E.  Country experience: Italy

 18.39.        Bank of Italy  adopts an integrated approach to the broad range of its statistical programmes. The approach of the Bank to the management of metadata is based on two pillars: (a) an information model, capable of fully describing the data, the processing steps and the elaboration algorithms and (b) a software platform, which supports the entire statistical production chain. 

18.40.        Bank of Italy has designed a proprietary model called “Matrix,”[1]  based on mathematical and statistical theory, to support all phases of the statistics production process (data definition, collection, compilation and dissemination) and all the data of interest (micro/aggregated, registers, questionnaires, etc.). A fundamental infrastructural component of the Bank’s system, representing a core part of the actual implementation of the Matrix model, is the central statistical dictionary, a repository describing the entire content of the statistical data warehouse, in terms of structural metadata (e.g., concepts, classifications, data structures and processing rules) and reference metadata (e.g., methodological notes).

18.41.        The Matrix model was also designed to take into account major international standards, so that, for example, the Matrix data and metadata can be easily transformed into SDMX and other metadata formats. Another essential feature of the Matrix model is that it enables a metadata-driven system by employing a recently introduced software platform for statistical processing called Infostat.[2] Consistent with the underlying holistic approach, Infostat supports the statistical production chain end-to-end, by providing the following services: 

(a)  Identity and access management (e.g., user registration, authentication, user profiling);

(b) Metadata definition;

(c) Online data entry and data upload;

(d) Support for secure data transmission, storing and versioning;

(e) Validation and handling of reporters’ feedback;

(f) Calculations;

(g) Data and metadata import, export and exchange;

(h)  Event subscription and notification;

(i) User environment for metadata prototyping and data production;

(j) Reporting and publishing;

(k) Information search;

(l) Inquiry and download of data and metadata;

(m) End-to-end monitoring of business processes.

18.42.        An important advantage of the approach detailed above is that most of the changes in the statistical processes (e.g., the establishment of a new survey, the production of new sets of statistics, the release of a new publication, etc.) can be implemented in a timely manner by metadata administrators, avoiding the need for complex software maintenance. In fact, due to an advanced user interface, metadata administration is rather user-friendly, allowing users who are not information technology specialists to accomplish it directly, without the intervention of technical staff.

18.43.        The system can handle both qualitative and quantitative indicators, micro- and macrodata, questionnaires, registers and unstructured data (documents), thus allowing broad integration. Infostat also adopts the Matrix model language, called EXL (expression language), to define expressions used in data validation and in data processing phases for calculations. EXL expressions are intuitive, as they are quite similar to spreadsheet formulas, and the language is conceived to be extensible, in order to support the great variety and variability of statistical requirements.


Back to Chapter 18 Metadata

[1] Vincenzo Del Vecchio, Fabio Di Giovanni and Stefano Pambianco, “The ‘Matrix’ Model:unified model for statistical data representation and processing”  (Banca d’Italia, 2007). Available from https://www.bancaditalia.it/statistiche/raccolta-dati/sistema-informativo-statistico/modellazione/matrixmod.pdf.

[2] Fabio Di Giovanni, Daniele Piazza, “Processing and managing statistical data: a National Central Bank experience”, paper presented at the “International Statistical Conference”, Prague, 14-15 September 2009. Available from  http://www.academia.edu/6455962/Processing_and_managing_statistical_data_a_National_Central_Bank_experience.