Data management challenges in the Commodity/Energy Trading management business verticals and the possible best practices that one can follow.

1. Data management best practices
&
CTRM/ETRM challenges
By: AJAY KOOMAR

2. CTRM/ETRM- THE SCORECARD
• Physical position reports for each location and commodity including
long and short status. (Position reports may be for current and
forward time periods.).
• Estimated profit and loss on completed transactions and processes
forecast present and future cash flows •
• Price risk exposure associated with current and forward positions •
• Credit risk exposure associated with current and forward positions •
• Operational risk exposure •
• Information required for regulatory compliance in areas such as
financial accounting, futures, exchange activity, price transaction
reporting, and government reporting.

3. THE SCORECARD DEPENDENCY-ON MARKET DATA
• ETRM system effectiveness depends on
accurate and timely reporting and market
information. Successful ETRM utilization
involves effective reporting and analysis to
navigate and improve trading performance.

4. The ETRM /CTRM system is a trading organization’s scorecard-Report
• Physical position reports for each location and commodity including long
and short status.
• (Position reports may be for current and forward time periods.)
• Estimated profit and loss on completed transactions and processes •
• Forecast present and future cash flows •
• Price risk exposure associated with current and forward positions •
• Credit risk exposure associated with current and forward positions •
• Operational risk exposure •
• Information required for regulatory compliance in areas such as financial
accounting, futures •
• Exchange activity, price transaction reporting, and government reporting.

6. ETRM/CTRM-DATA CHALLANGES
• Maintaining up to date market data overtime
and adding it to when needed is a significant
challenge for ETRM/CTRM sys-admin.
• Unexpected data changes.
• Market data provider supplies a correlation to
a previously published rate , the ETRM system
has to recognize the exception. In many cases
ETRM system finds it difficult to handle.

7. Data aggregator
CTRM/ETRM –Transaction life cycle
Capturing infrastructure, contracts and
counterparties
Pre-deal analysis
Deal capture
Scheduling operations and logistics
Risk analysis.
Settlement and Invoicing.
Market data
All commodity transactions companies would ultimately will require the
service of an external Data aggregation services to handle multi-
commodity, multi-source and multi-format market data.

8. External data dependency
Where trading is conducted with an exchange
rather than a company, exchange commodity
details and contracts have to be captured. These
include contract termination dates and delivery
timing, as well as broker intermediary details.
The quantity of information to be captured and
entered into the ETRM system before a
transaction occurs varies widely with the
business profile of the trading company.
Capturing infrastructure,
contracts and counterparties

9. External data dependency
Before executing a trade and entering the transaction into
the ETRM system, pre-deal analysis is usually performed.
Traders analyze current market prices and fundamentals to
identify optimal timing and pricing mechanisms for the
trade. Data requirements for pre-deal analysis, in particular
access to historical market prices and fundamental
information such as storage and weather data, are not well
served in ETRM/CTRM systems. Instead, analysts typically
use outside software tools that contain broader market
information. Access to market data within ETRM systems is a
challenge because the system tends to contain only the
market data needed to manage existing trades.
Pre-deal analysis

10. External data dependency
Deal capture includes all relevant transaction details. Typical
details might include trade number, quantity, commodity,
units of measure, counterparty, contract, delivery timing,
buy or sell, location, financial or physical, trader name, price
mechanism, transaction frequency, and link to other trades.
Data management challenges arise when deal volume is
high and errors in the details creep into the ETRM system,
even with automation. Deal entry errors are time consuming
to fix, render the scorecard inaccurate and threaten ETRM
system integrity.
Deal capture

11. External data dependency
Scheduling logistics require data interaction (often on paper)
with third-party transportation systems. Without up-to-date
scheduling and logistics information, the ETRM scorecard
cannot present accurate position reports to management.
The data management challenge in scheduling and logistics
is capturing data from third-party transportation,
processing, and scheduling systems. Data may not be
available in a timely fashion, and changes or corrections to
volumetric measures may occur without notice.
Scheduling operations and
logistics

12. External data dependency
Risk management
RISK MANAGEMENT
1. Market risk(price risk).
2. Operational risk.
3. Counterparty risk.
4. Regulatory risk.

13. Market data management best practices
• Use straight-throughput processing and
automation to eliminate manual efforts •
• Reduce data feeds to a minimum •
• Ensure timely data updates •
• Perform pro-active data quality control •
• Maintain audit trails of data changes and track
corrections •
• Manage data permissions to ensure data
integrity

14. Pre-deal Analysis
• Traders analyze especially historical data.
• Manages only market data required for the
trade as against the broader market
information.
Pre-deal analysis

15. Deal capture
• Errors in the details creep into the ETRM
system even with automation for high deal
volume. Deal entry errors are time consuming
to fix, render the scorecard inaccurate and
threaten ETRM system integrity.
Deal capture
Data from third party transportation , processing and scheduling systems poses a quite a challenge
for the ETRM sys-admin.

16. Risk management
RISK MANAGEMENT
1. Market risk(price risk).
2. Operational risk.
3. Counterparty risk.
4. Regulatory risk.
•Market data to manage market risk is primarily pricing. Generating daily forward curve
prices from market data (mark to market) –Risk reporting.
•A forward curve for each forward position in the trading portfolio.
•Many commodities and in particular for periods further out into the future , no market
data is available .
RISK MANAGEMENT CHALLENGES
Market data from multiple
sources
Data often poorly
organized
Data requires
Further
massaging
ETRM
Forward curve- built
outside
SOLUTION
VAR calculation is data intensive where they use simulation method
Data manipulation in excel

17. Operational risk
•Transportation.
•Processing.
•Storage.
Primarily all the above mentioned data is coming from a third party
and the same needs to be feed into the ETRM system
Challenges

18. Counter party –credit risk
•Market data from credit rating agencies.
•Financial reports.
Third party data collection.
Challenges
Note : Clearing houses removes counter party credit risk but incur overhead cost to manage the clearing process
And meet daily margin calls.

19. Regulatory risk
• To comply with regulation, it is necessary to
understand where outside data originates and
track any changes occurring en-route to
ETRM.
Access to original set of data is an important advantage in this process.

20. Associated contract
Trading
infrastructure
CTRM/ETRM- transaction life cycle
ETRM
Data set-up
Market data
Data
Aggregator
Transaction life cycle
CONTRACTS
Credit limit
Qty
Price
Delivery
Standard contracts derived from International swaps and derivatives association (ISDA). North
America energy standard boards (NAESB).

21. Best practices of market data management
• Use straight-through processing.
• Reduce data feeds to minimum.
• Ensure timely data updates are available.
• Pro-active data quality control.
• Maintain audit trails of data changes and track
corrections.
• Manage data permissions.

22. In a era of “Free fall of crude oil prices , “cost management” has become the priority for energy companies /traders
Scope of cost optimizationCost centers
1.Product and commercial cost 1.Reduce number of product approvals.
2.Streamline portfolio (product overlapping
activities).
3.Standardize front office set up /systems.
2.Support cost(including control
requirements)
1.Reduce control and reporting
requirements by way of maximum
automation.
2.Streamline trade process and increase end
to end automation.
3.Consolidate functions and evaluate near
off shoring and outsourcing
3.Information and Technology Cost
1.Eliminate high cost /low benefit projects.
2.Streamline application landscape.
4.Location Cost
1.Optimize location set-up/spread of
departments.
2.Increase building utilization.