Privacy Compliant Analytics

By Himanshu Pawar posted 6 days ago

  
As Data literacy increases, Delphix enables privacy compliant analytics and accelerates AI/ML projects while making business operations compliant with regulations.


This article was co-written by Alex Hesterberg and Hims Pawar


Background

Privacy Compliant Analytics

Delphix Aligns with the Top 3 Priorities for Data and Analytics Leadership*

  • Creating a data-driven culture.
  • Developing a data and analytics strategy.
  • Standing up a data/ information governance program.

* Gartner 2021 data and analytics survey( here- https://www.gartner.com/en/publications/data-analytics-top-priorities-for-it-leadership-vision-2021 )


4 Tenets of Data analytics teams in 2021

  • Maximize Collaboration
  • Domain (Industry) Centricity
  • Investment in the Data Experience
  • Accelerate compliance with Data Governance

One of the biggest challenges is the wide gap between data analytics and governance and business operations, goals, and objectives.

Data Governance

As we can see, everyone is trying to unlock value in data. It comes down to how to analyze metadata and predict issues/outages. Who owns data and who can extract value from it? Some questions that data custodian teams ask:

  • Where is the data needed?
  • Is it ready to use?
  • Can I use it?

 

How Delphix Enables Privacy Compliant Analytics

Data in analytics platforms and most AI/ML operations that are being done is based on metadata or synthetic data thus unable to harness the full value of vast expansive data sets. Delphix enables Privacy Compliant Analytics in various ways, especially with techniques like Differential Privacy, which introduces a slight blur and creates higher quality data.

There are 3 main advantages in this use case:

  • Generate better predictions - Delphix enables the model to run on high-quality data that means the predictions/recommendations do not need a tremendous amount of data to provide results and only get more precise as you increase data.
  • Train Models better - Models run better on high-quality data as it reduces data drift and reduces the overhead to continuously tweak model algorithms. 
  • Faster execution - Using traditional tokenization and scrubbing the data loses key structures and statistical value, Delphix masks data so that it behaves the same as production without the compliance/PII risk. No changes are required to models to consume data generated by Delphix.

Our customers understand how the various tool-mesh and cloud resources work together and use Delphix in ways that make the Data Analytics culture more secure, better, and efficient.

Most common use cases where Delphix is used

Delphix provides extremely high-quality data for analytics and enables the following:

  • Linear regression
  • Self-learning models
  • Recommendation engines
  • Predictive analytics

Delphix Value

Delphix generates high-quality, compliant masked data optimal for analytics. It provides contextualized and lightweight data for agile delivery & makes data sharing easy and compliant. Delphix enables the data Analytics culture in the organization and encourages easy collaboration for increased data intelligence.




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