Proposal for Customer Information Database Workshop

A Proposal to Establish an "Energy Data Clearing House"

By Michael Parti and Charles Hubay	


This proposal is being submitted jointly by Applied Econometrics, Inc. (AEI)
and Decision Sciences Research Associates (DSRA).  We plan to merge our
firms to combine our joint strengths in energy analysis and data base
construction and maintenance.  Our proposal is that our merged firm will
administer a central repository of energy-related data sets, and transfer
these data to CPUC-authorized participants in California's competitive
energy market.   Our motivation for this proposal is our belief that the
development of competition in California's energy market requires fast and
equal access to accurate information about California's energy consumers.
We are independent of all of the UDC and ESP retailers, the regulators and
all the other actors in the California energy community.

We propose that the CPUC choose our merged firm to serve as an Energy Data
Clearing House, a middle-entity between the UDCs and the other firms that
require energy market information.  In practice, the Energy Data Clearing
House would receive data sets from energy companies (including UDCs), and
then proceed to clean these raw data sets, standardize them into a common
record format, and assemble the required documentation so that these data
could be distributed for use by analysts and market planners working on
behalf of both UDCs and ESPs.  We believe that this strategy would provide a
healthy and low-cost information environment necessary for fostering the
virtues of competition in California's energy markets.

A very important function of the proposed Energy Data Clearing House would
be to administer privacy rules and to document to the CPUC the level of
customer information that is released to all market participants.
Centralized control and reporting of this type could serve to minimize the
misuse of energy customer data for non-authorized purposes.

To obtain the initial database we propose that the UDCs transfer to the
Energy Data Clearing House all the data sets that have been paid for by
ratepayers.  The primary data would of course be customer billing data, but
we propose that the UDCs should also supply various types of  supplementary
customer information, including customer-level and system-level demand data,
specialized customer surveys obtained as part of equipment saturation
studies or DSM evaluation efforts as well as UDC weather data (from
permitted sources).  After an initial data collection, the data holdings
would require periodic (perhaps quarterly or semiannually) updating.

The virtue of this data organization method is that the UDCs can transfer
the required data to the  Energy Data Clearing House only once in tape form
without meeting the sometimes difficult data request specifications required
to transfer data to a potentially large number of data users.  The Energy
Data Clearing House would also solve the problems of data comparability
among UDC sources, fully document the data holdings and then transfer these
standardized data to  qualified and authorized parties at a low cost,
maintaining the level of customer confidentiality as required by the rules
adopted by the CPUC.

The establishment of an Energy Data Cleaning House would serve to reduce an
unwanted responsibility for the UDCs, and would provide a fast turn-around
time and equal access to cleaned and standardized data for any entity that
has a need for such information, including the  UDCs, ESPs and the
consulting firms they may engage to assist them.  In addition, it would
eliminate any suspicion that the UDCs would take unfair advantage of the
marketing information that they collected under a protected regulatory
environment.  As part of this process, we propose that the Energy Data
Clearing House would set up reporting procedures to document and inform the
CPUC about all data requests fulfilled by the Energy Data Clearing House.
Making this information available to the CPUC and other interested parties
would further act to make the information environment fair for all.

We feel strongly that the combined experience and data processing facilities
proposed by the merger of our firms would be ideal for implementing the
Energy Data Clearing House as outlined in this proposal.  We are very
experienced in working with electric industry data sets, and we have the
capability to maintain a very large data base of utility customer
information as well as the necessary tape drives and other data transfer
devices to communicate data across  a wide range of computing environments
from mainframes to microcomputers.  In addition, we have extensive
experience with, and commitment to, maintaining customer confidentiality, as
required for this assignment; and we know how the data have to be cleaned,
organized, and documented to make useful analytical databases.

In addition to the issue of providing fair and convenient access to
information, the most important task will be to implement the CPUC-mandated
levels of customer confidentiality.  We can imagine a system based on a
hierarchy of customer information disclosure procedures.  This may involve
providing access to customer data identified only by geographic area
(weather zones, zip codes or census tracts), customer service addresses, or
providing access to customer data with names, addresses and phone numbers,
based on evidence that these customers have given permission for the release
of these data. 

An appropriate level of secrecy for the customers who wish their
confidentiality maintained would be that given in the section on
Confidentiality Protections B.1. (page 5 in our copy).  This section
stipulates that a level of secrecy of at least ten customers in each
aggregation category would be adequate unless a particular customer's
identity could be determined from the data.  This is similar to the Federal
Census Bureau's Suppression Rules for censoring data that could possibly
identify particular individuals or households.

We imagine that the Energy Data Clearing House would be funded from two
sources:  
(1) An information distribution fee would be paid by UDCs to Energy Data
Clearing House to be used for data standardization and documentation.  In
return, each UDC would receive all of the UDC information in a cleaned,
documented, standardized form that would be useful for load and marketing
analysis.  This would be combined with a report containing summary
statistics showing loads broken down by relevant customer characteristics.
(2) A fee would be paid for the sale of information to ESPs as well as
Marketing and Energy Research firms. We propose that these fees be based on
the number of customer records used for each data extract or deliverable
product. This would be combined with a report containing summary statistics
showing loads broken down by relevant customer characteristics.

Each summary report would also contain a complete process description
showing the steps taken in cleaning the data set and constructing its
elements.  The fees would be on a well-documented cost basis with a modest
overhead charge.

The Energy Data Clearing House services would be tailored to the needs of
the individual data user in that information would be available at any
allowable level of aggregation, and could be processed using options ranging
from simple summary statistics to our sophisticated econometric and
engineering analyses.  Most important, no matter what the level of
aggregation, the required secrecy of the individual customer information
will be maintained as we have always maintained it during our 20 years of
work in the energy field.

To sum up, the Energy Data Clearing House services would add value to all
the data sets because of our sophisticated data cleaning techniques; our
expertise with all the major mainframe and PC database packages; our
understanding of the needs of analysts/planners; and our experience with,
and commitment to, confidentiality.  In addition, this service could begin
immediately since we already have experienced staff.  We do not have to
acquire and train staff for this database construction and maintenance task,
since this is our primary business.  Finally, since this is our primary
business, the needs of data users come first.  We are prepared to expedite
any requests for data.




.