Analysis and reporting efficiency should be at the core of any plan to use data, analysis and feedback as part of a management system for anything. The purpose of analysis and reporting is not to 'look pretty' but to be effective at getting the right decisions made and good policy implemented.

Efficiency of analysis and reporting. There are many ways to measure and report performance ... no one way is the “right” way ... but some are better than others. The key concepts of reporting in accountancy are very efficient. The principles of accounting make corporate financial reports informative and useful without being extremely long.

Powerful ... not voluminous. Corporate financial reporting is very efficient ... making it possible for a huge organization like, for example, General Electric, to report in a very few pages the activities and results of perhaps 300,000 people. This is done using a Balance Sheet, a Profit and Loss Account and a Statement of Cash Flow.

Limited scope. The problem with decision making based on money profit accounting and reporting is that accounting only takes into consideration the 'money transactions' and everything else is outside the scope of the system. So decision making about money profit has been helped enormously by the money profit accounting process, but society has been short changed.

TVM value accountancy is structured in a manner similar to money profit accounting, but about both money transactions and value transactions.

Reporting is about both money profit (cash flow) and about valuadd ... a system that has drawn from the idea of 'Triple Bottom Line' or profit, people and planet ... a system that has also drawn from many other initiatives looking to expand scope beyond the money metrics of measures like GDP.

People level feedback. Experience has shown that performance improves when there is active feedback and there are the data that enables people and organizations to make decisions based on data, then be held to account for decisions made. People may not like it ... but their performance improves.

The purpose of analysis is to get a better understanding.

The data are neutral ... the analysis then produces results that might suggest some conclusions.

It really does not matter what analysis is done as long as the result is better understanding, improved decision making and improved performance

In my view, over the last few decades there has been a trend to doing more academic studies that have scientific rigor on top of data that are very limited. The results have frequently been inconclusive and then subject to academic argument over statistical method, interpretation and all the rest. Rethinking the whole process with a clear understanding that the purpose is to improve performance would result in a very different approach.