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All buying and selling of an energy commodity (natural gas and electricity) by both end-user customers and their energy suppliers, involves a simultaneous look and interpretation of only two variables: energy usage and energy price. While conceptually simple, in reality marrying accurate energy usage and price information is extraordinarily difficult, first and foremost because the added dimension of time must be considered as both variables are in a constant state of change.

Additionally, an abundance of inefficiencies exist in accessing, managing and interpreting the data. There is also no real standard nomenclature in the business, so often each market participant brings a slight twist on the meaning of the data interpretation. Energy data sits in a myriad of different IT systems. Data collection and data entry errors are rampant, data is often not delivered in a timely fashion, and frequent data analysis or interpretation procedures are error-prone and costly. Also the wide spread use of Microsoft Excel that still exists as the primary tool in many energy industry organizations to process this information, contributes to the problem as the multi-dimensional elements that need to be considered here cannot be accurately portrayed in simple spreadsheets.

The application that customers would love is an integrated business process solution that would marry energy usage and price data with automated analytics and data interpretation. This is a needed solution for both end-user customers, who are buying energy, and for retail energy suppliers who sell them the energy.

There are three unique characteristics of the ideal energy data solution for energy suppliers (retail marketers) and commercial and industrial customers (retail energy buyers) that have to date never been effectively delivered to the retail energy market. The data requirements of retail energy marketers, who often provide commodity service to end-users in different markets, are the exact same as those that are needed by large multi-site customers who buy competitive supply from different energy retailers for individual facilities they operate in different states. The common requirements are:

• Timeliness of Data -- Data would be either near real-time or delivered on a much more timelier basis than it is today, providing great opportunity for optimizing energy commodity contracts.
• Granularity of Data -- Data would be accessed at an individual facility level, so that forecasts and imbalances could be more accurately allocated at an individual customer level.
• Ubiquity of Data -- Data would be provided across multiple utility systems and delivery points supporting different regional energy markets.

To provide the needed solution requires:

• A core competency in understanding how to capture and aggregate the various sources of energy market price data;
• An understanding of how to capture and manage EDI transaction data and how to collect customer usage data out of multiple utility and meter data environments. The need here is also to best maintain data integrity and eliminate data entry issues that typically create errors in downstream process automation; and
• The domain expertise in the energy markets to understand how the data is interpreted, and then apply the business rules or applicable billing tariffs in each of the different retail energy markets, a highly manual process today in most energy companies that is ripe for automation.

Various market participants' benefit from an energy data solution incorporating these key data elements for collecting both price and usage data, and then combining this data with specific energy retail process expertise and automation.

This solution provides significant potential efficiencies to energy marketers and aggregators as a foundation for a "retail billing and customer care solution." It allows them to create timelier and more accurate bills for thousands of retail end-users that result in optimizing the revenue cycle and increasing margins with smaller retail customers, and maximizing customer retention.

This solution also provides great efficiencies to hundreds of multi-site C&I customers as an "energy forecasting and tracking system" that essentially functions as a platform interface between these C&I customers and their energy suppliers. The opportunity is to create a daily usage forecast based on key variables (meter data, plant operations, weather) and then integrate current market and forward pricing to analyze savings and contracting opportunities (index or fixed pricing, hypothetical nominations, transport and storage option calculations, etc) . This would provide tremendous time savings in terms of analyzing commodity purchase opportunities and provides tremendous opportunities for mitigating risk in a highly volatile commodity market.

The capability to leverage energy data in this fashion would also benefit carbon accounting and GHG reporting initiatives now being launched by many large multi-site C&I customers. There is a huge need to streamline the data collection process with more efficient capture of the information needed for GHG reporting. While many of the required data inputs for GHG reporting (supply chain, fleet/vehicles, air travel, etc) will still have to be collected, a consistent requirement is how to most efficiently get usage data (on electricity and natural gas) directly attributable to company facilities into a common data warehouse to start the process. This type of data solution could greatly enhance the productivity of that effort, which today is an extraordinarily time consuming task for most companies.

Thus a very high leverage activity in buying and selling energy commodities, and managing the inherent risk associated with this, is just getting market price and customer usage data in one place, then capturing and automating knowledge management elements associated with the various business process activities that happen around this data.