This document discusses the modernization of statistical business process models, particularly through the implementation of the sebstat statistical information model by the National Bank of Georgia, aimed at enhancing data collection, management, and dissemination in public institutions. It emphasizes operational efficiency, automation, and effective data management to streamline statistical production while incorporating new methodologies and architectures. The framework supports improved data analysis capabilities and harmonizes statistical processes across various economic and financial sectors.

1. Modernized statistical business process models
- Enhanced Statistical Metadata and Data Collection and Exchange
in Public Institutions.
- The SebStat statistical information model from the National Bank
of Georgia
Published by FIS MarketMap FAME Analytic
Platform
August 2018

2. MODERNIZED STATISTICAL BUSINESS PROCESS MODELS
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3. Table of contents
Statistical Metadata & Data Collection and Exchange......................................................................................... 4
Improved data collection and analysis through modern taxonomies .................................................................. 4
Identifying, relating and disseminating monetary data objects............................................................................ 5
Modern information models for statistical data warehouses ............................................................................... 6
Automated Data Collection via SebStat .............................................................................................................. 8
Benefits of SebStat Information Model within Statistical Data Production .......................................................... 9
Automation & efficiency through well-structured raw data .............................................................................. 11
Conclusion ............................................................................................................................................................. 14

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Statistical Metadata & Data Collection and Exchange
Improved data collection and analysis through modern taxonomies
FIS has recognized an industry wide race in today’s central banks, monetary authorities and inter-governmental
institutions including the IMF, OECD and the BIS. These institutions have been given increased supervisory
responsibilities to spot systemic risk to the overall economy from defaults or under capitalization in key sectors.
The response to the global financial crisis was swift and immediate in the industry with a “never again” attitude
influencing legislatures, central banks and authorities alike.
While reduction in costs is a promise of modernization, this is also a race for operational efficiency and readiness.
The focus is on streamlining and automating processes allowing for more operational flexibility and agility. FIS
has been modernizing the components of its MarketMap Analytic Platform (MAP) and the core FAME analytic
database management system to help its clients win this race.
A key focus for public-sector statistics department has been the incorporation of new information models and
methodologies, focused on statistical production. For example, the SebStat web-enabled Statistical Information
System from the National Bank of Georgia (NBG) aims to make the production of central bank statistics more
flexible, cost-effective, transparent and user-friendly. The MAP solution accommodates and facilitates the
implementation of this new breed of statistical business process model which drives the collection, validation,
processing and dissemination of statistics under the NBG mandate.
Underlying new statistical models of this type lies a conceptual and IT architecture based on a well-organized data
structure definition (DSD) which renders data understandable from a methodological point of view for all
consumers. Using the SebStat model, both consumers and contributors can generate and submit data easily,
through unique keys, describing detailed data characteristics.
The goals of SebStat are relevant to many of the infrastructure modernization efforts being initiated by central
banks and national statistical institutes. Consider the European Central Bank’s traditional production environment
and various collection, compilation and dissemination modules. The production system is FAME – a NoSQL
Analytic database system optimized for statistical time series and financial metrics. The Statistical Data &
Metadata Exchange data model (SDMX) drives the collection and dissemination of data managed in FAME
including web-based publications.

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SebStat is the brainchild of Ms Nana Aslamazishvili, Project
Manager at National Bank of Georgia (former head of
Monetary Statistics Division) and was conceived as a tool to
modernize the collection and management of statistical data
within Georgia and to make it more accessible and
understandable for users. Ms Aslamazishvili is keen to
expand the remit of SebStat into new statistical domains and
products. As institutions like the ECB look to sponsor new
supervisory frameworks such as AnaCredit and as banks
within the European Union take on new supervisory
responsibilities, they can enhance their systems by adding
the SebStat information model and framework to their
infrastructure. This framework can help modernize and
impact the collection, processing and dissemination of
monetary and financial statistics.
The SebStat framework approximates the data Structure Definition (DSD) principles used by SDMX and its
Concept Oriented Guidelines code lists are used for dimensions such as Frequency and Sector. Specific code
lists are added for key monetary statistics dimensions such as Additional Information on Loans, Collateralizing of
Loans, Interest Rate, etc. A key advantage of SebStat is that its conceptual framework allows for collection and
aggregation of data from different financial institutions without any additional effort. Simply expand “Source” code
lists by appropriate elements.
Identifying, relating and disseminating monetary data objects
The core component of MAP – FAME – is a columnar b-tree data vector store which is a format used in big data
technologies and differs from a relational database system. It is optimized for the storage, harmonization and
dissemination of time series. Our clients store statistical observations on the nodes of the vector and the vector
object itself holds the entire history. Bank of England, for example, stores pricing data back to 1880 in a FAME
object. With a single FAME database able to store 1 Terabyte of data, thousands of statistical data objects can
be stored, analyzed and harmonized the Platform.
Attributes describing the statistical series are attached to the object itself with all the necessary data attributes
called for by standards like SDMX able to persist with the data. Clients also value FAME for its ability to perform
time series-oriented transformations “in database” via a coupled analytic engine run next to the data storage.

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FAME: Columnar storage with attached
metadata
FAME: Statistical data stored and analyzed as
objects
Rather than using a structure imposed at the database level, objects in FAME databases are related to each other
by means of a naming schema. A consistent naming schema enables users to search and find the data they
need. Two decades ago, long standing FAME customers including Eurostat, Bank of Spain, European Central
Bank, BIS and the IMF began working on an improved scheme for naming time series and managing attributes.
Their work eventually resulted in a robust statistical data and metadata information model and exchange
standards known as SDMX (Statistical Data & Metadata Exchange). Use of SDMX facilitates proper metadata
management of the warehouse and easier navigation. Incorporating complementary frameworks like SebStat into
MAP ensures the efficient collecting, naming, correlating and dissemination of monetary statistics across the
institution.
A core feature of SebStat is an identification code of specific variable- the key. Each key is unique, ensuring no
collision when requesting specific objects from a FAME database container. The key consists of determinants
and attributes, which describe qualitative and quantitative features of the variable. These determinants and
attributes form specific digits in the key. The monetary statistic observations are persisted in the nodes of a FAME
vector object and unique keys as attributes to describe the data.
Modern information models for statistical data warehouses
Frameworks like SebStat provide methods to encode data submitted by external reporting institutions and re-form
this into a new dataset using dimensions and attributes for identifying and describing the data objects in the
warehouse. For example, the schema illustrates how data model was created initially, using ordinary excel-based
questionnaire. It describes a clear way to define data structure for any statistical domain and any desired indicator

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with an unlimited
number of
characteristics. In
this way it is
possible to
establish set of
keys identifying
different types of
data. SebStat
identifies peer data
to create specific
data families. Data
inquiry for each
data family
represents a list of
keys, instead of
questionnaires.
SebStat is then
able to collect,
validate, produce,
disseminate and store financial and statistical data through a unified mode.
The DSD describing and codifying loan data is in line and consistent with other DSDs, classifications and object
naming designations for balance of payment data and national accounts data in the warehouse defined by the
complementary SDMX information model.
As raw source data is more structured, the ability to automate the collection of data and validate the information
improves dramatically. There is also now a framework for exchanging data from the warehouse with other key
institutions such as the IMF, OECD and other regional central banks and statistical agencies.

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Automated Data Collection via SebStat
SebStat represents an information model capable of changing the conceptual framework of traditional statistical
production practices. Rather than obtain statistical data on paper or via Excel based questionnaires, each
requested variable is defined through a unique key, identifying its quantitative and qualitative characteristics.
Steps for external institutions to submit data to the central authority now include:
(a) Login by entering the ID and password;
(b) Upload the data;
(c) Analyze errors, if system identifies it;
(d) Send the data to System;
(e) Receive conformation of the acceptance of the data (after appropriate validation procedures).
This approach facilitates deep data storage of monetary statistics over time while avoiding duplication information.
It is also suitable for automation of data collection, validation, processing and dissemination. Web based workflow
tools such as the FAME Enterprise Data Manager (FEDM) can be used to monitor each of these key workflows.
Systemization and standardization of monetary related statistical data are grouped into specific Data Families
lending to categorization and easy navigation of the metrics. The 5 data families in SebStat covers peer data
population which automates collection of data based on similar structures. They include:
FIM - Monthly Financial and Statistical Data;
FID - Daily Financial and Statistical Data;
FEX - Statistics on Foreign Currency Turnover;
MTR - Money Transfers Statistics;
PCS - Payment Cards Statistics;
BTR – Banking transactions statistics (ongoing project);
CSH – Cash Statistics (ongoing project).
A SebStat key is composed of specific digits and the number of consequence of these digits are stated by the
Data Families. Statistical requirements towards commercial banks are specified as a list of keys rather than a list
of Excel based questionnaire forms. Each key in turn is tied to a database object or database formula object if it
represents a calculated series.
Source Instrument Currency Residency Sector Type of econ.activity Interest Rate ValuationFrequency
BBB.MM.F04000.GEL.RR.S11000.A0.11.30.3926076.00.20140531.
=

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SebStat features:
a) Use of Sectoral Balance
Sheet framework
b) Primary breakdown by
financial instruments
c) Instruments disaggregated
by counterpart
d) Instruments disaggregated
by type of data (stock/flow)
e) Instruments disaggregated
by status (assets/liabilities)
f) Breakdown by currency
Benefits of the SebStat Information Model within Statistical Data Production
The SebStat framework reflects the value of the stocks of financial assets and liabilities of a financial institution at
a point in time by instruments, by counterpart sectors and by currency. It shows financial flow results from
changes within a period – transactions, valuation change and other changes in volume of assets (OCVA).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Financial Statement DataStructure

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Data collection is
organized through web-
based mode and provides
a step-by-step
improvement in the scope
of financial institutions.
From the respondent’s
point of view, there is no
more paper or Excel
spreadsheet reporting
required. There is no
necessity for data
reconciliation among
different statistical
domains as reconciliation
is practiced at the raw
source data level.
The benefits of a SebStat data model tied to analytic data management system like MAP include:
 Improved statistical production process and increased productivity of statistical activity
 Improves coordination of statistical activity and avoids duplication of data; reducing reporting burden
 Facilitates harmonization of statistical business process as a whole
 Centralized analytics to harmonize statistics and provide consistent reporting
 Desktop data visualization tools and intelligent information exchange
 Web based software to track workflows around digitization of financial inputs and reporting
Financial Instrument
National Currency
Foreign Currency
Nonresidents
Residents
Residents
Nonresidents
Sectors of National Economy
Nonfinancial Corporations
Financial corporations
Government
Households
NPISHs
Sectors of National Economy
Nonfinancial Corporations
Financial corporations
Government
Households
NPISHs
By individual currency
By types of economic
activity
By sub-sectors
By sub-sectors
By individuals
By entrepreneurs
OS T VC OCVA CS

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Automation & efficiency through well-structured raw data
Frameworks like SebStat combined with data management software like the MarketMap Analytic Platform can
streamline the traditional statistical business process model considerably. Rules based validation combined with
metadata driven collection of well-structured raw data minimizes the need for manual intervention on the
validation and processing stages of the GSBPM.
Data inquiry represents a
list of keys; instead of
questionnaires. There is
no need for paper or Excel
spreadsheets reporting nor
is there overlapping and
data inconsistency. Data
reconciliation among the
different statistical
domains is minimized as it
is now also practiced at
the submitters level.
MAP features a web-
based workflow
management tool (the
FAME Enterprise Data
Manager- FEDM) to help
process these submissions
and monitor the automated processes from collection to validation to dissemination. It can automatically process
SebStat and SDMX data submission files and export data based on both standards.
FEDM running with
MAP features a rich
layer of data
collection utilities that
can process Excel
files, questionnaires,
XML documents
containing statistical
data and relational
tables for storage in
the optimized time
series container. The
analytic data loading
toolkit within MAP
provides loaders and
configuration files that
map the well-
structured raw source SebStat and SDMX source files to FAME and loads complementary metadata to the SQL
metadata repository.
Standardization of data
structure makes
extremely easy to
identify, design and
build new requirements
within the existing
mode, without
additional costs
Well structured raw data
simplifies automation of
collection and
processing procedures
and makes final outputs
more efficient
Thanks to optimization
of statistical business
process as a whole there
is more time for
analytics, improving
communication ways
with users, system
development etc.

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As data is mapped
from source files
into FAME, the
objects are named
based on the
SebStat or SDMX
information model.
Attributes needed to
transform and
disseminate the
data arrays
managed in FAME
into web friendly
formats are
attached to the
objects and
persisted within
metadata
management layer.
Collected data is initially stored in a “staging” area where validation rules can be applied to the data and any
anomalies not detected during the load process can be flagged and key stakeholders alerted. Corrections to
individual observations and entire time series can be made within FEDM data manager and saved back to this
staging area. Before data is promoted to additional areas such as a department server or external production
server, data governors can log-in and approve / reject the proposed changes.
FEDM in turn runs within an overall enterprise data management platform with APIs to link well-structured
SebStat and SDMX raw data along with harmonized aggregated statistics to other applications and reporting
tools.
Desktop data
visualization and
analytic tools
Connections to Tableau
and JasperSoft BI
reporting
Centralization of
analytics and application
integration
NoSQL analytic data warehouse for time series
statistics with coupled SQL database for modeling
SebStat and SDMX metadata
Extended data collection
network mapped to
SebStat and SDMX
information model
Data dissemination /
and online import
based on SDMX-ML

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Key features and workflows within FEDM can be accessible to SebStat analysts, economists and external
submitters. Access is via log-in and this log-in can be tied to the user’s Active Directory / LDAP. Based on that
sign-in key workflows, databases, reports and tools are presented in a tabbed web interface. Dashboards
analyzing specific sectors and macro-results are provided as well.
Features of FEDM include:
 Powerful end-to-end solution for data collection process and statistical business process model
 RESTful web services for scheduling data loading
 Enhanced cross validation process
 Batch approval process
 Time series analysis and management
 Formula expansion for slicing and dicing
 Sophisticated data administration and monitored user entitlement
 User-friendly access to data through data portal
The FAME Enterprise Data Manager (FEDM) supports the following functionalities:
FEDM tab Functionality Description
Manage Managing users and groups,
and their roles and
permissions
Handles how users, user groups, roles, permissions,
DataHub groups, input forms, bulk data operations,
and report groups are administered in the FAME
Enterprise Data Manager
Data Browser Browsing data Handles how data is browsed using FAME Enterprise
Data Manager
Data Entry Accessing FAME databases Handles how data is accessed and retrieved from
FAME databases
Basket Managing data and baskets Handles how data is managed in the FAME Enterprise
Data Manager using baskets
Data Promotion Promoting data Handles how approved data is promoted in the FAME
Enterprise Data Manager
Validation Validating data Handles how data is validated by setting validation
rules in the FAME Enterprise Data Manager
Inbox Handling notifications Handles how notifications are handled in the FAME
Enterprise Data Manager
Data Reports Generating reports Handles how reports are generated in the FAME
Enterprise Data Manager
DataHub Handling input forms Handles how input forms are handled in the FAME
Enterprise Data Manager

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Conclusion
Central banks, monetary authorities and national statistics institutes tasked with providing timely, accurate and
useful monetary and financial information are in a race to modernize their infrastructure in response to the global
financial crisis and the promise of emerging technologies and standards. Those focused on the collection,
processing and dissemination of statistical information in general, and of Monetary and Financial Statistics in
particular, should consider standards like SDMX and SebStat for creating well-structured raw source data.
The statistical production, like any other production process means inputs, processing, and outputs. SebStat is an
information model focused on these processes. Its goal is to develop and promote methodological and technical
standards and procedures and to reduce the reporting burden associated with the collection of data.
In turn, fully supported analytic platforms such as the MarketMap Analytic Platform can embed frameworks like
SebStat within an automated data collection, validation and dissemination platform run at the central authority.
• Standardize and harmonize statistical data and metadata;
• Collect information through unified scheme and structure from different financial institutions;
• Automate data collection and processing and increase effectiveness of statistical production;
• Improve data validation process and data quality;
• Expand the analytical capability of statistical data;
• Reduce the reporting burden of data providers.
For more details on the FIS MarketMap Analytic Platform contact Tim Earnshaw, FIS Sales, at
timothy.earnshaw@fisglobal.com or call him on +44 791 297 4613
For information on SebStat from the National Bank of Georgia, contact
Nana.Aslamazishvili@nbg.gov.ge