numéro 25 février 2023 #RetourSur… 4 ans de collection numérique la collection numérique ↓ 58 ↘
IMPLEMENT GOVERNANCE POLICIES THROUGH TECHNOLOGY
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To advance compliance and privacy , we must be cautious about use cases , build appropriate access controls , and maintain logs to audit compliance .
That is why we are operationalizing data governance into the organizational and technical model from the start .
Some key features include : ● Generating identified , de-identified , and synthetic versions of each data product .
This will enable policy-based access control (PBAC) as well as dynamic column and row masking .
Only when a user can clearly demonstrate the need for an individual-level intervention identifiable records may be provided .
Where group level aggrega- tions will suffice , de-identified or synthetic data will suffice .
● Operationalizing data governance and cataloging through the Ana- lytics Engineering team .
With this structure , we can determine the “rules of use” for each data product , then automate workflows , log- ging , auditing , and validation of data life cycle policies .
↘ Figure 3 : "Planet and Moons" Model Differential Access Based on Use Case Fully Identified Data De-Identified Data Synthetic Data (Individual Interventions) (Aggregations and Predictions) (Model Training and Experimentation) ↘
ALIGN YOUR DATA ENGINEERING AND ANALYTIC TEAMS
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Making data useful at scale requires diffuse technical skills .
Analytic teams may lack the capacity to build secure , reproducible data pipelines , while engineering teams may lack the institutional knowledge or domain expertise to create data models or documentation .
In this situation , siloed efforts result in technical debt , conflicting sources of truth , and subpar products across both domains .
To alleviate this problem
UCB
is : ● Creating a data engineering team comprised of analytic and data engineers .
The former are responsible for interfacing with domain experts to model data sets .
The latter are responsible for building and maintaining the data tech stack , and for putting data models into produc- tion .
The teams converge on operationalizing analytic workflows , deployment of data science models , and integration of analytic outputs into downstream systems .
● Aligning the group with a common project charter , and unifying roadmaps , backlogs , and toolkits , agile leadership , and budgets .
Eventually we aim to staff a “data desk” to act as a single point of entry for all campus data requests .
We believe that enabling data users will provide substantial benefits to our campus commu- nity , particularly in the areas of student success , faculty and staff retention , and operational efficiency while also maintaining strong governance , data management , data quality , and security standards .
References Borgman ,
C
.
L.,
& Brand ,
A
.
(2022) .
Data blind : Universities lag in capturing and exploiting data .
Science (New York , N.Y.),
378(6626) , 1278–1281 .
https://doi.
org/10.1126/science.
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