In our first article in this three-part series (see here), we covered the context (drivers and challenges) and acquisition of non-financial data.
This second article focuses on ESG Data Processing (with a focus on data quality management & enrichment) and Distribution in the Asset Management industry.
ESG (Environmental, Social & Governance) is one of the current key topics in the Asset Management industry, as well as in the broader economy more generally. The heightened focus on ESG is primarily fuelled by 2 major drivers: (i) a stronger pressure from regulators and (ii) an increasing demand from the market and investors.
The technical setup for ESG data comprises collection, processing, enrichment, and dissemination, while ensuring appropriate controls are in place. All steps are critical to efficiently support an Asset Manager’s end to end ESG activities. Accordingly, implementing an ESG data management strategy requires adequate governance, processes, and technology.
The successive steps of data life cycle
1. Data Processing
a) Data quality management and enrichment measures
The quality of ESG data has been improving over the past few years; however, the quality of data remains a challenge. Accordingly, users of ESG data need to have robust data quality control processes in place. High level of operational data quality, management and governance is typically exemplified through strong oversight by three key teams:
- Quality oversight first begins with IT teams who are responsible for proper data collection and storage with adequate tools. IT performs consistency checks ensuring that data is received in the expected format and has been updated completely and accurately
- The second layer of oversight resides with Data Analyst/Management teams who, amongst other things, are responsible for the data quality and availability. Data management make sure that collected data is accurate at issuer level and is mapped accordingly following the correct hierarchy
- The final level of oversight lies with the ESG analyst/Research function who is responsible for interrogating the data, understanding and improvement of existing data through additional analysis and enriching overall insights through the use of other selected data sources. Research teams typically focus on data relevance, suitability and usefulness to the analysis being conducted. Questioning often centres on plausibility, accuracy of the data as a true and accurate reflection of reality and consistency of the methodologies that underpin the underlying data / metrics to ensure it has not changed
These different checks can lead to discussions with the data vendor if errors in their files are detected, or if coverage of indicators or issuers is not in line with what was agreed. Research teams may instigate manual data overrides where data is incorrect or missing.
Data updates / corrections typically fall into one of two categories:
- Tactical fixes downstream (short term), which ensure data related issues are fixed and relevant reports are corrected (usually within tight timeframes)
- Strategic fixes upstream (long term), which ensure that recurring problems are permanently addressed and rectified
b) Data oversight
Establishing oversight indicators enables Asset Managers to monitor the number of data quality issues, as well as measuring the capacity to fix these issues. High volumes of data, subject to extensive controls and potential overrides, coupled with queries from multiple stakeholders often marks the data value chain complex. Accordingly, asset managers must incorporate into their data management processes technology and tools that enable effective: (i) data model management, (ii) workflow management, (iii) quality controls and (iv) user interface for overrides.
2. Data Distribution: Use of data across functions
Use cases for ESG data tend to be very different across Asset Manager functions but result in data consultation, modification and/or transformation.
- Research teams typically need access to all available information and may require edit access rights
- Portfolio Management teams may only require a limited amount of information, which is directly integrated into the Portfolio Management System
- Risk teams will often need access to historical data and data estimates to help identify, assess and manage new ESG risks transforming data into risk related insights
- Reporting teams access will typically relate to historical data over longer time periods
Meeting the above users’ requirements often involves one of two key options:
Creation of the issuer’s ESG identity card: this consists of a pre-built and pre-formatted view, to access the issuer’s main information, which comprises:
- A simplified version of the issuer’s identity card for data consumers (such as portfolio managers) allowing quick analysis and informed decision-making based on key data only
- A version for data producers (such as ESG analysts) that enables a broader view of available data alongside modification rights such as overrides and lock down of existing data to prevent inappropriate editing of existing data
Enabling ESG data queries and access to relevant databases: flexibility in terms of query functionality provides freedom to users and improves overall data processing capabilities. Data consumers should be granted access to create customized views by user type (e.g. via a Business Intelligence module) and to create their own queries to support more advanced use and analysis of data processing.
Both options are based on the principle of defining a unique golden source for ESG data within an Asset Manager’s ecosystem. ESG data should be stored within a single database to facilitate version control and centralized access, avoiding concerns over data consistency.
In conclusion, the different functional needs of ESG Data consumption require :
- functional applications (such as Portfolio Management System or reporting tools) connected to the ESG golden source data base, for which flows and evolutions are directly managed by IT teams for an industrialized usage
- accessible ESG golden source data base coupled with BI tools to offer agility for final consumers in charge to directly manipulate the data in full autonomy
As a result, all functions across the firm share consistent ESG data tailored for their specific uses.
Stay tuned for our next and final article in this series to be published soon about the key elements for sound and efficient ESG data integration, covering the guiding principles to establish robust Strategy, governance, organization, equipment and underlying target architecture.
Please reach out to us here if you would like to discuss your ESG data strategy requirements in more detail.