Synopsis: In this Techstrong.ai video interview, Terren Peterson, vice president of data engineering for Capital One, dives into the data management challenges that organizations need to overcome as they look to build and deploy artificial intelligence (AI) applications in the wake of a survey that finds 87% of business leaders see their data ecosystem as ready to build and deploy AI at scale, while 70% of technical practitioners spend hours daily fixing data issues.
In this interview, host Mike Vizard speaks with Terren Peterson, VP of data engineering at Capital One, about a new report assessing how ready enterprises are for AI. Peterson highlights that a significant majority of business leaders—around 80%—recognize the strategic importance of AI and ML. However, the report also reveals that many technology practitioners face substantial challenges with data quality, spending too much time on data wrangling and pipeline fixes rather than on higher-value adatanalytics and customer insights.
Peterson emphasizes that successful AI adoption hinges on robust data management practices. At Capital One, dedicated data product managers play a key role in organizing data through effective ontology and metadata enrichment. This proactive approach ensures that when new AI projects roll out, data is already well-curated, allowing analysts and data scientists to focus on extracting actionable insights rather than remedying data issues. The discussion also touches on the shift from traditional batch processing to real-time, event-driven architectures needed to manage the growing volume and variety of both structured and unstructured data.
Concluding with actionable advice, Peterson urges organizations to view the report not as a source of overwhelm but as a call to action—it’s never too late to invest in better data practices. He advises companies to start with immediate steps, such as enhancing metadata and breaking down data silos, to build a resilient data platform that will drive future AI initiatives. This focus on data quality and agile infrastructure is essential for realizing true return on investment, especially as businesses prepare to scale AI capabilities in the coming years.
