A useful piece of advice is to never store (save) your data or analysis in the same location as
the computer software that you are using.  It is easy to accidentally delete a key file that runs
the software when deleting or moving data or  analysis output files.  Also, it is often very
difficult to locate data and analysis files when they are mixed in with the system files.
The second task is to establish a simple, flexible, and useful set of 
file name conventions
that make it clear what each file contains.  Key information might  include: 1) the data type
(e.g., WP for wholesale prices or RP for retail prices), 2) the time period (e.g., 0697 for June
1997), 3) and the commodity (e.g., MZ for maize).  The three letters or number permitted
after the full stop usually relate to the computer program that the data are saved and stored in
(e.g., XLS for an EXCEL file, DBF for a Dbase file, WK4 for a LOTUS 4 file, and so on).
An example of an EXCEL file of retail maize prices for March 1997 would be written as
RP9703MZ.XLS.
The next step of data management is to develop and maintain careful and comprehensive
documentation of the data and files
.  The purpose of documenting files is for easy
reference and to understand how the data were collected to permit as accurate interpretation
of the data as possible.  This is a task that has to be done as soon as possible and updated as
changes occur.  The documentation needs to include who collected the data, at what level in
the marketing chain the data were collected.  All of these pieces of information are critical to
understand during the analysis and interpretation activities of price and market monitoring.
The final data management activity that should be understood is need to establish a regular
schedule to 
backup the data and information
.  Although this activity may sound obvious,
there are a lot of people who have lost irreplaceable files because they did not regularly back
up their data.  The optimal frequency to back up data is a function of how often you enter
data (e.g., daily or weekly) and the amount of data that is entered each period.  For example,
for data that is entered once a monthly for 15 markets for six commodities, it is sufficient to
backup the data after each month's data have been entered.  These data should be stored on
an external medium, whether it is a diskette, a zip disk, or a tape backup system.
Data verification
The purpose of data verification is to identify data entry errors.  Data verification involves:
random comparisons of hard copy and digital data for quality control if data are
entered from hard copies
visual inspection of data in tabular and graphical formats
identification and verification of outliers using range checks
spot checking price data during field trips
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