Using existing data

There is a huge amount of data that has been collected over the years ... but almost all is dramatically underutilized. In fact, in the relief and development industry, a very large proportion of the funds have been used for surveys and data collection, with rather little spent on doing what was most needed ... helping to fund practical activities.

Sixteen studies of the health sector

Why would anyone want to have sixteen studies of the health sector ... but this was the outcome when Namibia made health a priority for its first development plan after independence and sixteen difference donors made doing a health sector needs assessment study a pre-condition to funding anything else.

If there was no data, that would be one thing ... but three major studies had been prepared within the previous 12 months ... by Germany, by the UN system and by Namibian professionals ... all of which were available and clear about what needed to be funded.

There is a huge amount of old data ... and there is the potential to use these data as a starting point for better data about development. Mobilizing these data is a challenge ... and potentially very valuable. New data might well cost $20,000, yet similar existing data may well already exist, and could be retrieved for a small fraction of the cost of getting new data.

But while the cost argument appears strong, there is little effort evident indicating a movement to use of existing data. The reasons for this appear to be governmental concerns about sharing official data, and corporate and academic concerns about sharing intellectual property that might possible have the potential to be monetized for profit.

Data collection

In a corporate setting, data collection is done throughout the organization, and recorded in a systematic way. For TVM Value Accountancy data collection has to be done throughout society, and also recorded in a systematic way. Data collection can be done by anyone and everyone.

If you know something, TVM Value Accountancy provides a way for this information to be used as a part of the body of data that are needed. This is addressed in the section on functional structure.

There are situations where statistics don't cut it

How big is the fishing fleet?
These data were meant to be compiled for me prior to my arrival to do a fisheries resource management study. But the researchers failed totally to get any meaningful data because they were trying to figure out how to “sample” a fishing fleet and its operations and then use statistical calculations to get some results.

To get data about the fishing fleet ... a better way was to use the Fisheries Department data on fishing boat registrations ... a permanent record of all fishing boat registrations ever ... and then sample this to do physical validation of the data and confirm something about the validity of the dataset.

What is the fishing effort?
To get data about fishing effort ... it was possible to classify the fishing fleet into different types of vessel, learn something about each type of fishing and then get data about how much of the fleet was operating every day. These data gave a very good measure of the fishing effort not only for the fishing fleet as a whole, but for the various fish catches.

An approach that was driven by an analytical accounting mindset yielded more information rather than less ... had more accuracy than statistical method would have had ... was done in less time and with far less cost.

There is a reason why “journals” and “day books” are the basic books of original entry in old fashioned accountancy. Every transaction is written down every day. Regular precomputer accounting used day books and journals to record financial transactions and to start the process of accumulating the information. After compiling information in a daily record, the information was then “posted” to accounts where the data starts to have analytical meaning.

In a computerized world, storage is now electronic, but the concepts of organization do not change.

Making data accurate

The accuracy of data is critical. If the data are valid and respected, the data have power. Data may be seen to be valid when the same view of facts appears using data from different sources. The functional structure described in this paper addresses the question of how different dataflows will provide confirming validation. Data validation takes place in many different ways. In a good system, it is almost impossible to fool the oversight team because the data are being looked at and validated from many different independent perspectives.

Getting control over the data

Accounting has value because the data flowing through an accounting system are generally reliable. The data are reliable because they are organized and under control from very early on. Because of this they can be checked, and errors identified. In an accounting system all the data that are needed are recorded ... and registered.

In the design of an accounting system there are processes that ensure the accuracy and reliability of the data. These include the ideas of:

• Internal check ... that ensures the data collection process is functioning in the intended manner;
• Internal control ...that requires data to be processed in the prescribed manner; and,
• Audit ... that checks the way the process is working, using either internal audit staff or independent professional auditors

There are also checks that are associated with expectations of what the data should be like. It will be very rare for the crooks to know all of the validations that are going on. Some of the validations include:

• Is this what was expected? If not, why not?
• How does this compare with the past?
• How does this compare with some other location?
• How does this compare with some other organization?
• How do cost compare with value realized?
• How do costs compare with budget?
• Etc.

Understanding getting missed in search for money profit

For many years tax attorneys and accountants have encouraged tax saving strategies based on the provisions of tax depreciation laws and regulations. Many of these achieved a short term tax reductions goal ... but in the end the investment was lost. Simply put ... there was a modest tax saving, and a considerable investment loss. Fast talk and fees drove the marketing of these vehicles ... but there was nothing to clean up the mess and make the players accountable.

And cost accounting can be expensive ... with it being much less costly simply to have management and supervision that have some appreciation of cost control and their role in optimizing costs.

Management by Walking Around - I

Eyes are a very powerful management tool. They provide a good link between reality and the numbers. For years accountancy students were taught that an auditor had a responsibility to “see” the inventory as part of the audit routine since it is very difficult to have a very big fictitious inventory and not be able to show something to the auditors.

But I use walking around as a way to see things that do not find direct expression in the numbers and reports ... working conditions ... factory noise ... happiness levels ... excess inventory ... obsolete ... factory effluent ... scrap ... workflows ... excess staffing or under staffing ... etc. etc.

Management by Walking Around - II

I used to reckon that after a morning walking around a factory or construction site I would be able to identify hundreds of thousands of dollars worth of performance improvement. Part of this was because I saw things myself ... but part was because the department managers knew a lot that they shared. Simply by walking around, a whole lot of sharing became possible. Operating managers know a lot, and rather little of it gets put into use.

Low cost data acquisition and accessible data

Specialized PDAs (personal digital assistants) have been used for a number of years (since around 1995) to reduce the burden of paper based data in mobile situations. Organizations like Federal Express and UPS were early adopters of this specialized technology, and it has been adopted for many applications where accuracy and speed are important (for example inventory control). The use of a PDA is cost effective when labor costs are high and the use of data has a high value. PDAs are rarely low enough in cost to be of advantage in low wage settings ... but they have been deployed by AID agencies using grant funding even though the sustainability of their use is near zero.