about.html

---
layout: default
title: News
---

<div class="container support" style="margin-top: 10px;">
  
  <div class="row" style="padding-top: 20px">
    <div class="col-lg-12">
      <h2>The Data Station</h2>
       <p> Whenever data and models are shared, transformation ensues.
Breaking down data silos unleashes value that makes companies
more competitive. Pooling knowledge, such as when hospitals form
coalitions, accelerates discovery. Entire disciplines change when
researchers share benchmarks and models. However, three
barriers prevent effective sharing: easy access to sensitive data, data
discovery and integration, and data governance and compliance are
all challenges with both technical and human components.</p>
      		
			<h3>Challenges</h3>
			
			<h5>Discovery and Integration</h5>
			<p>Data lakes ease data access by collecting unrestricted datasets in a central repository where
			they may be accessed and downloaded by analysts. However, large
			volumes of data mean analysts spend more time in finding (discovery) and combining (integration) datasets than in their analysis.</p>

			<h5>Access to Sensitive Data</h5>
			<p>Organizations are wary of sharing data
			because they fear information leakage [13]. Simple anonymization
			techniques do not suffice [22, 30]. These disincentives block data
			sharing and stymie innovation.
			</p>

			<h5>Data Governance and Compliance</h5> 
			<p>Analysts routinely download datasets from databases to produce machine learning
			models, reports, and other derived data products. The consequence
			is a governance nightmare for those who want to control access
			to sensitive information, need to comply with regulations such as
			GDPR and CCPA, or want to ensure ethical use of data.
			To tackle these challenges, a radically new data architecture is
			needed to address both the technical and the human problem. Such
			an architecture must change how people access, and use data</p>

			<h3>Enter the Data Station</h3>
			<p>In the Data Station architecture, both
			data and derived data products—such as ML models, query results,
			and reports—are sealed and cannot be directly seen, accessed, or
			downloaded by anyone. The key idea is that instead of delivering
			data to users, users bring questions to data. For example, instead of
			downloading a dataset to train a ML model, a user may tell the Data
			Station what model they need and the Station identifies a suitable
			data + model combination, trains the model on the data, and makes
			the trained model available for inference. This inversion of compute
			and data mitigates many security risks of sharing sensitive data.
			</p>

			<p>
			Centralizing data and computation permits fine-grained yet scalable 
			data access: users see results of their tasks only after they have
			been given permission. In this model, data lifecycles and provenance 
			are known, which permits straightforward implementation
			of data governance policies. For example, it is possible to prohibit
			the use of non-interpretable ML models; to control the attributes
			included in training data to avoid propagating biased and unfair
			models; and to limit the data used for deriving data products to
			avoid leaking sensitive data. In general, it is possible to control
			what and how derived data products are produced and used.
			</p>
			<p>
			Centralizing data and computation has another benefit: 
			the Station sees all datasets, all models, and all compute requests. This
			information lays the foundation for the design of data markets.
			Data markets incentivize humans to share data and concentrate
			their effort where it matters most: assisting with discovery and
			integration tasks. Market forces can be used to recruit humans
			to clean datasets, to indicate how to join datasets, or to annotate
			datasets with tags and other documentation.</p>

  </div>
</div>