Leveraging best practices in data governance: An organization-wide data inventory and mapping project to support a five year data strategy
Bibliographic record
Abstract
IntroductionThe professional regulation sector is moving toward risk-informed approaches that require high quality data. A key component of a corporate 2017 Data Strategy is the implementation of a data inventory and mapping project to catalogue, centralize, document and govern data assets that support regulatory decisions, programs and operations. Objectives and ApproachIn a data rich organization, the goals of the data inventory are to: enhance authoritative data that support programs; identify data duplications/gaps; identify data sources, owners and users; and, apply consistent data management and standards organizationally. Routinely used data assets outside the large enterprise workflow system (excel/word files; databases; paper collections) were catalogued. Using data governance principles and a facilitated questionnaire, departmental data stewards were interviewed about their generated data. Questions included data purpose/sources/types/formats/owners, retention rates, analytical products, gaps and visions for a desired data state. A data mapping methodology highlighted data set and variable connections within and across departments. ResultsTo date, over 40 staff members in 10 departments were identified as data content experts. In addition to data in the corporate enterprise system, over 80 unique datasets were identified. In 1 large department, over 2,000 data elements across 26 datasets were inventoried. Data mapping analysis revealed thematic data domains, including member demographics, outcomes, certifications, tracking and financial data, collected and held in multiple formats ((Microsoft Access, Excel, Word), SPSS, PDF, e-mails and paper documents). While 72% of the data elements were formatted numerically, approximately 8% were free text. Significant data redundancies across staff members and departments were revealed, as well as unstandardized variable naming conventions. Gaps analysis highlighted need for standardized, electronic data, where not available and data management training. Conclusion/ImplicationsCustomized data mapping reports to data users will facilitate the development of local, standardized departmental data hubs that will centrally link to a centralized data repository to facilitate seamless organization-wide analytics, improvements in current data management practices and greater data collaboration with the ultimate goal of supporting risk-informed approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.117 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".