33 iii. Equip with adequate knowledge of gender-sensitive SDG indicators and highlighting those relevant to their domain; iv. Review data collection forms of administrative data to include gender issues relevant to sector; v. Build strong coordination and cooperation with NBS/OCGS to ensure harmonization of methodologies and data production and management processes, and timely production of indicators; vi. Adopt better data management processes; vii. Create awareness among users regarding the type of gender-sensitive data that existing administrative data systems regularly produced; viii. In collaboration with NBS/OCGS conduct periodic data quality assessments of existing administrative data systems; ix. Work with the SDG monitoring agency in the country to communicate monitoring, reporting and evaluation mechanisms related to the SDG framework to improve compliance and sustainability in gender data production and dissemination; x. Publish gender-sensitive administrative data reports in collaboration with NBS/OCGS; and xi. Establish systems to increase the accessibility of gender-sensitive administrative data and related products to users through regular statistical releases, publications and data sharing.
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CHAPTER FOUR: QUALITY MANAGEMENT IN THE PRODUCTION OF
ADMINISTRATIVE DATA
4.0 Introduction
Quality management is defined in the GSBPM as the process that includes quality
assessment and control mechanisms. It recognizes the importance of evaluation and
feedback throughout the statistical business process. It ensures that an
organization's products or services are consistent, meet users’ expectations, and
comply with the standards.
In assuring transparency statistical agencies’ policies, management practices, terms
and conditions under which statistics are developed, produced and disseminated
should be documented and available to users, respondents, owners of data source
and the public.
4.1 Guiding Principles
i.
Identify quality indicators in order to measure compliance with the respective
quality principles and requirements as defined in the Tanzania Data Quality
Assurance Framework.
ii.
Set levels of requirements for the quality indicators in the form of quality
targets which can serve as a tool for monitoring quality developments over
time.
4.2 Importance of Quality in Administrative data
High-quality administrative data is essential for several reasons:
i.
It forms the basis for informed realistic decision-making.
ii.
It facilitates accurate monitoring and evaluation of programs and activities.
iii.
It facilitates compliance with regulatory requirements.
iv.
It supports transparency and accountability.
4.3 Strategies for improving Quality of Administrative Data collection
Improving the quality of administrative data collection involves implementing
strategies aimed at enhancing accuracy, completeness, timeliness, consistency,
relevance, and accessibility of the data. Here are several strategies to achieve this:
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4.3.1 Clear Documentation of the Processes
Documenting data collection processes helps ensure consistency and transparency.
Some key elements to include in the documentation are purpose and objectives,
data collection methodology, data variables and definitions, roles and responsibilities
and training materials. Organizations should develop clear guidelines and
procedures for data collection, including definitions of variables, data entry protocols,
and quality control measures.
4.3.2 Building Data Collection instrument
The collection instrument is generated or built based on the design specifications
created during the design stage. A collection may use one or more means to receive
the data. Collection instruments may also be data extraction routines used to gather
data from existing statistical or administrative data sets. Administrative data
collection instruments are specifically designed to gather information relevant to
administrative purposes within organizations or institutions, this includes forms,
applications, checklists and templates.
Building data collection instruments involves designing tools or forms that facilitate
the collection of relevant information. The choice of instrument depends on factors
such as the nature of the data, the target population, and the intended objectives.
This also includes preparing and testing the contents and functioning of that
instrument (e.g. testing the questions in a questionnaire). It is recommended to
consider the direct connection of collection instruments to the statistical metadata
system, so that metadata can be more easily captured in the collection stage.
Connection of metadata and data at the point of capture can save work in later
stage.
4.3.3 Data Collection Standardization
Data collection standardization refers to the process of establishing and
implementing uniform procedures, methods, and protocols for collecting data in a
consistent and systematic manner. It involves defining and adhering to a set of
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standards and guidelines to ensure that data is collected accurately, reliably, and
efficiently.
Data collection standardization is essential for ensuring the integrity, comparability,
and usability of collected data for analysis, interpretation, and decision making
purposes. It helps minimize errors, enhance data quality, and increase confidence in
the findings derived from the collected data.
Data collection standardization involves the following:
i.
Scope: This refers to standardizing the specific techniques or approaches
used to collect data.
ii.
Focus: It emphasizes consistency in the way data is gathered, measured, or
recorded.
iii.
Goal: The primary goal is to ensure uniformity and comparability in the data
collected, making it easier to analyze and interpret results across different
contexts or time periods.
An organization should establish uniform data collection tools, such as forms and
templates, and provide training to staff on their use.
4.3.4 Training and capacity building
Investing in staff training and capacity building is crucial for improving data collection
quality. Training programs should cover topics such as data collection techniques,
data quality standards, and the use of data collection tools.
The following steps can help organizations develop their workforce's proficiency in
this area:
i.
Conduct a needs assessment to identify the specific training needs and
capacity gaps of personnel involved in data collection. This assessment
should consider factors such as existing skills, knowledge levels, and areas
requiring improvement.
ii.
Engage subject matter experts, experienced data collectors, or external
trainers with relevant expertise to facilitate training sessions, share best
practices, and provide real-world examples.
iii.
Provide ongoing support and mentoring to personnel throughout the training
process and beyond. Encourage open communication, feedback, and
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collaboration to address questions, troubleshoot issues, and reinforce learning
objectives.
iv.
Develop comprehensive training materials, manuals, guides, and resources to
supplement training sessions and serve as reference materials for personnel.
These materials should be accessible, up-to-date, and easily understandable.
v.
Evaluate the effectiveness of training programs through pre-and post-training
assessments, surveys, and feedback mechanisms.
4.3.5 Utilization of technology
Technology can enhance data collection efficiency and accuracy. Organizations
should explore the use of electronic data capture systems, data validation tools, and
other technological solutions to streamline data collection processes; select
appropriate tools and technologies for data collection based on the nature of the
information being collected; and ensure that data collection tools are reliable, user-
friendly, and capable of capturing the required data accurately.
4.4 Metadata, Concepts and Definitions
Statistical metadata is commonly defined as data that describes information about
other data. Metadata ensures the quality, interpretability and usefulness of datasets.
The first and most fundamental purpose of metadata is to help users of statistical
data to interpret, understand, and analyse statistical data.
4.4.1 Guiding Principles
i.
Create a codebook that provides question level metadata matched to
variables in the dataset at data processing stage of administrative data
production;
ii.
Provide sufficient metadata to ensure quality and add value to administrative
data. Provide information covering the underlying concepts and definitions of
the data collected and statistics produced, the variables and classifications
used, the methodology of data collection and processing, and indicators of the
quality of the statistical information in general, sufficient information to enable
the user to understand all of the attributes of the statistics, including their
limitations;
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iii.
Make available, comprehensive and clear metadata; and documentation
about the administrative source in order to understand and interpret the data.
Without this, it is not possible to understand and assess the administrative
source against the intended use;
iv.
Make available the system that stores all the necessary information collected
during data collection; and
v.
The metadata management system of the data producers should be well
defined, documented, archived and disseminated according to internationally
accepted standards. The data producers should ensure that the following
elements are assured for managing metadata.
4.5 Access and Use of Administrative Data and Statistics
Statistics Act provide access to public and other records where the Statistician
General/ the Chief Government Statistician is of the opinion that, the collection of
statistics relating to any matter may be obtained from any Government institution,
agency, or user or producer of statistics, he/she shall grant access to any authorized
officer or staff of NSOs for the purposes of getting the information required.
A legal framework for administrative data addressing confidentiality issues is crucial
for ensuring the protection of sensitive information collected by government agencies
and other entities. Confidentiality concerns often arise when dealing with personal or
sensitive data, such as health records, financial information, or other Personally
Identifiable Information (PII).
The general policies on data sharing within government bodies, which will influence
the right of access to administrative data for statistical purposes can be developed.
These policies are developed to ensure that data producers and users follow the
accepted standards for the long-term preservation and dissemination of data to the
wider public. In particular, standards should be supported that aim to facilitate data
harmonization and exchange across different stages of the administrative data
production and across institutions including common data structure definitions and
code lists, and the integration of data flows and processes within NSS.
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4.5.1 Guiding Principles
i.
Make dissemination and data preservation plan early in the production of
administrative data that includes archiving, publishing and distribution. Verify
and ensure that the released data after all the processing steps are consistent
with the source data. In the case of the derived variables, it means that one
should be able to reproduce the same results from the source data.
ii.
Preserve sustainable copies of all key data and documentation files produced
during the data collection process, as well as those files made available for
secondary analyses. Consider;
• To define the long-term preservation standards and protocols used.
• To maintain older versions of important data and documentation files so
that users can follow the changes made from one version to the next.
• Archiving collections in one archive which would keep master copies of
files in several locations but minimize the possibility of conflicting
versions of data and documentation files.
iii.
Conduct a disclosure analysis to protect respondent confidentiality. The key
goal of disclosure risk analysis is to ensure that the data maintain the greatest
potential usefulness while simultaneously, offering the strongest possible
protection to the confidentiality of the individual respondents.
iv.
Think about the production of both public and restricted use of data files.
Considering the following:
• Make data files fully available to the users by establishing clear rules
under which users can obtain the data.
• Establish clear policies for how users may access the restricted data
files by creating a set of application materials and restricted-use data
agreement that specify how users can obtain and use such data.
• In order to provide optimal utility for the users, produce a variety of
products for varied constituencies;
• Produce set-up files and ready to use portable files if applicable in
software packages to address the needs of those who seek to do
intensive statistical analyses.
• Consider disseminating data on websites.
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4.6 Agreement and sharing of administrative data
The assessment carried out by NSOs in 2024 showed that many institutions use the
policy as a guiding document for sharing data and not MoU. Given the legal and
policy frameworks required to permit the use of administrative data, written
agreements (MoU) are often necessary to detail and facilitate the transfer of
knowledge and data (UN, 2011). MoU will be applicable only if the law or policy of
respective institutions do not offer a window for sharing of its data.
Data sharing among agencies, refers to those methods whereby agencies can obtain
access to one another’s data on a timely basis. The collection process is duplicate if
different agencies collect similar data on the same source. Therefore, when the
institutions gather data together and share such information, it will help to reduce this
duplication and hence unnecessary costs. Although data sharing has many benefits,
it raises issues regarding to privacy and confidentiality; who should have access to
these data; how confidentiality and privacy rights can be protected while achieving
the benefits of linking program data; etc. All of these issues should be addressed in
the design of the MoU.
Beyond the act of sharing itself, data sharing entails a commitment to maintaining
the integrity and reliability of the shared data throughout its lifecycle. This means not
only making data accessible to all stakeholders but also ensuring that it retains its
quality, coherence, and usefulness for the processing and analysis by data users. A
crucial part of this process involves data producers carefully documenting and
labelling sets of data, including providing detailed descriptions and clear definitions
so that others can easily find, discover, and understand the shared data.
In addition, data sharing implies making data accessible to the relevant individuals,
domains, or organizations using robust access controls and permissions. This
ensures that only authorized personnel can access specific data sets, thus adhering
to regulatory compliance demands and mitigating risks associated with breaches and
data misuse.
4.6.1 Internal vs. External data sharing
In the landscape of modern business operations, NSS must distinguish between
internal and external data sharing, with their different approaches for organizations to
disseminate information. Internal data sharing is all about the exchange of