(US & Canada) | Explainable Data Quality Should Be a Thing Just Like Explainable AI — Genesco, VP and CDO

Deval Motka, VP and CDO at Genesco, speaks with Nazar Labunets, Product Marketing Manager at Ataccama, in a video interview about building sustainable solutions based on governed self-service, AI opportunities in retail, the surge in traditional AI usage, and the data quality challenge with AI.

Genesco is a leading Nashville-based specialty retailer and branded company with nearly 100 years of success selling footwear and accessories.

The final segment of the interview starts with Motka shedding light on building sustainable solutions. She reiterates that a sustainable solution can be repurposed and reused.

Delving further, Motka states that to build sustainable solutions, organizations must have some people who can continuously think of sustainability. In the fast-moving data world, full of siloed solutions, she is a fan of the data modeler, data architect, and data product owner roles.

According to Motka, these roles constantly strive to think about what else can be done with the same solution. The second aspect of sustainability involves AI, which generates on-point solutions for people that sometimes technologies cannot do.

However, technology leaders must keep an eye out for how people are using chatbots and what other things are included in business processes, says Motka. With massive access to AI, sustainability must come into play to make it safe and scalable.

Moving forward, Motka shares how she likes to use the term governance with democratization. Reflecting on data democratization, which led to self-service, she notes that it equally pressed on the need for governance.

Adding on, Motka refers to the term ‘governed self-service’, where governance will exist not to slow down but to be faster in a safer and more scalable manner.

When asked about the AI opportunities in retail, she mentions the creativity aspect of generative AI, which aids in creating personalized and thoughtful content for digital businesses. Motka is excited about the impact it has had on other parts of AI.

Like numerous industries, such as supply chain and security, retail has embraced AI for the long term, she says. This will result in a surge in traditional AI application usage.

Explaining a use case, Motka mentions knowing customer behavior better with simple imputations, which can be done with machine learning and traditional AI. Then, organizations can combine those with generative AI to communicate in the right way with the right number of consumers.

Furthermore, Motka states that the hype of AI is driving the hype of privacy. While people think of privacy in a certain way, there must be a balance between innovating safely and considering data privacy and ethics when used in algorithms.

Additionally, Motka says that while she is excited, she will continue to embrace incrementality in use cases and experiment when needed to be better.

Commenting on the data quality challenge with AI, she states that from a reporting standpoint, data quality is the first thing to worry about. When it comes to the other end of the spectrum with AI, data quality must be conjoined with explainability.

Explainable AI is a thing, and so should be explainable data quality, says Motka. She continues that data quality is not a quality issue but an explainability and understanding issue. In business, it is necessary that people understand everything to be able to trust; otherwise, the data quality will only remain a technical implementation.

Concluding, Motka says that the explainability of data must be made publicly available in a business context for people to comprehend what a good-quality dataset means.

​CDO Magazine appreciates Deval Motka for sharing her insights with our global community.