C/Etrm gap
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Commodity trading and risk management functions are critical for business performance, but many companies fail to adequately define their data and information architecture to support these functions. A robust data architecture is needed to determine the required systems configuration to deliver timely, accurate, and relevant data to front, middle, and back office staff. Additionally, commodity trading and risk management systems often fail to efficiently handle market data, lack expertise in big data and analytics, and have opaque forward curve building methodologies, undermining returns on investment. Integrating third-party applications for these missing capabilities can be worthwhile despite additional costs.
C/Etrm gap
- 1. #CTRM/#ETRM #GAP Feb 23, 2015 139 views 7 Likes 2 Comments Share on LinkedIn Share on Facebook Share on Twitter #"Commodity #risk #management" is emerging as a critical differentiators of business performance. Many leading companies have established "Commodity #Trading" and #Risk Management (#CTRM /ETRM) ‘functions’ which are delivering significant performance and control improvements.#CXO's need to know where and how they can make or lose money, which requires access to a wider variety of data types and sources that can be translated into ready-to-use information. Volatility needs to be actively managed because the uncertainty and variability it feeds directly into a company’s financial performance attracts the attention of external commentators. It impacts cash flows and margin and influences share price and market reputation. We know that trading and risk management organizations are inherently both data intensive and highly data driven. As such, there is a great reliance on accessing this information in a timely manner, as well as implementing strong IT disciplines, governance and processes to support management decision making. Managing risk in trading the #energy portfolio, for example, may rest on the ability to access and convert data from upstream and downstream, on-demand, to improve the value of the total group portfolio. This capability puts sophisticated demands on technology and supporting staff to collect, collate, model, sort and aggregate vast amounts of data. Increasing calls for such input requires a highly responsive operation in which accuracy and traceability are key. Timely, accurate and relevant data, analyzed and presented as user information for front, middle and back office staff, is critical for a high performing #CTRM/#ETRM function. Underpinning the delivery of this critical user information is the need for a robust data and information architecture which sets the framework to determine the systems configuration required to deliver the architecture. In our experience, many #CTRM/ETRM functions fail to adequately define the
- 2. data/ information architecture and this leads to sub-optimal decisions on the use of supporting systems. A number of common myth and misunderstanding undermine the success of #CTRM/ETRM system implementation and it's overall impact on business and the subsequent #Return on investment (#ROI).
- 3. Missing Blocks of the E/CTRM ecosystem :
- 4. Conclusion :While the #ETRM/#CTRM is good for capturing the trading lifecycle from Pre-deal analysis to Settlement and Invoicing but not all E/CTRM can efficient to handle the market data/exchange data on it's own product architecture and also lacks an internal expertise on #Bigdata handling and good #Visualization and self-service #analytics which normally is best to integrate with a third party application though it is an additional cost but worth the investment in the best of the interest of the business. Besides, #Fowardcurve building methodology is not transparent enough to to go back and check exactly the formula used for the calculations which is a
- 5. mandatory requirement under the various data compliance Act and audit compliance procedure.