Third-Party Data for Externally Managed Portfolios

Rebecca Beasley-Cockroft Clearwater Analytics

Russ WarrenThomson Reuters

Agenda

• Data aggregation for a data vendor

• How Clearwater aggregates vendor data

• TR data validation

• Clearwater data validation

Learning Objectives

• Understand the complexities of aggregating data from multiple sources

• Learn about best practices for data validation to ensure data quality

• Learn about the benefits of using a golden copy of data for your accounting, performance, and overall reporting needs

Polling Question #1

Client Data

Universal SecurityMaster File

Clean, Validated, and Enriched Data

ReportingReconciliation

• Thomson Reuters• S&P• IDC• Morningstar

Data Aggregation

TR and Data Aggregation – GovCorp

• Source from story or vendor feed (skeletal)• Issuer and issue set up (vendor or offering docs)

New Bond

• Completeness check• Leverage – underwriter, clearing house, web,

SEC filing, issuer sources

• Quality checks back to source• Make corrections as necessary

TermsMonitor

Finalize

TR and Data Aggregation – GovCorp

TR and Data Aggregation – GovCorp

TR and Data Aggregation – GovCorp

Extraction• Change tracking

• Data transformation

• Data distribution

Updates• DBoR syncing

• Intraday

• EOD

Monitoring• Visual

dashboards

• System traffic

• Database crowth

• Abnormal status

TR and Data Aggregation

TR and Data Aggregation – Due DiligenceThomson Reuters data is gathered, processed, and stored in accordance with the following tenets:

• Data is acquired from verifiable and reputable sources.

• The global marketplace is constantly monitored for new and alternate data sources.

• An active and rigorous rights management process exists to ensure that all usage and distribution complies with legal requirements.

• As members of a variety financial industry organizations, we seek to identify and spread good practice between data vendor organizations.

TR and Data Aggregation – DatabaseModel DesignThomson Reuters data is processed and stored in several major proprietary databases whose design takes into account the following:

• The granularity required of the data record

• Accurate representation of the information

• Derivation of value from data relationships

• Ease of maintenance

• Timely data retrieval

• Effective operational delivery to products

Polling Question #2

Bringing in data from multiple vendors

• Creating a connection with the vendor

• Timing of the data

• Understanding each vendor’s data model

• Storing the raw data

Universal SecurityMaster File

• Thomson Reuters• S&P• IDC• Morningstar

Creating a “Golden Copy”

• Normalization of vendors’ data into a single data model

• Aggregation of data sources

• Translation of data into the Clearwater reconciliation data model

Universal SecurityMaster File

• Thomson Reuters• S&P• IDC• Morningstar

Clean, Validated, and Enriched Data

Using the Clearwater “golden copy” downstream

• Reconciliation

• Accounting

• Performance

• Compliance ReportingReconciliation

Data Validation

Core Data Quality Principles

Completeness Accuracy Timeliness

Polling Question #3

TR Data Flow from Source(Exchanges and third-parties)

Example validation checks

• Sanity Error – A price has moved beyond a certain specified range

• Integrity Error – The lowest price recorded is lower than the low

• Jump Check Error – A price has jumped more than the percentage specified for the market over the past three days

• Flat Check Error – A price has not changed

• Missing Error – A price is missing

TR Validation – Quality ControlQuality Control

• Exchange• Other source

• Validation layer

• Database update

• Product

TR Validation – Quality ControlOther Data checks on Thomson Reuters Databases

Thomson Reuters employs a variety of mechanisms to monitor data quality:

• Price reconciliation

• File delivery time checks

• Data comparison

• Data quality sweeps

• Raw data checks

• Second pair of eyes

TR Data Validation – Quality AssuranceFeed Monitoring Program

Thomson Reuters employs a variety of mechanisms to monitor the quality and timeliness of data supplied by vendors:

• QC checks on data accuracy, completeness and timeliness

• Monitoring feed outages and data interruptions at global technical centers

• Metrics are used in the creation and management of Service Level Agreements (SLA) with suppliers

• Regular service reviews are held with key strategic suppliers globally

TR Data Validation – Quality AssuranceData Quality MeasurementThe Data Quality Measurement Program at Thomson Reuters is owned by the Global Quality Manager:

• The program consists of a series of Accuracy, Completeness, Timeliness and Freshness measurements and metrics which are run on core datasets on a regular basis

• Dedicated quality teams around the world monitor data from over 30 countries to provide a national, regional and global view of data quality

• Root cause analysis is initiated to investigate issues with accountability for results assigned to managers and team leaders. Their respective team performances are evaluated and reviewed by senior management on a monthly basis

TR Data Validation – Quality AssuranceQuality Control ProgramsThomson Reuters data audits are performed either on the database or on the product to replicate the customer experience:

• The Data Quality Measurement Program focuses on new data entered into the databases over the course of the previous calendar month

• The Data Audit Program provides an assessment of data quality across the depth of available data history.

• Data Audits are normally designed to assess levels of accuracy, timeliness and completeness in a statistically valid sample of data

Validation as part of data aggregation

• Detection of missing vendor files

• Insufficient data to map

• Data models restrict the type of data possible

• Minimum data requirements to pass information downstream

• Errors for invalid values

Validation of the “golden copy”

• Data consistency checks across the golden copy

» Conditions that should always be true

» Data sets that should be consistent

» Stale data checks

• Data comparison against publicly available sources (SEC, EMMA, etc.)

Validation of downstream systems

• Reconciliation system’s validation based on reconciliation data

• Reporting systems’ validation based on reporting data

Resolving data inaccuracies

• Determining the source of the inaccuracy

• Assessing the type of change needed

• Correcting data as quickly as possible

Outcome of Aggregation and Validation

Outcome of Aggregation and Validation

Questions?

• Take the post-session survey in the Clearwater Events app and scan out as you exit

• Take the Clearwater Client Benchmark Survey in room 440 and earn Clearwater swag

• Don’t miss the Monday networking reception from 4:30 – 6:30 p.m.

• Presentation is available in the app

Reminders