Sample Survey Questions

Set I

All applicable questions in this example use the following scale:
[ Not Initiated | Planning | Underway | Performed | Achieved ]

  1. General - Does your organization have a defined strategy for identifying TCoR?
  2. General - Are the insured (uninsured) costs of perils as they relate to the business processes of your organization catalogued and retrievable?
  3. Specific - What was the total insured (uninsured) loss you experienced during the ___________ (i.e. 1994 Northridge earthquake or 2017 Northern CA wildfires)?
  4. More Specific - As an organization in the ____________ industry, please provide loss data (in US $) for the business processes of ______________, _____________ and _______________ as they related to the _____________ (peril).
  5. Very Specific - In what business processes of your organization do you feel you are at the greatest amount of risk? What specifically are you basing this on?

Set II: Scoring Criteria

Survey questions provided by the Enterprise Data Management Council.

Engagement Right people at the right levels with organizational influence involved in the data program.
Process Established, structured, standardized and repeatable.
Evidence Capabilities supported by auditable evidence of adherence

    Line of Demarcation
  • 3.75+ (well on way)
  • 3.40↓ (work to do)

Score Category Description:

1 Not Initiated - Ad hoc (performed by heroes)
2 Conceptual - Initial Planning (whiteboard sessions)
3 Developmental - Engagement Underway (stakeholder recruitment; project-based activity)
4 Defined - Performed and Verified (roles and responsibilities structured; policy and standards implemented; content infrastructure established; sustainable funding)
5 Achieved - Adopted and Enforced (executive management sanctioned; activity coordinated; adherence audited; strategic funding)
6 Enhanced - Integrated (continual improvement)

Question 1: Our organization has a defined and endorsed data management strategy.
Implications: Defines the objectives of the data management program including how it will be implemented and governed. The goal is confidence in program objectives, approach and implementation.

Question 2: The goals, objectives and authorities of the data management program are well communicated.
Implications: Communications is not a sideline activity. The objective is enhanced awareness and alignment by stakeholders on the goals, scope, priorities and policies for the data program.

Question 3: The data management program is established and has the authority to enforce adherence.
Implications: The Office of Data Management needs the formal and official authority to implement the data management program. The goal is to make sure the stakeholders understand that adherence is mandatory.

Question 4: Stakeholders understand (and buy into) the need for the data management program.
Implications: The concept of data content as part of the operational infrastructure of the organization is new to many stakeholders. The industry still faces challenges adopting a data management culture.

Question 5: The funding model for the data management program is established and sanctioned.
Implications: Funding for the data management program can’t be optional. There is a difference between “seed funding” and “sustainable funding” for the data program.

Question 6: The costs of (and benefits associated with) the data management program are being measured.
Implications: You can’t manage what you can’t measure. A standard methodology for capturing expenses and benefits beyond traditional ROI is necessary for long-term organizational buy-in.

Question 7: Sufficient Resources: the data management program is sufficiently resourced.
Implications: Data management requires commitment from a wide variety of stakeholders. The goal is alignment of objectives with project delivery, funds, staff capability and time allocation.

Question 8: Data management operates collaboratively with existing enterprise control functions.
Implications: Effective data management is built on collaboration across IT, operations, control functions and lines of business.

Question 9: Data governance structure and authority is implemented and communicated.
Implications: Turns data management objectives into operational reality. Hire CDO … establish PMO … define governance routines … establish project review procedures … make funding operational.

Question 10: Governance “owners” and “stewards” are in place with clearly defined roles and responsibilities.
Implications: Appointment of individuals within the lines of business and control functions with direct responsibility for data management (requirements capture, quality control, data meaning, transformation processes, data flow, coordination across functions).

Question 11: Data policies and standards are documented, implemented and enforced.
Implications: Data management is governed via policy and implemented based on organizational standards. Implementation of the “rules of the road” with consequences and agreement from stakeholders is a prerequisite for effective data management.

Question 12: The end user community is adhering to the data governance policy and standards.
Implications: Adherence follows adoption. Adoption frequently requires a “burn-in” period to enable stakeholders to adjust and address gaps in data management capability. Adherence also requires enforcement mechanisms and the presence of control processes (i.e. checkpoints, formal review processes, audit engagement).

Question 13: The business meaning of data is defined, harmonized across repositories and governed.
Implications: One of the principle objectives of data management. Without achieving this capability, the industry will not be able to unravel interconnections, manage complexity, automate processes or manage linked risk across the financial system.

Question 14: Critical data elements are identified and managed.
Implications: Unraveling the data attributes that have a material impact on business functions is an essential component of the data management equation. It helps to establish priorities and determine where to focus limited resources. CDEs are derived from lineage and frequently have to be reverse engineered from calculated and aggregation processes.

Question 15: Logical data domains have been declared, prioritized and sanctioned.
Implications: Data needs to be characterized and organized as an early step in the data management program. Managing this across a complex enterprise is a difficult proposition and is based on an understanding of how the business functions and how data is manufactured across linked processes.

Question 16: End-to-end data lineage has been defined across the entire data lifecycle.
Implications: Documenting lineage starts with data discovery and includes reverse engineering critical measures and unraveling the flow of data across vertically aligned IT environments. It is an essential part of the data quality (and requirements) verification process – but with thousands of applications and hundreds of data models – the process is daunting.

Question 17: Technical architecture is defined and integrated.
Implications: Translation of data management objectives into the pathway for technical implementation. This is a collaborative activity to define the database strategies, analytics platforms, middleware solutions and process control considerations needed to support the data objectives of the organization.

Question 18: All data under the authority of the Data Management Program is profiled, analyzed and graded.
Implications: Profiling creates a quality benchmark for the organization to support the delivery of fit-for-purpose data across all data quality dimensions. This includes the definition of business rules and the capture of profiling results as metadata.

Question 19: Procedures for managing data quality are defined, implemented and measured.
Implications: Demonstrates the presence of a data quality strategy including the identification of accountable parties, assignment of DQ responsibilities and the implementation of a data quality control process.

Question 20: Root cause analysis is performed and corrective measures are being implemented.
Implications: Tactical approaches to fixing misaligned or corrupted data do not identify or address systemic data quality issues. Addressing root cause reduces the need for ongoing remediation and improves confidence as data flows across processes and among counterparties.

Question 21: Technology standards and governance are in place to support data management objectives.
Implications: Technology infrastructure governance guarantees adherence to platform and tool standards. The goal is to ensure alignment of IT with the data needed for applications along with the ability to comply with SLAs and data content requirements.

Question 22: The data management program is aligned with internal technical and operational capabilities.
Implications: Ensures that the objectives stated in the data management strategy are consistent with and achievable given the resources and capabilities of the IT architecture (including operational implementation).