Cost effectiveness of data collection. Data collection always has a cost ... but does not always have a value. Good cost effectiveness of data collection requires as low a cost to do the work as possible and only collecting data that are going to be useful.

Data collection is optimized when the data are collected using techniques that are appropriate to the type of data. It is valuable to get good permanent data. By getting high quality in the permanent data, everything becomes very much easier and the information rapidly gains credibility. With high quality permanent data, transient data becomes easier to collect and can be related to data of substance.

Where the data are being collected for use in a relational analytical environment, the permanent data are all accessible to any transaction related to this permanent data. To use some practical examples:

1. 1. All the information about a community is permanent, or at least permanent at a balance sheet date. All the activities in the community can be related to this community and analysis done about activities and results relative to this community.
2. 2. Information about a specific location can be related whether it is the house and its construction, the people living in the house, the bednets being used in the house, the IRS that has been done, the malaria that they used to have, and the malaria that they now have.

Local people collecting local information. In order for data collection to be cost effective, local people have to be collecting local information, and they must be doing it using low cost techniques.

No one data collection approach is likely to be universally optimum. So much depends on the training and experience of the people in the community, and the practical issues of access to information technology and communications infrastructure.

A hybrid system involving both manual forms and electronic systems will usually be the way forward.

Data that are used are almost always right ... data that are collected and never used are most often wrong and useless

Recording the data is also very basic. Write the key information down, preferably in ink and in a book, not a loose piece of paper.

Data collection workbooks. In addition to the interesting data that describes the transaction or activity and the cost also add in the key information needed for reference purposes later on. This includes things like:

1. 1. Where?
2. 2. When ... data and time?

Data from these books can be copied to an electronic database from time to time and made part of a cost analysis framework. Some “research” will have to be done along the way to make sense of all the information, and to make it complete. Most of the data are known, the challenge is to get all the data together in a single framework so that the information is meaningful for analysis.

Data collection optimization. Data collection can be optimized ... but the techniques used for data collection rarely result in an optimized outcome.

Unless the basic question “Why are the data being collected?” is answered correctly ... the methodology used for data collection is likely to be wrong. In the TVM framework the reason for data collection is simply that TVM aims to generate useful management information ... and management information is defined as the least amount of information that will ensure that the best possible decisions will be made. In this TVM framework, the data that are collected my well be a subset of data around a specific issue that has already been identified as important.

If the question is answered along the lines that the data are needed so that a research report can be prepared that is a requirement for an academic certification ... then the data will be collected using a very different methodology.

Collecting data about the fishing fleet. A group of experienced scientists were asked to collect data about the structure of the fishing fleet. They designed a survey and statistical method to make their inquiries and did a perfectly random set of interviews three times a week for six months. At the end of this time they had nearly nothing of value.

I was faced with the problem of time and money used and no useful data. I am an accountant that does not particularly like statistical data. Every fishing boat has a license. To get a license the fishing boat must be registered ... and to get registered a form has to be filled in, and is filed somewhere! I found the filing cabinets and now had details of every fishing boat ever registered ... date of registration, size, type of construction, date of construction, engine make and horsepower, fishing gear type, refrigeration equipment or not, etc., etc.. After a day of data entry typing there was a respectable database. After a few days of checking at the fishing port we were able to verify much of the data in the database ... and now had complete and good data about the fishing fleet.

This cost effective data collection was obtained by building on data that was already available ... but unused because it was in another department!