Roadmap to Data Integrity: Practical Data Validation, Verification, and Security Controls
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1 Roadmap to Data Integrity: Practical Data Validation, Verification, and Security Controls Emory Internal Audit Scott Stevenson, Chief Audit Officer Deepa Pawate, Associate Chief Audit Officer Kristy Habib, Manager, University Internal Audit 1
2 What is Data Integrity? 2
3 Data Integrity Multiple Dimensions Emory Policy Institutional Data Management says that data integrity is composed of: Accuracy: Data free from errors Completeness: All values are present. Consistency: Data satisfy a set of definitions or constraints that applied and maintained in the same manner across reports Reliability: Independent custodians or users obtain consistent results when applying the same definitions or constraints. Timeliness: Data are available when required. 3
4 Why Does Data Integrity Matter? Upfront & Ongoing Effort Risks of Data Integrity Issues 4
5 Examples of Data Integrity & Quality Issues Duplicate Records Data Collected Does not Match Context and Definitions Data Quality Spelling Errors Data Collected is Incorrect at Point of Entry/Feed Out of Date Info 5
6 Promoting Data Integrity = Validation + Verification + Security (1) Data validation The computer checks if the data is correct and makes sense (e.g., correct format, length, etc.). (2) Data verification and approval People step-in to confirm that the data is reliable and accurate (e.g., reported data matches source data, etc.). (3) Data security People limit access to data sets, systems, and spreadsheets to only those with a need to know (e.g. formal user access request process, access terminations, audit logs). 6
7 Data Integrity Key Controls for Your Unit/Department (1) Document your unit s data management procedures and controls: Data validation, Data verification, and Data security * Include source systems for data, where data is downloaded into (e.g., spreadsheet), who has access, how data is manipulated/edited to meet context of questions being asked, who reviews/approves it before submission to report preparer (e.g., Institutional Research) for reporting, etc. (2) Carry-out and implement unit-level controls. (3) Maintain evidence of unit-level controls. 7
8 (2) Carry-out validation, verification, and security controls: Data Milestone Examples of controls validation, verification, and security (1) Before, during and after data input/capture (2) After extraction from storage (3) After compilation for reporting purposes (a) Control access to spreadsheets and data systems (b) Enable system/spreadsheet audit logs to track changes (c) System/spreadsheet edit checks (prevent wrong data element type, range, etc.) (d) Designate independent reviewers to confirm correct capture (e) For automated feeds, establish a transfer log/record count check (f) Perform reconciliation manual or automated - between data extracted versus data source (entire population or sampling) (g) Agree on context and usage of data (Institutional Research and data owner/supplier) (h) Retain supporting data sets/results that substantiate reported values (i) Establish documented unit-level (data owner/supplier) data verification and approval process, which is relied upon by the report preparer (e.g., Institutional Research, who performs a direct query of your data OR receives a spreadsheet/data file from your unit). (4) Published data (j) Perform a post-publication review that data published was the data agreed upon. 8
9 (3) Maintain evidence of unit-level data validation, verification, and approval process Examples of evidence include: Unit-level approval support (e.g., s string from designated department level reviewers/approvers) Hardcopy sign-off approvals on forms maintained, etc. Approvals should be supported by retention of query and results supporting the submitted data. 9
10 Questions? 10
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