There is a concept in software engineering which is called ‘shifting left’, this focuses on testing software a lot earlier in the development lifecycle than you would normally expect it to. This helps teams building the software create better rituals and processes, while also ensuring quality and usability are key aspects to evaluate as the software is being built. We know this works in software development, but what happens when these practices are used when building AI tools?
Saurabh Gupta is a seasoned technology executive and is currently Chief Strategy & Revenue Officer The Modern Data Company. With over 25 years of experience in tech, data and strategy, he has led many strategy and modernization initiatives across industries and disciplines. Through his career, he has worked with various Internation Organizations and NGOs, Public sector and Private sector organizations. Before joining TMDC, he was the Head of Data Strategy & Governance at ThoughtWorks & CDO/Director for Washington DC Gov., where he developed the digital/data modernization strategy for education data. Prior to DCGov he played leadership and strategic roles at organizations including IMF and World Bank where he was responsible for their Data strategy and led the OpenData initiatives. He has also closely worked with African Development Bank, OECD, EuroStat, ECB, UN and FAO as a part of inter-organization working groups on data and development goals. As a part of the taskforce for international data cooperation under the G20 Data Gaps initiative, he chaired the technical working group on data standards and exchange. He also played an advisor role to the African Development Bank on their data democratization efforts under the Africa Information Highway.
In the episode, Adel & Saurabh explore the importance of data quality and how ‘shifting left’ can improve data quality practices, the role of data governance, the emergence of data product managers, operationalizing ‘shift left’ strategies through collaboration and data governance, the challenges faced when implementing data governance, future trends in data quality and governance, and much more.
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