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![Page 1: Francis Gross DG Statistics, External Statistics Division 6 & 7 May 2010 Committee for the Coordination of Statistical Activities Conference on Data Quality.](https://reader036.fdocuments.in/reader036/viewer/2022081602/5514b53b55034640138b52d9/html5/thumbnails/1.jpg)
Francis GrossDG Statistics, External Statistics Division
6 & 7 May 2010Committee for the Coordination of Statistical Activities
Conference on Data Quality for International Organisations
Data Quality Management for Securities
–
the case of the Centralised Securities DataBase
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Financial statistics based on micro data• Statistics based on individual securities data
– Micro-data generated from business processes– Classifications by statisticians
• Benefits– Flexibility in serving event-driven policy needs, near time– Ability to drill down, linking macro- to micro-issues
• Tool: the Centralised Securities Database (CSDB) – Holds data on nearly 7 million securities– Is in production with 27 National Central Banks online
Macro-statistics on“who finances whom”
and “how”
Securities data
Issuer data
Holdings data ** aggregated by economic sector and country of residence
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Centralised Securities Database (CSDB)
• CSDB provides consistent and up-to-date reference, income, price and volume data on individual securities
• CSDB is shared by the European System of Central Banks (ESCB)
• CSDB is intended to be the backbone for producing consistent and harmonised securities statistics
• CSDB plays a pivotal role in s-b-s* reporting as the reference database for the ESCB & associated institutions
* s-b-s: security by security
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Main features of the CSDB
• Multi-source systemFor coverage and quality: data from several providers
(5 commercial data providers, National Central Banks (NCBs))
• Daily Update frequency 2 million price- & 1 million reference data records per day
• Automated construction of a “golden copy” Data on an instrument are grouped using algorithms; most plausible attribute values are selected
• Data Quality Management (DQM) NetworkStaff from all NCBs contribute to DQMThrough human intervention and increasingly systems, toCheck “raw” data and validate “golden copy” results
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Data quality – two pillars
• Tackle issue at source• UPSTREAM• Preferably in bulk• Centralised process
Data Source Management
• Tackle issue at NCB• DOWNSTREAM• Individual securities• Decentralised process
Data Quality Management
• The data quality of the CSDB depends heavily on:
• Data quality managers face two critical problems:
– What data is correct? Where to look for the “truth”?
(verify vs prospectus, internal databases, Google…)
– How can dubious data be identified?
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Metrics to steer and prioritise DQM
• Problem: no access to benchmark data
• Macro metrics:
– distribution of different reference data attributes (eg price, income)
• Micro Metrics:
– inter-temporal comparison (stability index, concentration change)
– consistency checks
– can drill down to level of individual security
Strategy: identification, quantification, prioritisation
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Example: metric for change in country / sector
• The system calculates indices of stability in country / sector for the relevant group of securities between t0 and t1
• An index for a country / sector pair below 1 shows change in the group of securities (country or sector or both)
• 2 indices are calculated: from the perspective t0 (sees leavers) and from t1 (sees joiners) in a country/ sector group
Security 1 Security 2 Security 3
Security 2 Security 3 Security 4
T0 NL/S.11
T1 NL/S.11
stays stays
joins sector/ countr
y
leaves sector/ countr
y
t0 perspective (Laspeyres concept) covers leavers but no joiners
t1 perspective (Paasche concept) covers joiners but no leavers
Join both concepts Fisher index = PaascheLaypeyres indexindex
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19 instruments, of which 14 kept their issuer identifier (CH_IDENT = 1)
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CSDB Data Quality Management Made us face a choice:
eitherSYSYPHUS
or CHANGE,
Change beyond Statistics
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Finding data quality further upstream
Prospectus “perfect”, public data source, but no common language
Commercial data sets error prone, selective, costly
production, duplicate efforts, proprietary
formats
Compounding a costly
process
Data Quality Management & defaulting
costly
Compound first shot
Golden Copy after DQM &
defaulting, still not back to perfect -
and not standard.
Candidates for
compounding, a costly
collectionDuplication and non-
standardisation in the very data generation
process hamper the whole downstream value chain.
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Where to start?
The first layer of data out of reality.
Its generation process matters
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Data capture drives IT output quality
Large scale IT processing can be simple and cheap when data fulfils the programmers’ quality assumptions.
Messy data capture delivers “garbage in, garbage out”.
• Once good data is in the system, processing can work well.
• Data capture from the “real” world is the key step.
• Once lost at capture, information in data is lost.
• No “data cleaning” will help: data must be captured again.
• Messy data capture at source is very expensive downstream:– Most applications perform badly
– “Data cleaning” and fixing failed processes are costly for all
– Processes and IT must be designed in complicated ways
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Progress is on its
way
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“…a standard for reference data on securities and issuers, with the aim of making such data available to policy-makers, regulators and the financial industry through an international public infrastructure.” (J.C. Trichet, 23.2.09)
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The industry expresses demand for a Utility• Industry panel at Conference 15 Feb 10 in London:
– “An international Utility for reference data has its place, but
– Keep it simple, (concept of a “Thin Utility”)
– Ask industry to design the standards (ISO does exactly that) and
– Give us the legal stick”
A viable reference data infrastructure benefits from constructive dialogue.
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From browsing to farming for data:
The long way to standardisation
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Climbing the stairway to action
Business leaders, Policy makers, Regulators & Legislatorsnow embrace the dialogue with the Data Community
Understand the role of data as a necessary infrastructure
Understand how basic data is generated
Understand basic data as a shared strategic resource
Understand dynamics of standardisation
Imagine a feasible way; accept that way as useful
Accept the issue among priorities
Design a legal framework
Build into data ecosystem
Build the business case with all stakeholders
Imagine solutions addressing legacy
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“Thin” Utility
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“Thin Utility”: a unique, shared reference frame
• Two registers: one for instruments, one for entities
• Simple and light, complete and unequivocal• Hard focus on identification and minimal
description• The shared infrastructure of basic reference
data for:– Data users in the financial industry– Data vendors– Authorities– The Public
• An internationally shared infrastructure of reference
A “Thin Utility” provides the certainty of a single source on known, bare basics.
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Refer
ence
Data
Utility
Two reference registers: the Thin Utility’s frame
Register of instruments
Reg
iste
r o
f en
titi
es
• unique identifier, • key attributes, • interrelations, • classifications,
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Refer
ence
Data
Utility
Two reference registers: the Thin Utility’s frame
Register of instruments
Reg
iste
r o
f en
titi
es
• unique identifier, • key attributes, • interrelations, • classifications,• electronic contact address
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The Utility grows from a quickly feasible base
Refer
ence
Data
Utility
Register of instruments
Reg
iste
r o
f en
titi
esFor both registers:• begin with feasible scope,• grow over time by • adding instruments & entities,• adding attribute classes,• driven by demand• from industry and authorities,• and by feasibility
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The international
aspect
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Global Utility vs. National Law: an option
International Operational Entity
Utility
Service agreements with national authorities
Runs the service: • Collects data• Distributes / sells data• Certifies analysts• Monitors compliance• Informs national authorities• Releases new standard items
Governance of the Operational Entity • Global „tour de table“ (IMF, BIS, industry, etc.)• Establishment of Int’l Operational Entity• Seed funding of Int’l Operational Entity ?
International Institutionse.g. G20: • Discusses new regulatory framework for financial markets• Defines principles / goals for data
International Community
National Legislator
National Authority
Entity
Issues law: • mandates national authority• empowers it to enforce the
process and • to farm out operations to an
international entity.• (EU issues specific EU law)
Executes the legal mandate:
• Farms out operations to the Int’l Operational Entity
• Monitors compliance• Enforces, applies sanctions
Complies with national law:• Delivers and maintains data
in the Utility as required, possibly using services.
National Constituency
Develops/maintains standards: • Designs initial standards• Monitors market developments• Steers evolution of standards• Designs new standard items
Standards College
Ideally,ISO-based
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Positioning and
Design
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Positioning in the data supply chain
Issuer
CDP
Data User
Utility
Initially,the downstream supply chain remains untouched, except for quality:data users don’t need to invest
Competitive market
Public
Policy makers
Regulators
Option: Utility as
Tier 1
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Organisational modelValue chain
Utility value chain: monopoly vs. competition
Standards:- design- setting
Analyst trainingAnalyst
certificationData production
Data distribution:- primary
- secondary
Multilateral (ISO?)
MonopolyCompetitionMonopolyCompetition
MonopolyCompetition
Each stage of the value chain shouldbe given the most efficient organisation