Advanced Analytics Can Help You Manage the Knowledge Doubling Curve

EKA > Advanced Analytics Can Help You Manage the Knowledge Doubling Curve
Feb 23, 2017

Advanced Analytics Can Help You Manage the Knowledge Doubling Curve

February 23, 2017

 

 

R. Buckminster Fuller created the Knowledge Doubling Curve in 1982. Fuller observed that in 1900 human knowledge doubled each century, but by 1945 knowledge doubled every 25 years. Today, knowledge doubles every 13 months, and IBM predicts that the Internet of Things (IoT) will hasten this doubling effect to every 12 hours by 2020. The rapid growth in world population – which added 5.5 billion people since 1900 – has also contributed to the increase in knowledge.

 

How does this impact commodity market participants?

 

As the world’s population grows, demand for resources to feed, heat, heal, and support people increases. With 7.3 billion people in the world today – and the population projected to reach 9.7 billion by 2050 – demand for commodities in all industries is expanding, driving companies to find new ways to increase production, improve efficiency, and create more profitable operations. The explosion of new information from throughout the value chain provides an unprecedented opportunity for companies to make better decisions.

 

New information alone is not enough. As knowledge grows, so does the need for better tools to aggregate, filter, and analyze this knowledge to turn it into actionable insights. The McKinsey Global Institute’s recent white paper “The age of analytics: Competing in a data-driven world,” states, “Bad analysis can destroy the potential value of high-quality data, while great analysis can squeeze insights from even mediocre data.” The true value of big data depends on its ultimate use, and you need machine learning algorithms and artificial intelligence to evaluate information, determine what pieces have value, and maximize insight from each piece of data.

 

Machine learning has wide-reaching applications. Meteorologists use machine learning to create better weather forecasts – potentially saving lives – while retailers use machine learning to increasing sales through advanced customer segmentation. Machine learning is also used for fraud detection and to identify spam messages.

 

Eka has been a leader in smart commodity management for over 12 years, providing innovative commodity trading and risk management software. Eka’s Commodity Analytics Cloud solution aggregates information from disparate systems throughout the value chain. When you pose a question, Commodity Analytics Cloud’s intelligence engine uses machine learning to analyze all of that information to provide the best possible fact-based answer to your question. Commodity market participants make better decisions, improve operations, become more efficient, and increase profits.

 

As knowledge continues to grow, companies with the right tools to use this information to make better decisions will gain a competitive advantage. Companies that don’t embrace the data explosion and advanced analytics will be left behind.