![]() Date: 2024-08-17 Page is: DBtxt003.php bk009080700 | |||||||||
TrueValueMetrics
ACTION INFORMATION FOR ALL OF SOCIETY Metrics about the State, Progress and Performance of the Economy and Society Metrics about Impact on People, Place, Planet and Profit Chapter 8 - ABOUT DATA 8-7 EASY DATA | |||||||||
Making Data Acquisition Efficient ... Use what is available! The fact is that there is a huge stock of data … much of which never gets used. Some is compiled at great expense, and then used just once and forgotten about. Consultants have been paid enormous amounts of money to study a variety of things … in practically every case the work includes compiling data, doing analysis and drawing some conclusions. Once the study is done, the data exist, but do not get used again. The system is high cost and inefficient. These data can have value in a system that seeks to understand community state, progress and performance at least cost. Easy data are everywhere Some data are easy to acquire ... some very difficult. To the extent possible, easy data should be used as much as possible. These data may sometimes be obtained very quickly. The key is not to ask for specific information in a specific form, but to ask about what data are available that broadly relate to the subject at hand and use these data to the maximum extent possible. In many cases these data are easily available. Some easy data have the added advantage of providing some history from past periods that cannot be obtained in a data acquisition program that is only collecting current data. Data repositories and documentation centers A surprising amount of data exists … but it is only going to get found when there is some pro-active search. Much older historic data are in paper documents … and while not immediately usable in electronic media, the data may be transcribed if it seems to be of some value. Of course care should be taken in using data … whether new data or old historic data, that the data represents what it purports to relate to! Much data has been “fabricated” over the years and served to satisfy some dataflow conditionality without in fact representing any reality at all.
Walking around … observation and perception A large amount of data may be obtained simply by “walking around” … but converting this into a useful record is not particularly easy. Increasingly this is being done using photographic images, but too often there is inadequate labeling of the image. The time and the place are critical information … together with some brief narrative. Training in “observation and perception” is helpful … too many people do not see what there is to be seen. Hardly anything of what people see gets into any system of metrics about the progress and performance of society. This has to change! Not more and more date … more information. The goal is not to get more and more data … but to get more and more understanding of the community and the socio-economic state, progress and performance. Some duplicate data is an advantage. When the same set of facts is reported using two separate sets of data, there is a good probability that the data are accurate. If there are three separate sets of data also showing the same set of facts, then it is very likely that the data are accurate. More sets of data after this, does not add anything except cost. Data about other things adds to understanding. If one set of data are about health, another set of data about education would be interesting … and any other sector that seems to be of importance in the community, especially the production sectors. Advanced common sense The key goal of data acquisition is to have data that are useful and help improve performance. The goal of TVM is not to have data suited to research studies, but to have data that are useful for decision making and measuring performance.
Sometimes, the understanding of data may be enhanced by statistical study ... but good techniques of data collection, accounting and analysis are usually sufficient to get good management information for decision making. The key is to fully understand what data are important and what issues have a material impact on performance. | |||||||||
|