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Self-Service Analytics Redefines Commodity Trading

When computers were first developed, only people who knew operating system commands could use them. The advent of graphical interfaces changed that. Before the arrival of the internet, information was carefully curated and coded by librarians. Now anyone with a web browser has access to encyclopedias’ worth of information.

At one time, only professional statisticians could perform analytic functions on complex data sets. Today, advances in technology make advanced analytic capabilities available to everyone in an organization, providing business users immediate access to all kinds of information and insight.

Technology Fuels Change

Traditional data analysis requires users to know the questions they want answered up-front. Data must be properly set up by IT specialists before a query can be made. The rise of in-memory data grids, schema-on-read technology, dynamic visualization, machine learning, and predictive analytics is changing all of this, shifting analytics away from IT departments towards business users. By enabling self-service analytics, these new technologies are delivering far greater insight to all areas of a business.

In the commodities sector this radically changes the way decisions are made. When business users are dependent on the skills of IT specialists, individuals can answer basic questions based on an assessment of historical events. For example: how much of a commodity was purchased; what happened to it; when did it happen; and who was involved.

With sophisticated quantitative methods of analysis available to everyone -including regression techniques, forecasting, optimization, and simulations - business users can ask more predictive and proactive questions. These might include: why did a certain event happen; how many times has it occurred in the past; is there a pattern or seasonality to it; how likely is it to happen again; what would be the effect of changing one or more factors that enabled that event; what would be the best action to take moving forward; and what other information is available that we did not consider asking before.

Analytics Drive Better Decisions

Analytics enable commodity traders to explore data from multiple sources, discover new and unique insights from it, and use those insights to beat the market and increase margins. In the same way, risk managers can use more diverse data sets to discover previously obscured risks, investigate them, and then develop and test mitigating strategies to minimize the likely impact.

On the logistics side, supply chain managers are able to develop advanced techniques for visualization and statistical optimization of their operations and, most importantly, react to changes in real time. These new insights can be used to improve scheduling decisions and facilitate greater levels of collaboration both with other business units and with external partners.

Finance and treasury teams can delve much deeper into company data in order to improve financial operations, including confirmations, invoices, cash, and settlement – and so minimize their exposure to credit and counterparty risk.

A Key Differentiator 

Access to data and the ability to synthesize, analyze, manage, and visualize it will be a key differentiating factor for successful commodities businesses. Effective data analytics will enable businesses to leverage multiple, complex algorithms and predictive models in a fraction of the time and with significantly less effort than before.

Organizations with this kind of information will be the ones able to identify likely opportunities and potential issues and respond accordingly. They will gain improved understanding of the market and the ability to act on that understanding through evidence-based decision-making.

If knowledge is power, then self-service analytics brings power to all business users.



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