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Data Readiness Lab

The Data Readiness Lab provides a collaborative environment and contributes skills, tools, frameworks and resources to increase data readiness and data maturity among actors in both the public and private sectors.

Challenges

Work with data readiness is often underestimated. A low level of data readiness is a serious obstacle to AI development projects. The data readiness lab will focus on and develop tools and guidelines for four main challenges:

  • data readiness
  • annotation
  • anonymization
  • evaluation.

The project focuses particularly on text data but many of the results will apply to different types of data.

Data readiness
Guidelines as to what data readiness is and how it can be applied in practice. 

Annotation
Knowledge sharing and procees development relating to annotation.

Anonymization
Anonymization and pseudonymization through Named Entity Recognition (NER); i.e. using name recognition to identify and delete or replace personal information such as name, social security number, etc.

Evaluation
Guidelines and frameworks to define and develop evaluation data.

 

Project purpose

The Data Readiness Lab is a platform that contributes skills, tools, frameworks and resources to increase organizations’ and companies’ data readiness. This will accelerate the ability to use data to realize projects of organizational and societal value, for example, through the use of AI or language technology.

The needs related to data readiness are largely common to all types of actors. This Data Readiness Lab will increase the data readiness and data maturity of actors in both the public and private sectors.

 
 

Expected outcomes

  • By making tools and guidelines available, and raising knowledge about data readiness, partners will increase their data readiness and gain the opportunity to develop and apply AI widely.

  • The stakeholders contribute with tools, processes and methods as well as case studies for the evaluation of the lab's results.

  • The data owners' data readiness will be evaluated at the beginning of the project to create an opportunity to follow up on how their data readiness improved during the project.

  • Results and lessons will be widely disseminated.

 

 

Facts

The project is funded by Vinnova and coordinated by AI Sweden. Other project parties include the Swedish Public Employment Service (Arbetsförmedligen), the Swedish Association of Local Authorities and Regions (SKR), Strängnäs municipality, Statistics Sweden (SCB), the Swedish Police (Polismyndigheten), The Swedish National Financial Management Authority (ESV), KBLab, Peltarion and Gavagai.
Project period: October 2021 - October 2023.

 

Contact

Strategic Program Manager NLU, PhD

Francisca Hoyer