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Clairvoyant: The Story Behind This Big Data And Enterprise Security Company

This article is more than 6 years old.

With the rapid growth in the data footprint across the organizations, and the desire to use data to differentiate from competition, Organizations often find themselves vulnerable without all the tooling and protections in place to help secure their sensitive data. Chandler, Arizona-based Clairvoyant is a Big Data company that has built a platform for enterprise environments called Kogni, which solves that problem.

Kogni helps solve all of these issues with tools that enable companies to discover sensitive data in enterprise data sources (cloud-based and on-premise), secure data as it is ingested, and continuously monitor data sources for possible breach and policy violations. Essentially Kogni creates a “data catalog” of sensitive data such as credit card, social security numbers, etc. by scanning enterprise data sources including Hadoop, NoSQL, Cloud Storage, and RDBMS. Kogni leverages an ensemble of AI, machine learning, and data mining techniques to recognize sensitive data in both text and images. Lastly, Kogni continuously monitors the data sources and user behavior for anomalies and alerts on sensitive data proliferation and policy violations.

To learn more about Clairvoyant, I interviewed CEO Chandra Ambadipudi. Ambadipudi told me that he has roots in the higher education space. Before he launched Clairvoyant, he was VP of Engineering at Apollo Education Group, which is the parent company of University of Phoenix.

Clairvoyant works with a number of companies and helps build data products and platforms that enables them to leverage data as a strategic asset. Clairvoyant built this expertise building it’s first product Blue Canary, a predictive analytics solution to help higher education institutions with their student retention problems. Clairvoyant achieved this without external funding. Ambadipudi highlighted that because of its unique business model, Clairvoyant was able to fund product development through its services business and that helped the company grow and remain independent. In the past 5 years, revenue increased by over 60% based on a compound annual growth rate.

While working at Apollo, Ambadipudi led a team of 300 engineers, business analysts and project managers. He was also responsible for all aspects of the enterprise systems that University of Phoenix and other Apollo subsidiaries used. He led a technical team that implemented a predictive analytics solution that was a needle mover on improving student retention at the university.

The founding team's deep expertise in analytics, big data and software engineering, made launching Clairvoyant a “natural choice” and a seamless move next move.

“As is the case with starting and getting any new business off the ground, there were challenges. But, we had both a defined product idea as well a business case. A practical use case is something often missing from many tech startups. There’s a great product idea, but often no proof of concept,” said Ambadipudi in the interview. “Additionally, we were fortunate to have some senior executives and industry leaders as our advisors and mentors. Continued focus on big data services along with product development helped us scale the company rapidly from the start.”

Clairvoyant started in 2012 and launched Blue canary in 2013, In 2014, the company was awarded Startup of the Year by Arizona Tech Council. In 2014 Clairvoyant also started organizing the Phoenix Data Conference, an annual conference to bring together data scientists, engineers, product managers and other practitioners in the southwest region.

Clairvoyant now has over 50 customers and 300 employees world-wide. Blue Canary was then acquired in 2015.

I was curious about how the acquisition of Clairvoyant’s sale of Blue Canary to Blackboard took place so I asked Ambadipudi to elaborate on that.

"With our background and deep experience in the higher education vertical, we identified a business need amongst both large and small academic institutions to improve student retention. Based on the work we had done at Apollo Group, we realized that creating a predictive analytics driven product with custom algorithms and data science modeling tools would address this business need. This was the genesis of Blue Canary, and we created it with this specific use case. Clairvoyant as a company started with the vision to build SaaS-based products and Blue Canary was our first one. Blue Canary’s key component was a custom built Data Science Modeling tool (DataBrew) that automated the process of building customized predictive models. This significantly reduced the time it took to build models from days and weeks to hours and allowed us to easily scale product adoption” Ambadipudi explained. "When Blackboard acquired Blue Canary in 2015, it was a time when academic institutions were adopting technology that enabled them to improve learning and performance. Blackboard was specifically seeking technology that could power the next generation of their BlackBoard Learning Analytics and Predict platforms.”

Over the last five years Clairvoyant has evolved into an engineering organization that provides solutions across multiple platforms in enterprise systems, cloud computing and big data. And Clairvoyant gained and developed significant expertise in the Hadoop ecosystem building data pipelines, large scale data management platforms and analytical products for its customers.” In addition to its development expertise, Clairvoyant also provides highly specialized administrative and cluster management services to its customers, addressing a gap in talent and expertise.

Limelight Networks leverages Clairvoyant’s Hadoop Managed Services to drive improved availability, reduce outages, decrease costs, and gain access to a larger pool of skilled Hadoop architects, administrators and engineers. Ambadipudi said that Limelight Networks had a complex billing and reporting use case that their existing systems were struggling to compute within desired SLAs. Clairvoyant worked with Limelight Networks, defined service-level agreements (SLAs) for availability, job execution and turn around times and provided continuous monitoring and 24x7 support, addressing these operational challenges and allowing the teams to gain value from their Hadoop investment.

Clairvoyant

Along the path of building various data products, especially on the public cloud, the company identified that there were no tools that provided enterprises with a data-centric approach to security. Big data platforms, by virtue of centralizing large volumes of data, in a rapid and cost effective manner, enable monetization of data, but also significantly increases the risk associated with managing these assets from a compliance and security perspective.  “Many enterprises focus exclusively on perimeter security. Unfortunately, as demonstrated by an endless stream of high profile data breaches at major companies, the question of firewall breach is no longer a matter of if it will happen, but a question of when. Perimeter based security solutions have to be paired with data-centric security approaches to help focus on the protection of high value data assets and reduce the exposure of compromising sensitive data in the event of a breach. We developed Kogni to help organizations implement a data-centric solution. Kogni helps move customers from a highly tedious, manual approach to an automated, systemic approach that dramatically reduces the effort and time required to manage sensitive data in an enterprise landscape.” Ambadioudi added.

What are Clairvoyant’s future company goals? Ambadipudi answered that Clairvoyant’s vision is to be a “customer outcomes focused company driven by engineering excellence in the Big Data space” with plans to expand into new markets in Europe and Asia. Clairvoyant will continue on expanding their services on the Hadoop platform, and help organizations with the implementation of AI driven products. With Kogni, we are increasing our capabilities in anomaly detection, improving our AI engine to help catalog sensitive data in a more efficient ways, and finally provide ease of compliance with regulatory frameworks such as GDPR.

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