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Jul 21, 2021
Safe, usable data can be one of most valuable assets for any organization. At Privitar, we work closely with AWS to ensure customers get faster time to value from analytics, AI and machine learning tools by leveraging safe data in the cloud.
Recently, Jorge A. Lopez, Global Lead for Data & Analytics Partner Strategy at AWS with Steven Totman, Privitar’s Chief Product Officer, discussed how organizations can get business value from data, handling sensitive data in secure, compliant and appropriate ways.
I’m going to share my three key takeaways here, but don’t let that stop you from listening to the full conversation, led by Mike Simons, Contributing Editor of CIO magazine, on-demand here. See if you agree, and share your thoughts with our team in the comments below.
Enterprise data has always incorporated sensitive data, but widespread adoption of analytics and cloud computing have reshaped both the opportunities and the risks.
What does this mean for companies trying to extract more value from data?
Organizations recognize that their ability to compete depends on the ability to leverage data. Technology makes it easier to collect and analyze more data than ever before, but with more data comes more responsibility. Good data is key, but you need proper controls, data access policies, security, and data governance.
Companies with a thirst for analytics need governance, but the governance landscape has changed dramatically in recent times through an increasing number of regulations. What used to be guidelines and best practices are now laws in effect, and this can have a devastating consequence for organizations that fail to comply.
Privacy is center stage in many regulations, such as GDPR and CCPA. If you look closely, many of the challenges these regulations try to address centre around personal identifiable information (PII). Data breaches are meaningless without PII. ‘Right To Be Forgotten’ and user consent rules are also mostly related to PII.
So if you can anonymize sensitive data, including PII, while still retaining the analytical value of the data, then you can solve many of the concerns around data privacy. You can turn sensitive data from a liability to an asset that you use for innovation, and a source of competitive advantage.
If you think about your house, data security is like the lock on your door; it stops people getting in. Privacy is like the curtain on your windows, allowing you to do things within your house that are safely hidden from view.
When customers deploy on AWS, they inherit all the best practices, policies and operational processes built to satisfy the requirements of the most security-conscious customers. At the same time, organizations need privacy services that allow you to push data through a system and come out with safe and secure data that affords you maximum utility in machine learning and analytics, recognizing and de-identifying the personal identifiers in data.
Good data enables analytics. Once you can do that you can move to machine learning and AI, but as you build the layers, privacy is the critical piece. For companies that do it right, privacy is the freedom to use data responsibly and ethically across your business and beyond.
The goal is to provision data safely and securely so that when someone goes to an approved, protected dataset they know they are using everything legally and ethically.
Data is a key asset because it fuels innovation. The cloud has distinct advantages in this area. If you can collect every single data point about your business and distill all the insights you need, you are able to find economies and efficiencies, and innovate with new products that improve customer experiences.
Traditional on-premise architectures are very siloed. They are difficult to procure, set up and scale. You needed a ton of hardware to run an experiment, and by the time you set-up servers and configure software months may have passed. If the experiment fails you are stuck with a big investment. Therefore large organizations limited this kind of experimentation.
In contrast, in the cloud you have an environment where you can store all your data in a central location (e.g. Amazon S3) and then you have a very broad set of analytic tools and engines to analyze the data in many different ways. Now, if you want to experiment, it’s very easy. You run the experiment, pay for what you use and take the results. If the experiment fails, it’s not a problem. You didn’t make a huge investment. You take your learnings and move on. If the experiment is successful, you can deploy in production very quickly.
The Privitar and AWS joint solution is focused at enabling customers to do something dramatically different with data. De-identifying data, clear management policies, and making data traceable gives you the springboard to innovate on data.
The solution’s power lies in its ability to create a de-identified data lake where you can address the concerns around data privacy but still use your data for innovation. Customers are finding that data which was once seen as a liability can now be fully leveraged as an asset.
Much more can be said about the business value of sensitive data and how to reach it. Privitar’s new Safe Analytics on AWS Resource Hub provides a variety of resources that will help you understand how to reach maximum value with safe data analytics on AWS. I want to encourage you to make a visit.
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