Data quality is an ongoing issue for energy organizations. Identifying profitable trading opportunities ahead of the competition and being able to act on them is what sets the leaders apart.
Every day decisions worth hundreds of thousands of dollars rely on access to, and analysis of, clean and accurate data. Any error in the data could lead to a decision that proves to be unprofitable. A data source that is 100-percent accurate is ideal, but without rigorous cross-checking and scrubbing this is unlikely.
Data quality is of particular importance in light of the Financial Accounting Standards Board’s FASB133 ruling that came into effect January 1. FASB133 highlights the need for derivative reporting companies to use accurate pricing data in their reports. This data is not easy to come by, especially in quantity.
The problem with bad data
Third party information specialists offer services that include 24/7 data operations teams responsible for cleaning and rendering consistent dozens of data sources that are often aggregated and distributed each day. One vendor offers data cleansing processes that are independently certified and reports that an average of 15,000 prices are corrected every month. Typical data errors include missing data, incorrect dates, and incorrect prices. These data errors can be introduced in a number of ways:
- Assessments. The nature of assessing markets which underlies price setting in energy markets is subject to error. Most reported prices are based solely on what a trader or broker tells a reporter, rather than access to the actual trade details.
- Hard copy vs. electronic data. It is not uncommon for the price in an information provider’s electronic feed to differ from that in the paper feed.
- Transmission method. Errors can also be introduced during data transmission if the provider’s method is unreliable.
- Manual entry. Manual entry from paper data is a prime source of error for companies that do not receive data electronically.
- System error. Incorrectly configured IT systems may use the wrong set of data or process data incorrectly.
- Sharing data. Company networks allow multiple users to access and change data, increasing the likelihood of error.
- New markets. With frequent trading in an emerging power market, a huge volume of power-related prices are published daily creating a potential data aggregation and storage problem. Extreme volatility of power prices makes it difficult to assess, even with automated checks, whether a price is accurate.
- Internet appeal. Companies posting vast quantities of data on the Internet are not so concerned with the quality of the data when they are after speed of transaction by doing business online instead of by phone or fax.
Impact of bad data
Data errors can have an impact on all areas of a company from trading and risk management operations to back-office activities. Larger errors, such as the omission of a decimal point, are usually detected quickly as they have an obvious impact. Smaller data errors are virtually impossible to detect, even with automated checks, as they are usually valid, although incorrect, prices.
It is not unusual for data errors to occur monthly within trading organizations. In some cases the counterparty queries an invoice at the end of the payment period. The back office must then identify and correct the invoice error. This can add several weeks to the process, which can significantly increase the original payment period. In addition to the cash flow implications of this length of payment period, which can be more significant than the original errors, the subsequent erroneous value of a deal does not take into account the handling costs incurred in tracking down and rectifying the original source of the error. Costs for this activity are difficult to quantify and vary greatly from one organization to another. Some large accounting departments assume a handling cost of between one percent and five percent for such tasks.
Clearly, good management of accurate and complete data will be critical to ensure that all accounting and public reporting are done properly. The FASB ruling is intended to resolve inconsistencies that have plagued companies regarding how to account for derivatives and it dramatically changes the way many derivatives transactions and hedged items must now be reported.
Woodall, data product manager at FAME Energy, previously worked for Elf Gas and Power in London before joining Fame Energy. She may be contacted via e-mail at email@example.com.