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Industry Benchmark
Benchmarking the Industry

RESEARCH & benchmarking

EDM Council Research

In support of our membership the EDM Council conducts research into a variety of data management topics and evolving trends. The Council aspires to evaluate trends and issues, collaborate with multiple industries and their subject matter experts, and produce valuable resources for our membership. Topics include defining critical data elements (CDE), data implications for analytics, AI and machine learning and evaluation of regulations and their data management impacts.

Global Industry Benchmark

As part of the agenda for data management research, the EDM Council conducts periodic benchmark studies, using DCAM® (Data Management Capability Assessment Model) as the framework for the analysis.

2019 Global Data Management Benchmark

The 2019 Benchmark survey closed on October 25, 2019. Watch this space for the published results in Q1 2020.

The 2019 Benchmark study will not only assess data management within the financial industry but in multiple industries. This will be the first cross-industry study of the state of data management and will provide insights into the similarities and differences of its challenges and progress within and across industries. Once again, the 2019 study will be assessed using the DCAM framework and will ensure that all participants have a consistent view of where we collectively stand against the requirements for sustainable data management. 

2017 Study

The 2017 Benchmarking study consists of 22 DCAM-derived questions that highlight the most essential concepts in data management. The results include comparison to the previous study performed in 2015. Participants included the full spectrum of financial services companies (universal, buyside, sellside, asset servicing, and insurance) with an emphasis on G-SIBs and Tier 1 firms. We selected a "control group" of companies that we knew had demonstrated an organizational commitment to data management as the baseline for evaluating overall industry progress.

There are clearly some bright spots for the practice of data management. We have made progress in overcoming the inertia of organizational change management. But the underlying truth remains. We can't respond to regulatory pressure, achieve automation or put data to work until we fix the underlying data challenges. The EDM Council believes these obstacles can be addressed.

Key Findings and Highlights

The biennial Benchmarking study was produced, in collaboration with industry leader Sapient Consulting and Pellustro (the DCAM assessment platform developed by boutique strategy firm Element22). The survey covered 22 of the most essential concepts extracted from EDM the Council’s DCAM framework (see Benchmark Questions below) and incorporated responses from over 150 financial institutions. The analysis found progress being made towards setting up enterprise-wide data management programs and implementing data governance. However, it also highlighted several key areas where industry capabilities lag in meeting the requirements set out by global regulators and market authorities.

Key Findings

Previous Benchmark Reports

benchmark reports 2017
benchmark reports 2017

Benchmark Response Overview...

Establishing Data Management Programs

Data management as a core part of the way financial institutions operate is growing, but not fully entrenched. There has been some advancement in establishing a true “data management culture” particularly among G-SIBs and Tier 1 buy-side firms. Over 70% of the industry, and 90% of G-SIBs, now have a Chief Data Officer.

Establishing the Program

Governance Implementation

The industry has made substantial progress in establishing foundational governance defining organizational structures, implementing data stewardship and implementing operational policy.

Governance Implementation

Content Infrastructure

Work is underway on defining lineage, managing critical data and implementing data management glossaries. This is the core building block for meeting the regulatory goals of harmonized data necessary for linked risk analysis but progress has been slow to mature. Only 8% of the industry has achieved the harmonization of meaning of data across all internal repositories.

Content Infrastructure

Data Quality

The industry is still mired in manual reconciliation of data and mapping from physical repositories to applications and reports. Trust and confidence in the data used for regulatory reporting and business analytics remains an elusive goal of these data management programs. Just 13% of the industry has achieved the definition and implementation of control procedures for managing data quality.

Data quality

Organizational Collaboration

There is a growing understanding of data as a shared resource and timely progress in managing the collaboration between data, information technology, business applications and control functions such as information security and privacy.

Organizational Collaboration

Benchmark Survey Questions

1. Our organization has a defined and endorsed data management strategy

2. The goals, objectives and authorities of the data management program are well communicated

3. The data management program is established and has the authority to enforce adherence

4. Stakeholders understand (and buy into) the need for the data management program

5. The funding model for the data management program is established and sanctioned

6. The costs of (and benefits associated with) the data management program are being measured

7. The data management program is sufficiently resourced

8. Data management operates collaboratively with existing enterprise control functions

9. Data governance structure and authority is implemented and communicated

10. Governance “owners” and “stewards” are in place with clearly defined roles and responsibilities

11. Data policies and standards are documented, implemented and enforced

12. The end user community is adhering to the data governance policy and standards

13. The business meaning of data is defined, harmonized across repositories and governed

14. Critical data elements are identified and managed

15. Logical data domains have been declared, prioritized and sanctioned

16. End-to-end data lineage has been defined across the entire data lifecycle

17. Technical architecture is defined and integrated (NEW QUESTION ADDED IN THE 2017 SURVEY)

18. All data under the authority of the Data Management Program is profiled, analyzed and graded

19. Procedures for managing data quality are defined, implemented and measured

20. Root cause analysis is performed and corrective measures are being implemented

21. Technology standards and governance are in place to support data management objectives

22. The data management program is aligned with internal technical and operational capabilities