Statistics are essential for development of a nation. With arise of technologies such as AI and big data eficient data governance become more and more important to overcome challenges and opportunities evolved by them. Unfortunately, most of databases in our public and private companies and organizations lack interoperability. This work proposes a statistical data governance mechanism based on the Statistical Data and Metadata eXchange (SDMX) standard, designed specifically for statistical data sharing and exchange between organizations.We designed and implemented a statistical database based on SDMX. This system allows more than 10 benin public organizations to be able to produce, publish and share statistical data from various theme. They can express the indicators and levels of disaggregation of these indicators in a flexible way, without having to create a new database.
Today, digitization and increasing information exchange,
statistics play an essential role in the development of
nations [
The rapid advent of information technology has led to a massive explosion of data, creating unprecedented opportunities, but also posing complex governance challenges.
Data governance is defined as “an overall framework within
the company for assigning rights and duties to decisions in
order to manage data appropriately by as a corporate asset”
[
Good data governance facilitates exchange and
compatibility between diferent systems and organizations. It thus
promotes greater interoperability.
• Transnational initiatives such as :
– World Bank [
African continent [
– Benin open data portal [
SDMX is an international initiative aimed at standardizing
and modernizing statistical data and metadata exchange
mechanisms. This standard encompasses a data model
(the multidimensional data cube), standard vocabularies
(content-oriented guidelines), a formal schema definition,
and various data serialization formats for building data files
and electronic messages for data exchange. In the SDMX
ecosystem, data providers can choose between diferent data
serialization formats for sharing datasets, including XML,
CSV, JSON, or even EDIFACT [
These standards are implemented through new
technologies such as application programming interfaces (APIs).
APIs facilitate interaction between two diferent
applications so that they can communicate with each other. They
act as intermediaries. APIs use the Hypertext Transfer
Protocol (HTTP) for cooperation between diferent programs and
web services (REST or SOAP) [
APIs are therefore catalysts for interoperability. However, as their use increases, data security becomes a major concern.
Protecting sensitive data is an important part of data governance. It involves implementing measures and protocols to prevent unauthorized access, leakage or manipulation of confidential information.
However, despite eforts to ensure data security, information leaks are still a reality. Due to the rise in API-related vulnerabilities, the Open Web Application Security Project (OWASP), a foundation dedicated to improving software security, has been issuing its list of the top 10 web security vulnerabilities every 2-3 years since 2003. The OWASP foundation’s separate classification of the top 10 vulnerabilities for web applications and APIs highlights the divergence between modern APIs and traditional web applications, requiring a tailored security approach.
The OWASP foundation provides a list of the top 10
OWASP API vulnerabilities in 2023 [
Flows • API-7 : Server Side Request Forgery • API-8 : Security Misconfiguration • API-9 : Improper Inventory Management • API-10 : Unsafe Consumption of APIs
To prevent such situations, developers need to focus on
writing secure code and ensuring that APIs are configured
securely. To guarantee API security, it is essential to
consider three fundamental pillars of security : confidentiality,
integrity, and availability [
The aim of this initiative is to ensure interoperability between databases and facilitate the distribution, availability, use and reuse of information. The same applies to data security.
A set of technological tools including a development environment, programming languages and software tools was used.
There are several stages to the process.
Setting up a statistical database system involves a series of steps.
The diagram below summarizes the process :
• Identifying and validating indicators :
indicators are quantitative or qualitative measures
used to assess the performance or state of a
specific domain. They can include statistics such as
the unemployment rate, the economic growth rate,
the number of new businesses created [
For example, modeling an observation on the unemployment rate for women in rural areas for the year 2018 in a database can be done through the following parameters : • Indicator : unemployment rate for women in rural areas • Disaggregation levels : – Commune : BAN (Banikoara) – Department : ALI (Alibori) • Observed value : 2.6% (percentage) • Period : 2018 • Producer : the organization or entity responsible for collecting and publishing these statistical data.
However, when it comes to integrating data with any type of indicator and several levels of variable disaggregation, the task quickly becomes arduous because it sometimes requires rebuilding the database. Hence, the need for a standard that takes these complexities into account.
SDMX is an essential standard for simplifying statistical data modeling and supporting diferent types of database, whatever their level of complexity. It uses a multidimensional approach based on the data cube model. The data cube model, also known as the Online Analytical Processing (OLAP) model, is a data modeling method designed to make data analysis and visualization more accessible by presenting it in multidimensional form. Data is organized in cubes, with each dimension representing a distinct aspect of the data.
A multidimensional data cube can be thought of as a
model focused on the simultaneous measurement,
identiifcation and description of multiple instances of an entity
type. A multidimensional dataset consists of several
measurement records (observed values) organized along a group
of dimensions (e.g., “period,” “location,” “gender,” and “age
group”) [
The multidimensional data cube model can support data
interoperability across many diferent systems,
independent of their technology platform and internal architecture.
What’s more, the content of a multidimensional data cube
model need not be limited to small datasets. In fact, “with
the advent of cloud and big data technologies, data cube
infrastructures have become efective instruments for
managing earth observation resources and services” [
Taking again, the example on the unemployment rate for women in rural areas for the year 2018 its data cube representation can be identified by the following dimensions (figure 2): • Period = 2018 • Sex = female • Geographical distribution = rural area • Observed value = 2.6
In this way, SDMX enables data to be clearly represented by associating measures with dimensions and attributes. SDMX data structures, known as Data Structure Definitions (DSD), describe how data is organized, identifying key dimensions, measures and associated attributes. It also provides standardized terminology for naming commonly used dimensions and attributes, as well as code lists for populating some of these dimensions and attributes. More specifically, a DSD in SDMX describes the structure of a dataset by assigning descriptor concepts to statistical data elements, which include : • dimensions that form the unique identifier (key) of individual observations ; • measurement(s) conventionally associated with the concept of “observation value” (OBS_VALUE); and • attributes that provide more information about a part of the dataset.
In addition, SDMX ofers a set of globally agreed DSDs
for diferent application domains, ensuring consistency and
interoperability between statistical organizations. [
This eliminates the need for a multitude of statistical databases. A single database is enough to federate all an organization’s data. The various players can then define the indicators and levels of disaggregation which will be encoded in the database, ready to receive any type of statistical data.
Taking the example of the indicator “Total number of
support requests for multiple births met” [
TOTALH • TRANCHE_D_AGE with the following code list : 0-17
ANS, 18-34-ANS, 35-59-ANS, 60-ANS-PLUS • TYPE_HANDICAP with the following code list : HMI,
HMS, HA, HV, HM, AFH
• DEP with the following code list : ALI, ATA, etc
• COM with the following code list : BAN, NATI, etc
The data representation presented in [
This has the advantage of a more simplified representation and saves storage space eliminating redundancy. The ability to create data structures to define data representation ofers great flexibility to data producers, who could define context-dependent data structures for the same indicator.
SDMX provides the tools and standards needed to structure open data in a way that maximizes its usefulness and impact.
Its use has enabled us to meet a number of challenges : • eliminate the multiplicity of databases used to collect and process statistical indicators ; • put an end to the disparity and scattering of monitoring-evaluation data ; • an integrated database for storing indicators ; • efectively operationalize the statistics development strategy ; • establish a coherent and scalable governance system for statistical data ; • standardized data ; • SDMX interoperable APIs, which focus on retrieving metadata and data in XML-JSON-CSV formats. They can used as intermediaries between SDMXstandardized systems or platforms. These platforms include : the .Stat Suite ; • an environment for producing and disseminating statistical data.
This standardized data is available on a dedicated platform.
Data governance, data interoperability and data security are interdependent and essential elements in maximizing the value and minimizing the risks associated with the growing use of data in our society. Implementing the SDMX standard has enabled us to standardize data and obtain interoperable SDMX APIs enabling statistical data to be exchanged between diferent systems or platforms. The result is an information system based on this standard. There is no longer any need for a multitude of statistical databases ; a single one is suficient to federate all an organization’s data. The various players can then define the indicators and levels of disaggregation which will be encoded in the database, ready to receive any type of statistical data.