Aligning data to business goals was the topic of a recent discussion between Centric Consulting’s Kris Moniz and Wendy Gilbert with the Airlines Reporting Corporation.
Kris Moniz, Centric Consulting’s Director of Data and Analytics, recently interviewed Wendy Gilbert, Director of Data Services for Airlines Reporting Corporation (ARC) for CDO Magazine. ARC provides data, settlement distribution, and financial services to the air travel industry.
Gilbert, an avid traveler with a passion for data, shared her insights on data strategy and governance. In it, she highlights the importance of aligning data initiatives with business goals, maintaining a practical approach to data quality and governance, and leveraging product management techniques to ensure data projects deliver value.
At ARC, data is used both internally to support business decisions and externally for customer-facing data products, but its uses vary drastically by industry and even company. “I’ve worked in retail and healthcare, banking, hospitality, and for big companies and small companies, and that data strategy is so drastically different in every company that I’ve been in because what you’re trying to do with your data is so different,” she shared.
‘The Art of the Possible’
The two touched on generative AI’s recent hype cycle and “the art of the possible,” including using data company leaders may not have considered. It’s critical, Gilbert emphasized, to focus on what the company is trying to accomplish and how data and AI can be used to accomplish those goals.
“For us, it’s being able to make sure that the people that are designing our products, working with our customers to figure out what to do, are educated on what the market offering is when it comes to the art of the possible and how they might be able to use the data,” she said. “What we don’t want is for people to start building products that are built on AI just for the sake of AI or for the new technologies, but making sure that it’s grounded.”
Tiering Data by Criticality
Advocating for a “right-sized” approach to data quality, Gilbert shared that she uses a tiering system based on the criticality of the data. For example, anything financial is top tier while shopping data may be lower because the need to perfect it is lower. “So again, it kind of depends on the company and what you need,” she explained. “But the same for ARC, we’ll have some data that absolutely has to be perfect and some data that’s close enough.”
Gilbert recommended treating data as a product and employing product management techniques to help ensure that data initiatives align with business needs and deliver value. Product management teams can help solve whether data is treated as an internal or external product and make sure you put a business mindset on top of your data.
Moniz and Gilbert also discussed how data itself is not a priority, but the capabilities coming out of the data product are. “Data, AI, BI, data science, ML — all of it is a means to an end,” she said. “Data itself is not a priority; the use cases are.”
View Part I and Part II of the interview.