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