Improving data quality is a crucial element of implementing advanced data analytics initiatives like AI. In this interview in advance of CDAO FSI Brazil, Valter Andrade, Data Science Director at Visa discusses how organizations can address their data quality challenges:
What do you think are the most significant challenges organizations face in maintaining data quality, especially with real-time analytics?
"The main challenges involve the maturity level of companies in data handling. There's a dependency on knowledge and proper utilization of data for decision-making. Some organizations have extensive experience with data but need more refined processes to improve results.
The key is establishing governance and metrics to support decision-making, not just in terms of governance and technical processes but also in fostering a data-driven culture. It's crucial to help people understand the benefits of data processes and how they can use them effectively."
What best practices or tips would you suggest to data leaders aiming to improve data quality in their organization?
"Firstly, it's vital to establish effective governance processes. Using specialized tools like can also be beneficial.
We're seeing initiatives in generative AI to aid in areas like identifying missing data. The focus should be on using the right tools for governance and technical support. It's not just about having tools; it's about using them effectively in tandem with governance and data quality processes.
Credibility and accurate communication with clients are crucial. If your data is inaccurate, it impacts client relationships and market credibility."
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How can data leaders nurture a data-conscious environment in their organizations for better data quality?
"It's about knowledge, training, and support. More training, coupled with communication about the impact of data on business outcomes, is key.
A data-driven culture is essential for companies that have vast data but lack a data-oriented mindset. The focus should be on developing a data-driven mindset and ensuring the data's quality for making informed decisions."
What are the risks for organizations if they get data quality wrong?
"The biggest risk is damaging client relationships and losing credibility in the market. Poor data quality can lead to ineffective campaigns and investments, impacting the company's reputation and client relations."
What future developments do you see in improving data quality and their effects on business initiatives?
"The future will see more automation and AI integration in business processes. Tools will evolve to assist in decision-making, analyze data quality, and optimize campaign effectiveness. The focus will be on efficient data storage and quality improvement techniques.
Technologies like generative AI will become more integral, helping to address data quality issues more efficiently. The goal is not to replace human input but to enhance it, making processes more efficient and data driven."
Want to learn more?
Valter will be speaking at CDAO FS&I on February 27th and 28th, 2024 in São Paulo. Join him and many other data and analytics leaders to learn about the latest trends and opportunities in the industry. Register to attend here.
