solutions provide analyses using all available information, enabling users to make smarter decisions and gain a competitive advantage. Companies relying on slower technology, or ignoring the data deluge, will be left behind.