This is a fictional situation report from the fictional nation of Patria that aims to illustrate the issues associated with managing the data, statistics and records used to measure the Sustainable Development Goals (SDGs) in lower- and middle-income countries. This approach should make it possible to present key issues without identifying individual countries and it should allow them to assess the extent to which the fictional situation matches their own realities. A close match will suggest greater relevance. It is hoped that this will increase awareness and understanding of issues and their consequences, leading to concrete action.

Background

Patria is one of 193 countries that signed on to the SDGs initiative led by the United Nations. Within the government, the Ministry of Government Affairs (MGA) is responsible for coordinating its commitments under the initiative and for submitting the statistics to the UN Statistics Division. Generated by the Patrian National Bureau of Statistics (NBS), they are based on its own in-house surveys, as well as on surveys and other data sources supported by individual ministries and are used to measure the SDG indicators developed by the UN.

The government’s efforts to implement the Millennium Development Goals (MDGs) have revealed significant weaknesses in the data and statistics used to measure their achievement. In a number of cases, it has been found that the statistics produced to measure the indicators were flawed, often because the data used to generate them were also flawed. In some cases, it was possible to identify where and why this was so, but in too many others it was impossible because there was a lack of supporting documentation. The records that should have documented the processes tended to be fragmented or missing altogether. It was embarrassing to the government when it was discovered that certain goals had not been achieved or when it was clear that the government’s statistics could not be trusted. This caused several development partner organisations and private sector investors to question whether to trust the statistics when deciding on the level of donor support to provide.

In order to avoid the same issues emerging in the SDG initiative, the MGA commissioned a situation analysis to assess the quality, completeness and integrity of the data and statistics used to measure SDG indicators. The government recognised that while issues associated with the quality and integrity of data and statistics were at least partially understood, the role of records was poorly defined. This helped shape the scope of the study, the analysis of issues and the development of suggested strategies. The following key issues were identified:

The MGA retained an information management expert to undertake the situation analysis. In addition to improving the quality and integrity of the data and statistics used to measure the SDGs, the government expects that the analysis will also improve the data and statistics that support operational and administrative programmes. The result should be more accurate, complete, authentic, relevant and trustworthy data, statistics and records.

Organisation of the report

This report describes the results of the situation analysis. It defines the methodology for the study and the terms used, and it analyses the quality and integrity of the processes followed in collecting and manipulating data and producing statistics used to measure the SDG indicators, with an emphasis on the quality and integrity of the records that document the processes. The implications for achieving the SDGs are highlighted at both ministry and NBS levels.

The report suggests strategies for addressing the issues that have been identified by establishing a framework of policies, standards, systems and people supported by an effective management structure. The last section describes a series of maturity levels to help the government establish milestones for implementing the strategies, and it recommends immediate first steps.

Methodology

The consultant’s activities focused on:

Definitions

When government officials were interviewed, including those responsible for data, statistics, records management, IT, audit and programme management, it was clear that their understanding of basic concepts such as data, statistics, records and processes differed. For some, the concept of data embraced all recorded information, regardless of physical form, from information recorded on paper or in electronic form, to the highly structured information recorded digitally on computer readable media. For these individuals, records in digital form, including email and reports, contained data that could be manipulated and exploited just as readily as the data recorded in highly structured computer-based data files. Records were just another form of data.

Others made a clear distinction between records and data. According to them, data were highly structured codified information recorded in computer readable form for processing and manipulation by computers. Records, whether in paper or electronic form, were information recorded with the primary purpose of documenting actions and decisions and serving as evidence to meet various accountability requirements. Records, for these individuals, were static, never-changing documents, rather than data that could be manipulated. Some were even more focused in their views, believing that records were the paper files that they use, while data were what are held in databases that IT people use.

Given this range of views, it was decided to use definitions that reflected a balance, respected the scope and objectives of the study and were, as far as possible, based on authoritative sources. The following definitions resulted from considerable discussion among representatives from the various disciplines:

The roles of records include:

Analysis

This section describes issues that present high risks for the quality of data, statistics and records, based on an analysis of:

The government of Patria and the SDGs

Ministries are required to submit final versions of data files and statistics to the NBS as the basis for measuring specific SDG indicators. The Bureau, which serves as a coordinating hub, incorporates the data and statistics in reports that it submits to the MGA. It also collects, verifies, analyses and produces its own data and statistics. In some cases, it merges data from a ministry with data from other ministries as well as its own data to produce statistics covering multiple SDG indicators. It may also undertake additional data processing to ensure that statistics reported to the MGA are presented in a consistent format. In turn, the ministry reviews the reports, confirms their acceptance and produces a summary report that it submits to the UN Statistics Division according to a predefined schedule.

Data collection and analysis at the ministry level

Twelve of 23 government ministries are responsible for collecting data used to measure the SDG indicators. A ministry may be responsible for one or several SDGs, or it may only support one of several indicators associated with a given SDG. The ministries may use one or more of the following methods to collect data and produce statistics.

Survey data

Survey data are collected through longitudinal (repeated observations of the same variables over short or long periods of time) or one-time surveys using questionnaires or interviews. Some are large, such as the census; others are small, such as household surveys. In some cases, ministries have designed entirely new surveys to meet the requirements of a specific SDG.

Surveys are generally designed and administered by research divisions in ministries. Typically, data are collected on survey forms by mail or distributed by contract survey staff to a sample of a target population. In a few cases, data are submitted online. The data then are transferred to coding sheets and input digitally to a raw data file. Several process files may be produced as the data analysis moves through various stages. Where the data contain personal information, anonymised versions may be created.

The resulting statistics are formatted into tables and embedded in various reports for distribution to a wide range of audiences in paper form or, in a few cases, in digital form via a ministry website. In some cases, especially for longitudinal survey results, the data file might be input to a database of data files from previous surveys. A customised report, together with a copy of the master data file produced as a result of the analysis, is forwarded to the NBS, which produces a standardised report containing the statistics and submits it to the MGA to report on progress towards meeting specific SDGs.

Records documenting decisions and actions relating to planning, designing and conducting surveys may be in various forms. These include emails, paper-based correspondence and reports about a given survey; process and master files created as a result of the survey; survey documentation, such as completed coding sheets, survey design reports and descriptions of the methodology; and records describing the business context for planning, designing and carrying out the survey. Together, these records, when well-managed, provide evidence that can substantiate the integrity and trustworthiness of the data and statistics used to measure relevant SDG indicators.

Examples of SDG indicators supported by survey data are:

Registration and administrative data

This type of data results from administrative activities, such as personnel and finance or operational registration activities, such as licensing. Personnel and finance data tend to be generated in relation to ministry-wide standards and procedures or to workflows associated with hiring and retaining staff, processing expenditure and preparing budgets. Work processes established for registration activities, such as licensing, vary depending on the process, and most are well defined. For instance, in the case of a typical licensing process, licensing applications are received by the responsible ministry and reviewed for completeness and suitability; applicants are notified of whether or not they are accepted, the information is processed and included in a database, and licences are issued to applicants.

Records documenting these processes may be in multiple forms. For instance, records documenting a licensing process might include emails, paper-based correspondence and reports about a given application for a licence; completed application forms; copies of notifications; completed data conversion forms or logs documenting entry of the data into a database; review and analysis documentation (to verify qualification for a licence); copies of approval and notification documents; and reports documenting review and renewal actions. Together these records provide evidence that verifies the integrity and trustworthiness of the data and statistics used to measure relevant SDG indicators.

Examples of SDGs supported by registration and/or administrative data are:

Scientific data

These include instrument readings measuring natural or physical phenomena, such as weather (for example, temperature, rainfall), geology (for example soil composition, erosion) and hydrology (for example, water levels, pollutants). Data generated from instruments are stored within the instrument or transmitted to receivers that store the data separately. In the case of weather data, for instance, readings are made on a regular basis from instruments located around the country. These are transmitted to a satellite, which transmits the data to ground stations supported by computers that automatically convert the readings and merge them in a master database holding not only the readings but also the processed data that underpins weather reports. There is very little human intervention.

To take another example, water acidity measurements are taken by staff and volunteers for the natural resources ministry using instruments that take water quality readings, including acidity levels, on an annual basis in selected areas of the country. These are recorded on coding sheets, submitted to the ministry, converted to digital form, analysed, and used to produce a wide range of statistics including average marine acidity statistics used to measure SDG indicator 14.3.1. Some of the statistics also are combined with land use data to measure the impact of agricultural land use on the levels of water pollution.

Again, records documenting these processes may be in multiple forms. From the water quality example, these might include emails, paper-based correspondence and reports about a given water quality activity; completed water quality measurement logs; completed data input forms; data verification logs; extract files (data files created from the master database); and report files describing statistics resulting from analysis of the data. Documentation on planning, designing and operating the water quality measurement process, the database, the programme administering the process and the database all form part of the documentary trail.

Examples of SDGs supported by scientific data are:

Data and records issues at the ministry level⁷

The findings that follow are based on interviews with selected staff and on-site observations in the 12 ministries responsible for measuring the SDGs. Quotes from some of those interviewed for the study are included to illustrate the practical issues involved in measuring the SDG indicators reliably using official data and statistics.

In general, ministry staff tend not to recognise the need to build a documentary trail to support the processes of collecting and processing data and producing statistics. Often, they are unaware of:

In most of the ministries responsible for conducting longitudinal or one-time surveys, data files are not well described; documentation on data structures, coding and formats are fragmented; and data verification and quality control procedures are weak and often non-existent. Little care has been taken to ensure that a documentary trail is in place to provide evidence of how surveys are designed and conducted, how data are collected and processed and how statistics are produced.

Large operational databases in the participating ministries tend to be well-managed, but data extracted from the databases to measure SDG indicators are often poorly documented. Records documenting data extraction tend to be fragmented or non-existent, and procedures for managing the data after they are extracted and used are generally poorly defined. In some cases, the lack of metadata makes it hard to understand the relationship between the extracted data and the source data in the database. Without records documenting changes made to the structure of the extracted data or to the definition of key fields, it is often difficult to know to what extent statistics are inaccurate or misleading.

In a number of ministries, staff managing large databases are being asked to generate statistics to support measuring the SDGs. This is a new task, and many staff do not have the expertise needed to document the processes generating the statistics or to ensure quality control.

Given the need for rigorous standards for collecting and analysing scientific data, the quality of the documentary trail is somewhat better than for administrative and survey data. However, data reliability is undermined by the failure to keep records of changes in the instruments used to make scientific measurements, by changes in sampling methods or by failure to update metadata schema.

Several of the surveys used to measure the SDG indicators lack sufficient documentation about the metadata schema supporting the surveys. There are inadequate definitions of key terms, which has led to confusion when interpreting some of the statistics generated from the surveys.

Problems in finding, retrieving and understanding data held in older data files for trend analysis purposes are hindering the government’s ability to regularly measure SDG indicators.

Metadata describing the context for many data and statistical files tend to be incomplete. This makes the analysis of trends very difficult and it also makes it difficult to respond to access to information requests or court challenges.

Many organisational units across government are involved in developing statistics that support the SDG indicators. Multiple organisations may be involved in developing any given SDG indicator, from the initial planning for a survey or the extraction of data from a database, to the final submission of the statistics to the UN Statistics Division. In many ministries, it is practically impossible to bring together the complete story of measuring an indicator because each unit takes its own approach to capturing and classifying the records documenting its activities.

When one organisational unit passes data to another unit, if the units take different approaches to capturing and managing records documenting the processes they follow, it can be challenging if not impossible to bring together the complete story of how the indicators are measured.

Most ministries have assigned accountability internally for producing statistics to support the SDGs. However, often no one is accountable for classifying records that should document processes for collecting and analysing data and producing statistics or for ensuring that they are complete and accessible through time. Changes in methodology (for instance, in the sample size) and in definitions of key concepts (for instance the target object being measured) tend not to be well-documented. In a few ministries with long involvement in generating statistics there is documentation on survey methodologies (such as coding schemes and analytical techniques) and on conducting surveys (data verification checks, evaluations and audits) somewhere in the ministry, such as in the library. Even in these cases, however, there is seldom a link to the records, such as emails and correspondence, that document the conduct of the survey itself. As a result, the quality and completeness of the documentary trail for individual surveys and for the survey programme varies considerably.

Records management programmes do not exist in most ministries. The one exception is the Ministry of Health, which has a small records management unit with responsibility for managing all of the ministry’s records and ensuring that they are accessible through time. Unfortunately, the unit does not yet support the Health Statistics Division, which is responsible for generating statistics measuring several SDG indicators. The staff are on their own in managing records documenting their surveys.

The government of Patria does not have a digital preservation strategy. Most IT staff believe that digital preservation means storing data securely but don’t recognise the importance of managing the metadata that will make it possible to access and understand the data through time. Nor do they realise that there is a need to convert data to new formats that new software can read or to generate and maintain complete and accurate records documenting these changes. Many look to the NBS for direction and guidance, and some have suggested that it should become a centre of expertise or even a storage centre for data files with long-term value. However, the Bureau lacks the necessary resources and expertise, as does the National Archives.

Senior officials in several ministries suggested that some or all activities involved in producing statistics for measuring the SDG indicators should be outsourced. They feel that if the resources and expertise aren’t available in-house, outside service bureaus and contractors should fill the gap. Others argue that the government needs to control its own production processes, assess the quality of its own data and be able to prove that the statistics it provides to the UN Statistics Division can be trusted. In their view, most companies don’t have the necessary expertise in any case.

Interviews in several ministries participating in the SDG indicators process revealed that sometimes the numbers are changed as the result of political pressure before statistics are provided to the NBS. This doesn’t seem to happen often, but when it does, it usually isn’t recorded. The combination of poor recordkeeping practices and corrupt actions on the part of government officials has undermined significantly the quality and trustworthiness of the statistics used to measure the SDGs.

Data and records issues at the NBS

The NBS maintains a large database that describes the demographic characteristics of the population, including sex, geographic location, education, employment status and income level. Many of the data are collected through the surveys managed by the NBS, with some provided by ministries based on their own surveys. In some cases, the NBS amalgamates data provided by several ministries to generate statistics on cross-cutting topics. In these cases, data submitted by the ministries are converted to formats and structures that can be matched with specific sets of demographic data from the demographic database and matched with other survey data files.

Regardless of their source, data held in the NBS are used to produce statistics that are then incorporated into report files and submitted to the MGA before being transmitted to the UN Statistics Division. The reports are in both digital and hard-copy form. Hard-copy reports are held in filing cabinets managed by the administrative assistant in the office of the director responsible for the demographic database. Digital versions of the report, together with any master data files, are held in the data library ‘forever’ and managed by the head of the IT area. Copies of the data from the ministries are also maintained in the library but disposed of after five years on the assumption that if the files are needed, they can be accessed through the respective ministries.

Statisticians and IT staff in the NBS understand the importance of providing quality data to support the SDGs. However, the lack of resources and of a records management infrastructure make it difficult to document processes for collecting and processing data and producing statistics. This, in turn, makes it hard to ensure that data and statistics are of high enough quality and integrity to be used effectively.

Records documenting the design of the demographic database and the management of the data (data collection, processing, analysis and reporting) are poorly maintained, and there are no documentation standards.

The NBS assumes that ministries are submitting data files and statistics of appropriate quality and integrity.

Sometimes the documentary trail is broken when statistical files are transferred from ministries to the NBS. Each participating ministry uses its own classification standards, which makes it difficult to get a complete picture of any given survey/data collection and analysis activity. The lack of evidence of the quality and integrity of statistics increases the risk that they could be flawed.

Although the professional staff responsible for the demographic database are concerned about preserving the data, they do not feel equipped to tackle this complex issue.

There tend not to be formal retention and disposition schedules.

Implications of the failure to establish a management framework

The implications of these issues for the government’s inability to achieve the SDGs are:

Strategies for sustainable solutions

This section focuses on strategies for developing a comprehensive and sustainable framework for managing data, statistics and records. The issues identified in the previous sections reflect weaknesses in the overall framework for managing data, statistics and records. Just as there are frameworks for managing human and financial resources, this framework should provide an integrated combination of laws and policies, standards and practices, systems and technologies, and people, supported by management and governance structures. A focus on symptoms, without considering the broader causes, will result in fragmented and ineffective strategies and offer only short-term temporary solutions. Inevitably, there will be issues needing urgent and immediate attention, but the focus should be on establishing a comprehensive and sustainable framework for managing the completeness, authenticity and trustworthiness of data, statistics and records.

The section is organised according to the components of the framework. The key issues and relevant strategies are described below for each component.

Laws and policies

Issues

Strategies

It is important that existing laws, such as a national archives act, data protection legislation, statistics act or other relevant legislation, should support the effective management of information needed to measure the SDGs and enable the government to achieve its operational and strategic goals and meet a wide range of accountability requirements, notably:

Standards and practices

Issues

Strategies

Systems and technologies

Issues

Strategies

People

Issues

Strategies

Management and governance

Issues

Strategies

Awareness

Issues

Strategies

The Ministry of Public Administration (which manages the civil service) could be an appropriate agency to take the lead in establishing a framework to address the quality and integrity of the data, statistics and records used to measure the SDGs and, at a more general level, to support the requirements of government programmes for authentic, complete, accurate and relevant data, statistics and records for decision-making and accountability.

Implementing the strategies

Capacity levels to guide the way forward

A roadmap, in the form of capacity levels, will enable the government to move incrementally through defined stages to build the capacity needed to manage data, statistics and records in line with available resources. Five capacity levels are described below, the fifth level being an ideal state for a country that wants to ensure that data, statistics and records used to measure the SDG indicators are of a high enough quality to measure and implement the goals. For most organisations, achieving Level 5 or even Level 4 will be challenging.

The levels reflect diminishing degrees of risk, with Level 1 representing the highest risk of loss and inaccuracy and Level 5 being the least risk. They also reflect increasing levels of sophistication in terms of the way data, statistics and records can be used to support implementation of the SDGs and, more broadly, the government’s operational and strategic goals. The roadmap for moving forward will support an objective and systematic approach. Examples are included in the maturity level descriptions, drawn from the targets and indicators supporting SDG 5:

Level 1: poor-quality data, statistics and records undermine SDG implementation

The organisation produces statistics to measure the SDGs, but they are unreliable. Professionals responsible for data, statistics and records lack the knowledge and skills needed to develop a reliable framework of policies, standards, practices and systems.

Level 2: data, statistics and records enable basic SDG measurement

A framework of laws, policies, standards, procedures and people is in place to ensure that data and statistics are gathered and analysed to measure the SDGs. Managers are generally aware of their responsibility for ensuring that data files and statistics, with their supporting documentation, are stored properly. However, the framework is not applied universally, with some managers providing poorly documented data and statistics. There are no standards for documenting surveys and other data-gathering and analysis activities, nor have policies been developed for managing the records that should document processes for collecting and processing data and producing statistics. Records and data management professionals do not have the expertise needed to manage the interrelationships among data, statistics and records, especially those that need to be preserved through time.

Level 3: the quality of data, statistics and records makes it possible to measure SDGs effectively and supports government programme activities

Data, statistics and records generated to measure SDGs are managed through a comprehensive framework of policies, standards and practices, systems and technologies, and qualified people. Records management staff work effectively with data management and other professional staff to ensure that requirements for identifying, describing, classifying, protecting and retaining data, statistics and records are integrated in the design of processes for collecting data and producing and using statistics. Managers know that they are responsible for ensuring that the data, statistics and records generated are authentic, reliable, accessible and understandable and can be retrieved when needed. Professional staff apply clear, consistent standards and practices. However, preservation is not addressed adequately; retention requirements have not been established, metadata standards for data, statistics and records have not been developed, and preservation standards, procedures and technologies are not in place.

Level 4: well-managed data, statistics and records make it possible to measure SDG implementation effectively and consistently through time; data and statistics are of high enough quality and integrity to support government programme activities at the strategic level

Data, statistics and records generated to measure the SDG indicators can be reliably merged or combined with other data sources to support programme activities, including those supporting the organisation’s strategic goals. Organisation-wide policies and standards are in place to protect records of decisions, and accountability requirements, for instance under access to information legislation, are supported by consistently applied records management policies and standards. Trends can be analysed through time, and comparisons can be made from year to year because changes to formats, coding schemes and data collection and analysis methods are well-documented. Preservation standards ensure that data, statistics and records are stored properly and migrated to take account of changes in technology. The preservation programme ensures continued accessibility and authenticity of data, statistics and records through time.

Level 5: processes generating data, statistics and records, and the framework for managing them, are designed to make it possible to exploit data, statistics and records, including those measuring SDGs, in new and innovative ways

Managers of SDG initiatives understand the benefits of sharing and exploiting data, statistics and records for stimulating innovative thinking on implementing the SDGs and achieving the operational goals of individual programme activities and the strategic goals of the organisation. Professional staff have the knowledge and expertise needed to design comprehensive management frameworks covering multiple organisations and technology environments that encourage information in the data, statistics and records to be exploited to the greatest possible extent.

First steps

Rather than trying to work on everything at once, it is suggested that the government should start by identifying and defining solutions for a few processes where weak management of data, statistics and records has significant implications for achieving the SDGs. This experience will then inform the development of the framework.

Identify a leader and assemble a team

Given the MGA’s leading role in the SDG initiative, a senior official in the ministry should oversee the initiative. This person should have a background in data management, statistics, information technology or records management, the capacity to bridge these disciplines and the ability to communicate with a variety of stakeholders, including senior management.

A steering committee should be appointed, made up of representatives from government programmes supporting the SDGs as well as programmes where the quality and integrity of data, statistics and records is particularly important. Specialists in managing data, statistics, records and information technology, as well as legal experts and auditors, should also be included. The committee should help select the SDG processes to be covered, to identify issues and strategies, and to explore how to extend the results to other SDGs.

Some government officials have argued that attempting to build a comprehensive framework is creating a ‘mountain out of a mole hill’ and that the focus should be on addressing immediate issues associated with specific SDG initiatives. Others have realised that systemic issues need to be addressed across government as a whole. This tension between the need to address immediate and critical problems and the goal of developing comprehensive and sustainable solutions needs careful management. One way to address the tension is to focus on specific carefully identified processes in order to gain knowledge and skills that can be extended to other processes or used in developing a comprehensive management framework.

Identify processes as examples

For each of the three process types (survey, registration/administrative and scientific), identify one or two processes that present significant challenges for measuring one or more SDG indicators and for using data, statistics and records for operational and programme delivery. These are likely to be processes where undocumented flaws or inaccuracies in data, statistics and/or records have led to embarrassment, bad decisions about the use of government resources, missed opportunities or increased risk and costs.

Describe the selected processes

The description should cover the stages of generating the data, statistics and records and of managing a given process:

It should be possible to identify the data, statistics and records created at each stage. The aim is not to describe every single stage and every piece of data and statistics and every record for a given process but to identify key stages of the process and the associated data, statistics and records that are significant in terms of measuring an SDG and providing a complete and authentic documentary trail of the process.

Finally, the overall framework for managing both the process and the data, statistics and records should be reviewed. Policies and standards are particularly important, as is the governance structure (who is accountable to whom for what). This will provide a template for analysing the quality and integrity of the process itself and the data, statistics and records it generates.

Identify issues and implications

It should then be possible to analyse the issues, distinguish between symptoms and causes and identify solutions. For instance, a poorly documented data file input to a set of statistics that turned out to be flawed is a symptom. The cause was the failure to establish metadata and documentation standards at the planning and design stages of the process and to assign accountability for implementing them as part of the management framework for the process. In identifying the issues, it is important to distinguish between immediate issues particular to measuring a given SDG indicator and issues related to the broader management framework for the organisation as a whole.

Finally, issues should be explained in a way that programme managers responsible for generating SDG statistics can understand. A key idea that should be reinforced continually is that where data, statistics and records are flawed, where their accuracy cannot be established and where they are lost or destroyed, the credibility of the manager responsible for the data, statistics and records will be undermined irrevocably. By extension, society’s trust that the government is capable of carrying out its obligations, including achieving the SDGs, will be eroded significantly.

Develop strategies for resolving issues

Understanding where symptoms and causes are located on the roadmap and how they relate to one another will be helpful in developing integrated strategies within the context of the overall management framework. Most of the strategies should focus on the planning and design stages of the survey, system or other data collection and analysis activity when the steps in the strategy can be integrated more easily and more cost-effectively. For instance, the need to develop and apply enhanced metadata standards and procedures for enabling data, statistics and records to be related to one another should be acknowledged and addressed at the planning stage with subsequent stages incorporating the testing, implementation and assessment of the standards and procedures themselves. This approach to developing and implementing strategies can be applied to any process, from small one-time surveys to large IT systems supporting continuously updated databases from which data and statistics supporting SDGs are extracted.

Apply the experience to other processes and to the framework for managing data/statistics/records

The approach should result in strategies that can be applied to all processes, not just for those measuring the SDGs but for any process supporting the government’s programmes and services where data, statistics and records essential to decision-making and the ability to meet accountability requirements are being placed at risk. In parallel and over the longer term, the results will be invaluable in developing a comprehensive, policy-driven standards-based framework for managing data, statistics and records, regardless of the process or business function.

Ultimately the goal is to build a comprehensive management framework to cover all government programmes and services and to allow the government to demonstrate that the data, statistics and records it generates can be trusted. The outcome should be that the government is able to demonstrate, through the availability of complete, accurate and relevant data, statistics and records, a high level of credibility, both to Patrian people and to international partners, investors, development agencies and other international organisations, including the United Nations.