Date: 2024-09-27 Page is: DBtxt003.php L0900-COofCA-2009-030000
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Burgess Manuscript
CONCEPTUAL OVERVIEW of COMMUNITY ANALYTICS Manuscript Draft from 2009 |
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Chapter 3
Managing for Progress |
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Many Aspects of Progress
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Progress is not all positive ... be mindful of the downside ...
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The Management Cycle
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The basic management cycle is quite simple ... but often absent. CA has data at the center of the
management process. Data are needed to develop management information which is central to the process
... to know what is being achieved, and how it can be improved.
“Management information is the least amount of information that enables a good decision to be made in a timely way.”This is a simple representation of the CA perspective of the management cycle. 
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About Data ... Metadata
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Data ... the basis for everything!
Ubiquitous ... Data are everywhere. The more we learn about life ... about almost anything ... we learn that there is a data component that makes life work. The brain is all about data ... When the post industrial age was named the information age some decades ago ... perhaps around 1970 ... there was little appreciation of what role information ... data ... played in the functioning of everything. Compared to what is possible modern institutional management uses data systems that can best be described as stone age, or at best, medieval. The purpose of data is to make it possible to manage well ... for operations to be efficient and effective, and for knowledge to be accumulated. For this data are needed about what is going on ... timely ... accurate ... relevant. Data are the raw material for management information and the development of knowledge. Data have no purpose unless they are used in some productive way, unless they are part of an integrated system Intellectual property The recognition that data have value has been important in making it possible to collect data, process data, and manage with data ... but the downside of this has been that data and related analysis has been managed as intellectual property (IP) ... and this property then being exploited for its value to its owner rather than being used for public good. The issue of the “public right to know” is not central to much debate ... and this has made it possible for public sector performance to be very low efficiency and nobody any the wiser. What a corporate organization tells the public is only a tiny amount of what the company knows ... and is carefully presented to send a message that is designed for the stakeholders, and not much related to the underlying data and knowledge. The rule seems to be that only data that are required by law to be accessible to the pubic are going to be accessible ... everything else is going to be secret. More than anything else, this means that society will progress way more slowly than it would where data and analysis were being used to the optimum. The argument that the value of IP produces an incentive to use data and innovate has some merit ... but so also does the argument that professionals and scientists are not only motivated by money, but also see value in discovery as a value beyond just its money value. Public access The CA methodology is to have data and analysis as much as possible openly accessible. Data and analysis that might be useful for decision making are made openly accessible as rapidly as possible. The CA approach that makes data and analysis easily accessible contrasts with the widespread practice of treating data about public matters as a proprietary private property. Data and analysis that might put people “at risk” are not openly accessible. Permanent data and transient data Data may be characterized as either permanent data and transient data. Permanent data changes slowly, while transient data is changing all the time. For example the name of the town and its location are permanent data, while the current weather is changing all the time and is transient data. Transient data sometimes changes very rapidly ... for example data about economic transactions, while the results or impact changes more slowly. In accountancy, the operating statement reflects the aggregation of transaction data, and the balance sheet an aggregation of items that change as a result of the transactions. This is reflected in the accounting constructs of balance sheet and operating statement, with the balance sheet representing the more permanent data and the operating statement the more transient data. This is not, of course, very rigorous, since in a good accounting system both the balance sheet and the operating statement are the result of summing all the individual transactions. In practical terms this translates into an ability to verify balance sheet reports more easily than one can verify transient operating statement transactions. This is a vital matter, because fraud and corruption can easily take place within the activities of an organization and the funding of these activities, but it can easily be detected if there is meaningful oversight of the results and the balance sheet that puts result on the record. What data are needed CA is a system that has community ... a place ... as the core of data collection and analysis. All the data are linked to time and place. Every fact that is going to be important in decision making about the community is needed. Broadly this breaks down in the following sections:
Spatial information Spatial information ... maps ... are a critical part of the information needed for planning and the management of operations. In real estate the saying goes that the three most important features of a property are location, location and location! Everything has a spatial characteristic, and from a cost effectiveness and performance perspective, spatial information is important and central to the way CA records data and does analysis. Mosquito and malaria control have strong spatial characteristics that have a very large impact on performance. Accordingly spatial information and mapping are a very important part of cost effective high performance integrated malaria management and include:
Time Series Information In addition to mapping that shows the simple spatial dimension of the data, there also needs to be an ability to understand the changes that occur over time about a specific place and a specific characteristic of the data. Time series information is also critical in the measurement of progress. The goal is to have progress, and to do this as fast as possible, and in ways that are cost effective and with a minimum of undesirable side effects. All of this is best done in a data environment where there is good time series information. Account codes ... analytical codes The power of relational analysis is maximized by the design of the analytical codes. This is the key to easy analysis, and relatively easy to do for a relational database. Frequently, however, it is ignored and easy analysis then becomes impossible. Multiple use of data The most cost effective data are data that are used in many different ways. There should ideally be one pool of data, and this one pool should be used in different ways for the specific analysis needed. Essentially the analysis is another view of the data. In the IMM context local data is first used to help with local operational decisions, then is used within an operational management and oversight module that addresses cost effectiveness and performance issues, and finally is used for scientific research to help have a better understanding of the underlying science and more fundamental problems that might be emerging. A lot of good data is far better than a little perfect data A key concept for success in the context of integrated malaria management is to have data that have meaning. The goal is not to have perfect data, but to have useful data that facilitates good decision making and helps in achieving a cost effective reduction in the burden of malaria.
Data Acquisition and Transmission
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:
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:
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. Handling sensitive information ... cost accounting Some information is quite sensitive, such as pay rates and benefit packages, and the like. Though they are sensitive, they are also important to understand since the cost of activities is very much a function of the cost of people. Cost information ... cost accounting is often a missing link. There is a dire lack of good cost accounting. Even though computer based accounting systems are commonplace, they are rarely being used to develop data that may be the foundation for cost analysis and reporting, and in many cases may not be able to provide cost data that are useful. Where cost data are collected, they are rarely accessible to the general public and are kept within an organization, and even then quite often not easily accessible. Cost information is treated as if it is very valuable ... which it is! However, cost information should be reasonably accessible. There are two problems that have to be addressed:
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 CA framework the reason for data collection is simply that CA 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 CA 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. 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! Dataflows A functional planning and operational framework needs a dataflow system and management information. Without these, it is as dysfunctional as a human being without a nervous system. The complex institution framework for malaria control is operating with very limited performance metrics. There are pieces, but not a complete framework. Most of the analysis data are derived from very small surveys and statistical manipulation, with very little of cost accounting, and even less of cost effectiveness analysis. The following describes in simple terms how CA data about community is collected and used. Data are most cost effective when one set of data are capable of being used in many different ways ... in this case at both the local level and the academic or scientific level. The key goal of data collection is to have data that are useful and help improve performance. Local data collection ... local analysis ... local action is the cycle that improves performance most directly and most quickly. Having the data also used at a “higher” level facilitates oversight and the sort of monitoring that can be used to identify the need for corrective action by the analysis of much larger sets of data. At a higher level there can be analysis that identifies “best practice” and issues that are impossible to identify with local analysis alone. Data flows
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Data Organization, Storage and Access
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Organization
Data are essential to transparency and accountability but data that are needed are rarely easily accessible. Data are needed for the effective management of performance ... but it is not at all clear that the essential data are collected ... and to the extent that they exist, they are not easily accessible. Laws and regulations Because data are important for the administration of society, it is normal for there to be laws and regulations that give guidance about how data must be stored and be accessible to interested parties. In general these laws and regulations do not help very much with the issue of transparency and accountability as a part of day to day ordinary life. The issue of socio-economic performance and the impact on society is not part of the data landscape. The corporate organization is increasingly aware that data storage is a cost in the best of times, and may be a catastrophic cost if the law and regulations are called into effect for access to these data. Technology Data storage has moved way beyond just paper ... everything can be digital ... everything can be organized so that there may be easy analysis and the data be valuable ... especially for society as a whole. The cost effectiveness of technology is only going to be fully realized if the data architecture is sound and logical. This is the core of what CA can do. Storage The details of the storage architecture will change from time to time ... but the general theme is that data should be accessible easily for those who need the data to make good decisions. Data in the hands of a data collection person These data are needed so that the work of data collection can be as efficient as possible ... including some immediate feedback about changes that might be locally important. Data at the community level These data may be analyzed very quickly to provide the information needed at the local level to determine what are the issues and how they might best be addressed. Data at the national oversight level These data are a component of the data needed for good governance and oversight. Data for national level research These data are a part of a research process that has the potential to help with both learning and teaching in the country Data for global research These data are a part of a research process that has the potential to advance learning on a global basis. Modern computational technology such as available at the US National Center for Supercomputing Applications (NCSA) makes it possible to process very large datasets and learn from these data. Access Multiple use The multiple use of data is a key to making data cost effective and valuable. The basic data architecture used by CA maximizes use of data. This has the secondary effect of making the data more reliable, because data that are used are always more reliable than data that merely sit and do nothing! |
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Objective Analysis
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Cost effectiveness of data collection
Purpose The purpose of analysis is to understand the data. The purpose of reporting is to make it possible for others to share this understanding. For CA data are neutral ... they do not represent opinions. Data flows into a store and all data are analyzed so that conclusions can be drawn. For CA the goal is reliable management information so that good decisions may be made. CA is built on the concepts of accountancy, and accountancy is, at its core, a system for recording economic transactions in an organized manner. In the corporate form, all the economic transactions are put into the record ... ALL ... and the analysis proceeds from there. In accounting there is no statistical component to the recording of transactions. In accounting great care is taken to prepare reports based on all the relevant transactions and not just a subset that may or may not reflect all the data. This is a very different approach to surveys and statistical studies done for research. Financial reports are not research ... they are merely all the data summarized according to the basic principles of accountancy. Analysis must have clarity CA analysis has a community focus. The frequency of CA analysis depends on the natural frequency of the subject matter and the objective of the analysis. CA reporting aims to make the result of analysis easily accessible, convenient and timely. The purpose of CA reporting is to facilitate decision making that improves the quality of life of a community. One of the first steps is to be assured that the data are what they purport to be. Data should be easily verified ... and data that cannot be verified should be treated with the utmost caution. Sadly, this is no longer universally true because accounting principles have been superseded by various laws, rules and regulations that allow various forms of reporting of financial results that are in conflict with the underlying principles of accountancy but suit various stakeholders in the process. CA builds on accounting principles, applying them to a reporting unit that is the community and incorporating the double entry of both money and value. There are many tools available for analysis and reporting included techniques like (1) aggregation analysis; (2) time series analysis; (3) value chain analysis; (4) various forms of cost analysis; and, (5) various way of looking at impact and cost effectiveness. Analysis of aggregated activities Accounting has good concepts for how different activities should be aggregated so that there is a minimum of double counting and distortion ... this is consolidation accounting, and it is very useful when rigorously applied in the analysis of community performance. Cost analysis and cost efficiency Cost accounting is a subset of accounting that informs about how much it has cost to do something. This is not difficult to do when there are trained staff and there is a good accounting system. It is painfully tedious and difficult when the record keeping is simplistic and critical data not available. This is a reflection of management competence and priorities. With cost analysis it is possible to move on to evaluate whether or not the operations are efficient. One way of doing this is to compare what is being achieved with what should be achieved. Impact and cost effectiveness. In theory, the reason for doing the work is to get a result. The result has a value ... a social value which should be given a value. In the case of health interventions the impact should be more good health ... and good health has value. It is not easy to quantify this, but CA avoids this problem by assigning standard values to most of the outcomes of community activities. Value chain analysis Value chain analysis is used to identify the winners and losers in various parts of the economic structure, and makes it possible to understand the systemic flaws in the way the economy operates. Value chain analysis is used to show cost and profit distribution across multiple areas and organizations as in the petroleum industry, or across time as in the case of education and the student's subsequent career. CA makes use of the idea of value chain analysis. What appears as a success, may have negative impact on other parts of a value chain. Value chain example Value chain analysis is used to identify the winners and losers in various parts of the economic structure, and makes it possible to understand the systemic flaws in the way the economy operates. Value chain analysis is used to show cost and profit distribution across multiple areas and organizations as in the petroleum industry, or across time as in the case of education and the student's subsequent career. Value chain from raw material to consumer is important. It shows why some companies are very profitable and others are not. The value chain show how costs accumulate and profits are extracted from the value chain. Petroleum The CA petroleum value chain explains the costs and profits between the origin of oil in a poor part of the world to gas being used in rich places. It explains how excellent crude oil in the Niger Delta makes some Nigerians super rich, with the country remaining terribly poor. It explains the links between high gas prices at the pump and production costs ... and how markets work! Coffee The CA coffee value chain does the same for coffee. How is it that coffee consumed in a retail coffee shop is many times more to buy now than years ago ... but the price paid to farmers for their coffee has increased so very little. Where is the money going? Value chain shows that some of the organizations that were created to make the economic playing field fairer for the farmer have ended up being merely a way of extracting profit from the value chain without doing anything in return. Value chain over time is also important. The value chain technique may also be applied over time. In this case an activity that has costs today creates profit and value tomorrow. Education The education of a child is a big expense ... but it is an investment that will pay back many times over the life of the person. Value chain analysis shows something of how a cost in early years creates opportunity for benefit in later years ... and could be the basis for economic analysis to justify investment not only by parents, but also by society in education and building human capital for the future. Health Treating disease is also a big expense ... and again with an economic dynamic that changes over time. A strategy that invests so that there is no need to treat disease because the disease is controlled or eradicated is probably better than one that merely waits and treats the disease and incurs the associated costs. Value chain helps to determine whether prevention rather than cure is the optimum strategy. Infrastructure The building of the US Interstate Highway System is another example. The system cost the US Government more than $100 billion ... but the immediate incremental property values around the country were way more than this ... and the productivity improvement of the national economy way bigger, and long lasting. Value chain analysis of this shows how amazing big good investments can be for society. Understanding priorities and needs A good starting point is to recognize that every community is different, and what is a top priority in one place may not be the same in another place. Priority needs are both a reflection of physical and human characteristics at a point in time, but also a reflection of history and what has been done in the past. Caveat To use the sporting analogy ... the game is already going on. How do you make some useful contribution as a scorekeeper when the game has already started? One way is to find out what has already happened and start off with the score as others are reporting it ... and then to keep score on a continuing basis from that point on. This seems reasonable. With these CA data and analysis it becomes possible for everyone to know a lot more about socioeconomic performance than would be the case without. In sport, the score determines which team wins, but the statistics of the game show which players contributed the most to the result. Value chain from raw material to consumer The value chain from raw material to consumer helps to show why some companies are very profitable and others are not. The value chains show how costs accumulate and what profits are extracted from the value chain. 1. The petroleum value chain helps to explain the various connects and disconnects between the origin of oil in a poor part of the world to gas being used in rich places. How is it that excellent crude oil in the Niger Delta makes some Nigerians super rich, with the country remaining terribly poor. How is it that there is seemingly little rational link between high gas prices at the pump and the costs of producing this gas? How do markets work ... and who do they work for? 2. The coffee value chain does the same for coffee. How is it that coffee consumed in a retail coffee shop is many times more to buy now than years ago ... but the price paid to farmers for their coffee has increased so very little. Where is the money going? Value chain shows that some of the organizations that were created to make the economic playing field fairer for the farmer have ended up being merely a way of extracting profit from the value chain without doing anything in return. Value chain over time The value chain technique may also be applied over time. In this case an activity that has costs today creates profit and value tomorrow.
Time Series
Year to year comparison By measuring what is easy and useful, it is possible to keep costs down and get some material value. Is there progress? Take any issue and ask the question “Is there progress in the last year?” ... and maybe also over the last ten years! If the answers are yes, then there is progress. Comparative balance sheet CA draws on the experience of financial reporting. CA reporting aims to be clear and relevant, and does this by reporting in a modular manner around issues that are material for the community. The first level of CA reporting is the comparative balance sheet. How do key matters of the community change from period to period, that is year to year, or month to month, or at even more frequent intervals. Most good, stable communities change rather slowly, and there is little that is material to report. Poor communities, on the other hand may have a lot that is material because, for example, small changes in crop production can easily magnify malnutrition in children and mortality. Measure what is most useful ... but may be more difficult to do If there is progress on many issues ... but not on some important issue ... get data about why this issue has not progressed. This may be easy ... or very difficult ... but its value is significant, because an issue not progressing is a constraint, and maybe a chronic matter for the community Why is this issue not progressing? Take any issue and ask the question “Is there progress in the last year?” ... and maybe also over the last ten years! If the answers are yes, then there is progress. A comparative balance sheet is a simple example of time series ... two datapoints. Having datapoints associated with time makes it possible to do time series. Tine series show how things are progressing or regressing over time. The time interval should be a balance between very frequency and cost and the value of the associated results. Sometimes data needs to be daily, or even more frequent ... sometimes once a year is enough! Changes over time are very informative. One bit of data is better than none ... but the same data over time starts to tell a story. Are the data telling us that the situation is getting better or worse? Are the data telling us that there is a seasonal characteristics? How do these data sets compare with data from other places? Do changes shown by these datasets show a causal relationship with actions or events that can be identified? These time series are immensely powerful ... and become even more powerful when they are used both in as simple a form as possible and also in ways that facilitate complex searches for correlation. So much is driven by time series So much is driven by time series ... whole theories of capital market behavior have been developed around this ... scientific analysis ... economic analysis ... etc. But hardly anything has been done at the community level to understand poverty and progress. CA is setting out to change this. Time series trends are great indicators of progress ... or not. Time series are simple, clear and powerful. While it is possible to do advanced statistical manipulation ... simple and clear time series tables and charts work very powerfully as well. There are many different time periods that may be used. The choice depends on the natural characteristic of what is being measured.
Changes over time are very informative. One bit of data is better than none ... but the same data over time starts to tell a story. Are the data telling us that the situation is getting better or worse? Are the data telling us that there is a seasonal characteristics? How do these data sets compare with data from other places? Do changes shown by these datasets show a causal relationship with actions or events that can be identified? These time series are immensely powerful ... and become even more powerful when they are used both in as simple a form as possible and also in ways that facilitate complex searches for correlation. The Bloomberg System The Bloomberg organization made data about the capital markets easily accessible to uses of the Bloomberg system. For almost every possible metric about the capital market, the Bloomberg system contains a time series. Time series trends are great indicators of progress ... or not. Time series are simple, clear and powerful. While it is possible to do advanced statistical manipulation ... simple and clear time series tables and charts work very powerfully as well. There are many different time periods that may be used. The choice depends on the natural characteristic of what is being measured.
Time series example Experience from Kwa-Zulu Natal.
In this example the measure was low, then increased rapidly,
and then decreased again. In this example the measure is the
number of malaria cases in the area, which rapidly increased
when the use of DDT was stopped, and then decreased again
when DDT was reintroduced.
There is no reference to cost. It is possible that DDT is not only very effective in reducing malaria, but might also be very cost effective as well. Return The idea of return is very common in the field of for profit investment ... but less so in the area of philanthropy and in public sector initiatives. The concept is simple ... an amount of money paid out now produces a flow of money back in the future ... a return. In practice there are many ways to do the calculation. The different ways of doing the calculation reflect different priorities. Return on Investment Return on investment (ROI) is probably the most common and is used to measure results from the perspective of an investor. ROI is not a good measure of performance from most other perspectives, especially where financial leverage is being used to improve reported ROI. When profits are positive, financial leverage improves the ROI, but the same leverage with losses becomes catastrophic. Return on Capital Employed Return on capital employed (ROC) is a better measure of performance than investment. It measures how much revenue and profit are generated relative to the amount of capital being used, that is, the equipment and machinery, etc. (fixed assets) and the inventory, receivables, etc. (working capital). ROC eliminates the financing costs and leverage and provides a measure that is most closely related to real, that is technical, productivity. Productivity Productivity is a very important measure, especially productivity computed in respect of social values rather than productivity relative only to financial profit. A society that is not productive is unlikely to be sustainable ... and what might appear to be sustainable is not when there is a draw down of the assets of the society. An economy is as productive as its people and its infrastructure ... with infrastructure being used in the broadest sense of the word. For many years, the US had a better infrastructure than most everywhere else, but this advantage has been allowed to diminish over time. There was a time when almost all examples of great infrastructure were the best and biggest in the USA, but that time is long gone. For the past several decades ... since the 1970s ... the US advantage in productivity because of its infrastructure has been in decline ... and with it the standard of living. The following are two examples: There was a time when US made steel was the best quality and the lowest cost. The US used the best machinery and the workers pushed the equipment to the limit ... way better than the Europeans at the time. The US steel workers were paid a lot more than the workers in Europe, but the high productivity in the US kept the cost of the steel low. The productivity of US steel was a function of productivity and cost from end to end of the value chain. Iron ore was mined productively. The transport was highly efficient and low cost ... the steel mills typically located where ore ships could unload directly into the plant. Energy was low cost. Everything end to end was productive. What happened? When the Interstate Highway System was first built there was a huge improvement in distribution productivity. Trucks were able to travel faster and with less wear and tear on the vehicles. Over time, experience showed that much bigger trucks were practical, and productivity went up even more. But all this ground to a halt when the highways became overcrowded. With too many vehicles, the transit times increased, safety dropped, and productivity increases reversed. As overcrowding increased, costs soared. The cost of congestion is not a widely reported metric of productivity and economic performance, but if it was, congestion costs would be seen to be a terrible drag on the productivity of the United States. |
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Responsible Reporting
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Reporting Clarity
A good report is one that is clear, complete and unambiguous. Financial reports are able to report a lot about corporate performance in very few numbers ... a few pages to report performance of an organization with more than 100,000 staff is impressive. In order for this to be reliable, there have to be sensible rules and the strict application of good principles of financial reporting. Though there have been serious problems with financial reporting in recent years ... notably Enron, financial sector institutions, and a number of corrupt enterprises, the basic principles of accountancy applied to financial reporting are very powerful. There are many ways in which reports are prepared in ways that misinform. Often the best way to misinform ... or half inform ... is to report using graphs. Progress ... but at what cost? The data presented in the following examples are unclear and only tell part of the story. They are constructed in a way that provides misinformation. The data must be organized so that this type of analysis is easy. Example 1 Experience from the Marathon Oil, Bioko Island In this example, the graphic clearly shows change over three years. The left three year series shows the prevalence of malaria infected mosquitoes down by 95%. The right hand series shows the prevalence of malaria parasites in children down 44%. But there is no indication of how much this cost. There is no indication about the population involved, and the size of the program in terms of area. The graphic does show progress ... but at what cost? Distorted graphics Thee following is an example of distorted graphics. It should be noted that this appeared in a report that was prepared by a well known consulting firm ... McKinsey and Company ... and distributed at a very high profile international forum ... the World Economic Forum ... by a group that was skilled in PR but maybe not as good on responsible reporting ... Malaria No More. The good new is that they achieved in great measure what they set out to do ... Malaria No More helped to mobilize a lot of money for malaria control activities and in this regard their work was magnificent. But they also set the stage for the money raised to be used very badly and for the results to be puny compared to what might otherwise have been possible Example 2 Experience in Zanzibar 2000-2006 In this example morbidity has declined by 77% according to the report and the graph, and this is a good outcome. But is it the whole story. This relates to measures at the clinic ... less malarial incidence results in less attendance at the clinic ... but what about those that do not have access to clinics. Mortality is down by 75% according to reports ... but this is mortality among the young children subset. The question about cost is not addressed. Is this the most cost effective way to reduce the malaria impact. Maybe it is, but the information is not presented. The main interventions were bednets and free delivery of ACT medication. Cause and effect The cause and effect at very important pieces of information. It is difficult sometimes to “prove” causality, but good management and decision making makes it very important to have data that facilitate good judgment and supports not only the making of a decision, but validating the quality of the decision as quickly and as reliably as possible. Example 3 Experience in Eritrea 2000-2006 The morbidity was reduced based on the number of visits to clinics by 63%. The mortality was reduced by 85%. A small survey of 2,300 households suggests that bednet distribution has reached 67% of the population in Eritrea. This example is a simplification that shows progress, but does not explain why or at what cost. At the national level there is progress but regionally within Eritrea there were areas that progressed well and areas that did not improve very much. Why was this? Was it because they were already malaria free, or was it because the interventions were ineffective ... important questions that should be guiding policy and program. There is no cost information included that shows cost effectiveness. Report Formats CA reporting has more of accounting than it has of statistics. The goal is for everything that is “material” to be accurate. Roll up and drill down The CA reporting format embraces the accounting ideas of “roll up” and “drill down”. The CA reporting framework has the community as the primary unit for accounting. The data about community may be aggregated to provide reporting at the district and provincial level as well as at the national level. The same framework of data is used at each level. The “roll up” and the “drill down” of the data provides a coherent set of data for decision making at the national policy level and at the tactical operational level in the community. The following shows the “roll up” and “drill down” framework Country Province District Community Location About “state” and “activity” The data are of two types: (1) data that describes a state; and (2) data that describes an activity. This is the same concept that is used in corporate accounting where there is a balance sheet (that describes a state) and the operating statement or profit and loss account (that describes activities). The changes in state are a result of an activity. Progress is most accurately measured by observation of the state. It is possible to have activity with no change in state and no progress. The following shows the tabulation of any item from State 0 through Activity 1 to State 1 and then through Activity 2 to State 2 ... and so on. Typically this is for an annual period. Item State 0 Activity 1 State 1 Activity 2 State 2 By month seasonality Many of the elements of the community socio-economy have seasonal characteristics, and the data needs to be compiled to show this. Using a monthly reporting format will show seasonality clearly. Item Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year |
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Feedback
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The value of metrics is most when the data are used with feedback to improve decision making.
The only public sector organization that had very good performance feedback in my
experience was the United Nations High Commission for Refugees (UNHCR). They were
able to move from problem identification to effective action in hours, and keep its program
relevant in very fast changing and often challenging dangerous conditions. No other
organization in the Official Development Assistance (ODA) community had anything like
the feedback capacity of UNHCR.
Basic control theory shows that when there is rapid measurement that feeds back to the operations, the system can be both stable and performing to its optimum. CA uses metrics in this manner. This is very different from ex-post-facto monitoring and evaluation (M&E) which is normally too little and too late to make much of a difference. M&E mainly what results were achieved ... good or bad ... but too late to do much to improve the outcome. The following experience is from 1973 shows, however, how powerful feedback can be when it is used in the right way and at the right time. This story illustrates the vital importance of timely information. Most of my career I have been associated with corporate accounting, consulting, planning and the analysis of performance. I have not done many line management assignments ... but in this case some years back I was appointed VP Manufacturing for Southern States Inc, a manufacturing company making air-break switches for the electric utility industry during a reorganization to improve the company's results. The company had orders, but the factory was a bottleneck ... and we had neither the time more the money to invest in expanded manufacturing facilities. We had to do better with what we had. For years the factory production report had been written up and distributed every day around 10 am ... informing everyone of the production numbers for the day before ... a fairly standard practice! I changed this to give management a report at 8.30 am (the factory got started at 7.30 am) about the anticipated production for the day ... today, not yesterday! By 9 am the support staff were deployed fixing problems that would improve performance today! The factory always beat its anticipated production ... and the factory production almost doubled without any major capital investment to expand the capacity! |
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Technology
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Technology to facilitate paradigm shift
Throughout history technology has always been the primary limiting factor in making sustainable progress ... but there has been a shift in the last few decades. Technology may now have the power and capacity to do far more than our society will allow it to do. CA was designed to be independent of technology ... the data are a logical framework that does not need technology ... but this becomes a million times more powerful when it is matched with the capabilities of technology. CA is about data much more than about technology. The ideas of CA were applicable when paper was the storage medium, and the same ideas still have application in a fast moving digital age. Powerful technology and analytical capability should not be used as a substitute for good data. There is no more place for sloppy concepts in a powerful analytical environment than in the much more power constrained situation of earlier times. Rapid changes in IT economics are taking place, and it is likely that this will continue. Computational power has increased exponentially for many years and the potential is a long way from being fully utilized. Stationary centralized computational systems have given way to distributed systems ... to the Internet and to mobile systems. The power has gone up and the costs have come down. If the cost to power relationship has improved by a factor of 1 million over the past 40 years ... how come a data centric profession like accountancy are not a million times more useful? Why has so little of the potential been used for public good? Internet and World Wide Web While CA is built on concepts that were applicable for pre-computer accountancy, the architecture of the data also works for an electronic environment and Internet accessible data and analysis. As Internet technology has evolved, the need for and use of “broadband” has increased, and most applications now require broadband access for the Internet to be an efficient tool. This has the effect of making the Internet a limiting factor for the universal deployment of CA. The combination of Internet and other technology driven tools now makes data centric programs cost effective. Satellite imagery This image shows individual houses in a section of Monrovia, Liberia. Images of this sort enable plans to be made for surveillance and for interventions. The image is a start ... how it is used to plan and deploy interventions depends on the local situation and the staff on the ground. Specialized PDAs 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. Mobile phones Mobile phone technology has produced a paradigm shift in communication. The deployment of cellphone technology has been very rapid, and a very good example of a low cost technology producing a very high value ... and marketed in ways that have made the service affordable to customers in a broad range of economic circumstances. Mobile phones have both data and analog capabilities, and this enables both text or data transmission and image capture and transmission. It is unclear how much of these technologies can be deployed immediately, but it is clear that rapid change is happening. Social network web architecture Social network web architecture is changing how people interact, and how knowledge is used. In general most of the data moving around social networks are of little management value, but this can change. The same data architecture that links people with people may also be used to link problem to solution and the resources needed for everything to come together. Village bus data transfer While most systems that have been developed have been for markets that are rich and where profits can be made, there are emerging systems that are designed to bring value to communities in the very least cost manner. Community focus data can move in and out of a community using methods other than Internet broadband ... as for example the village bus data transfer system, where data are moved from a community based system to a traveling intermediary system and on to a central datastore. |
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