(US & Canada) Viral Kamdar, Data Governance Leader at Intuit, speaks with Jack Berkowitz, Chief Data Officer at Securiti, about the challenges related to people data and how it differs from financial data, the governance challenges with unstructured data, and the potential strategies to overcome those.

Intuit is a global technology platform that helps our customers and communities overcome their most important financial challenges.

With significant experience working with massive financial institutions, Kamdar discusses his recent pivot from handling financial data to people data. Highlighting the differences between the two, he states that people data is spread across various systems and applications. Whether it is payroll, compensation, talent acquisition, or retention, each of the applications operates independently, thereby creating disjoint data silos that are challenging to govern, says Kamdar.

Another noteworthy challenge with people data Kamdar mentions, is the need for numerous manual touchpoints across the employee data life cycle, which makes data automation challenging. Additionally, there are evident challenges when it comes to compliance with privacy regulations like GDPR and CCPA. There are varying standards within these frameworks related to data retention, consent management, and transparency.

Kamdar further states that a massive portion of employee data is categorized as sensitive or highly sensitive information, making access controls a critical factor. While people data is critical to driving operational excellence in an organization, it requires the strictest adherence to privacy and compliance.

Shedding light on data governance challenges with unstructured data, Kamdar states that while such data can be a goldmine for analytical insights, it can also be a minefield of governance challenges. Elaborating, he says that unlike structured data, unstructured data is difficult to catalog, classify, and validate.

Furthermore, the challenge adds on when it comes to identifying owners of unstructured data and determining access control policies. To succeed with unstructured data, organizations must have sophisticated metadata management systems that can process the data and classify it with similar rigor as structured data.

According to Kamdar, unstructured data in isolation is not as impactful as using it in conjunction with structured data. For instance, an organization can have structured data such as information on employee tenure, stagnation, and compensation, or declining performance reviews. Similarly, it can analyze unstructured data, like negative sentiment detected in emails or recurring complaints in surveys, and combine the types of data to model employee attrition risk effectively.

The key to success here lies in having data architectures that can seamlessly combine both structured and unstructured data in a unified manner, says Kamdar. That is where the true potential lies, he remarks.

Delving further, Kamdar discusses another challenge with unstructured data that boils down to regulatory risk. He affirms that unstructured employee data contains sensitive PII, for which explicit consent for analytics has not been obtained.

Therefore, organizations are exposed to regulatory risks with GDPR and CCPA from the outset, thereby presenting a major challenge. Kamdar argues that even after addressing these issues, driving consent continues to be a significant hurdle, which is much easier said than done.

Concluding, he maintains that apart from regulatory challenges, there is the challenge of integrating unstructured and structured data. While Kamdar notes that there may not be an organization yet that has succeeded in harnessing both data types together, he believes that if one succeeds in doing this, it would be profoundly impactful.

CDO Magazine appreciates Viral Kamdar for sharing his insights with our global community.