​Frequently Asked Questions (FAQs)

What is the Data Innovation Office (DIO)? 

AKU has invested in the creation of the Data Innovation Office that is focused on supporting research projects employing data intensive methodologies. The DIO deploys all projects on a modern data stack utilizing cloud technologies and open-source algorithms. A key mandate for the DIO office is to leverage data in innovative ways to address population health challenges in East Africa thereby leapfrogging over many traditional Western methodologies. 

What does the Data Innovation Office (DIO) do? 

                                       

I hear the Data Innovation Office supports the UZIMA-DS​ project – in what capacity?

The Data Innovation team plays a vital role in supporting the data infrastructure needs of the UZIMA-DS project. Operating within a cloud-first, open-source environment, the UZIMA-DS architecture undergoes continuous iteration, incorporating emerging data algorithms, best practices, and standards. Our team's primary responsibility is to ensure that all data required by researchers undergoes processing and cleansing through high-quality data pipelines and is then organized within a data model optimized​ for analysis. Additionally, we provide essential training and support to researchers on accessing cloud resources and utilizing tools effectively. Furthermore, our team ​ensures compliance with Kenyan Data Protection laws and serves as the primary contact with the Office of the Data Protection Commissioner.​​


What should the AKU community and external partners expect from you?

                                                      

What are the key tenants for the Data Innovation Office?

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How does the Data Innovation Office support​ researchers?

  1. A researcher wants to undertake a study on prevalence of heart attacks amongst elderly population in the urban areas of Kenya. 
  2. Data Source: We provide anonymized medical records from our health data repository to support their grant application.
  3. Data Preparation: Our data engineers clean and process the data to make it ready for analysis.
  4. Data Infrastructure: The Data Innovation Office establishes systems for storage and processing of data to make sure they’re compatible and compliant with data privacy regulations.
  5. Consultation and training: We advice on applicable AI models that can be used for predictive analysis
  6. Privacy and Ethics: We ensure compliance with data privacy regulations and ethical guidelines throughout the pipeline.​