Tech Trend – Privacy Operations Infrastructure

5 Areas to Watch: Part 1


As early-stage technology investors, we see a wide range of companies working on game-changing businesses. This 5 Part Blog Series shares what our firm has been most intrigued by within the early-stage Enterprise B2B IT Infrastructure space in the first half of 2019.

Trends we noticed led to the 5 Areas to Watch which we’ll cover in this 5 Part Series:

Privacy Operations Infrastructure (PrivOps)

The looming threats of GDPR & CCPA will shift the way regulators define privacy and security. Enterprises realize that as these start to get enacted, more risks will arise in acquiring, securing and maintaining personal information. Also, with highly publicized failures from several large organizations in data privacy over the last few years, organizations now realize the negative consequences of privacy violations go far beyond regulatory financial penalties. Brand reputation, customer attrition, and revenue loss are all at stake. Privacy and Data Security has emerged to be a “continuous” need of the enterprise and we’re seeing the need for platform technology that manages privacy, risk, and compliance continuously, which has given birth to Privacy Ops Infrastructure (PrivOps) space.

Technologies that address next-gen data privacy standards will play a huge role in Enterprises getting up to speed with the new regulations and customer expectations. After reviewing the market, the main point we emphasize is that maintaining structured data is relatively easy and many solutions will be able to do this. Managing unstructured data will be a much bigger problem and is an increasing blind spot for organizations. If you have not already started, we recommend kicking off plans to manage these large sets of unstructured data. We see these 3 factors driving this: 

  1. Growing volume of unstructured data = more inherent risk. Unstructured data is harder to manage across the organization, which creates more blind spots. Also, it inherently holds more sensitive data, while being more prone to access from unauthorized parties. 
  2. The approach used for structured data doesn’t scale to unstructured data. It requires AI to solve. 
  3. Understanding relationships between entities in large volumes of text is very hard to do 

Sierra Ventures recently invested in a company in this domain called Text IQ, which is a machine learning system that parses and understands sensitive corporate data. Text IQ started as co-founder Apoorv Agarwal’s Columbia thesis project titled “Social Network Extraction from Text.” The algorithm he built was able to read a novel, like Jane Austen’s Emma, for example, and understand the social hierarchy and interactions between characters. This people-centric approach to parsing unstructured data eventually became the kernel of Text IQ, which helps corporations find what they’re looking for in a sea of unstructured, and highly sensitive, data.



Brendon joined Sierra Ventures in 2017 and leads business development operations for the firm, including the CXO Advisory Board and Annual CXO Summit. In addition, he partners with management teams in the Sierra Ventures portfolio to accelerate their growth by identifying mutually beneficial CXO engagements, key strategic partnerships, and providing support with analyst relations strategies. Brendon also works with the Investment Team on sourcing and due diligence. Brendon has a background supporting CIOs in the healthcare industry and investors in the Venture Capital & Private Equity sectors through his work at Gartner. Brendon holds a MS in Entrepreneurship and BA in Environmental Science from the University of Florida. Outside of work he’s an avid golfer, traveler, and big Florida Gators fan.

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