Santona Tuli, head of data at Upsolver and a specialist in data culture, shares her insights on defining roles in the data field. She dives into the distinction between productivity and performance, emphasizing purposeful goal alignment. Tuli critiques the current stagnation in data management, urging a focus on outcomes rather than mere outputs. She also discusses innovative practices in data architecture and the evolving landscape of data warehousing, highlighting the importance of collaboration and curiosity in driving impactful results.
Santona Tuli emphasizes that productivity and performance are different, stressing the importance of valuing impactful work over mere task completion.
The podcast clarifies the distinction between data analysts and analytics engineers, highlighting the broader role of analytics engineers in product development.
The discussion advocates for embracing innovation through defined business outcomes while adapting to changing data management practices and technologies.
Deep dives
The Importance of Productivity vs. Performance
Productivity is often mistaken for performance, but they are distinct concepts. Productivity relates to the amount of work done, while performance focuses on the value and impact of that work. An example given is the construction of an elaborate engineering infrastructure to address a simple query; if that query could be resolved using a more straightforward approach, then the effort put forth does not equate to valuable productivity. The conversation suggests a shift in focus towards achieving outcomes that directly contribute to the organization's goals rather than merely completing tasks.
Evolving Roles in Data Management
The distinction in roles within data management is emphasized, particularly between data analysts and analytics engineers. The title 'data analyst' often conveys a sense of working solely with Excel or generating reports, whereas 'analytics engineer' better encapsulates the encompassing role that integrates analytics into product development. Furthermore, it is noted that the definition of productivity will differ among data professionals depending on whether they are in data-centric organizations, like Netflix or Amazon, or companies where analytics are supplemental to core operations. The complexity of defining roles and expectations is illustrated by the variability in how effectiveness and productivity are perceived across different companies.
Innovation as a Byproduct of Outcomes
Innovation should not be seen as a distraction but rather as a potential outcome of focused work directed toward business goals. The discussion acknowledges the necessity for teams to prioritize real outcomes that enhance productivity while encouraging an environment where innovation can flourish. As teams grapple with emerging tools and technologies, the impact of innovation may often be evaluated against the backdrop of the specific business contexts and needs. Ultimately, effective innovation is framed not as an ancillary task, but as deeply integrated into the strategic efforts aimed at generating value.
The Future of Data Architecture
Current discussions in data management center around the evolution towards open table formats and their implications for data architecture. Open table formats serve as a bridge between raw data storage and organized querying, allowing for improved accessibility and efficiency. The conversation raises the prospect of a 'queryable index,' positing that better file organization can lead to faster query times and more user-friendly data lakes. This shift is viewed positively, suggesting that improved data architecture will ultimately facilitate better outcomes for organizations by making data more manageable and actionable.
Source of Innovation and Historical Perspectives
The origins of technological and conceptual innovations often stem from surprising sources, which is illustrated through references to historical advancements. The character of data itself, coupled with ongoing developments in data management practices, reflects a need to continuously adapt and re-evaluate what constitutes effective data usage. The conversation highlights that many ideas in data have been explored multiple times throughout history, and the importance of learning from those experiences is emphasized to prevent repeating past mistakes. As the landscape evolves with the introduction of technologies such as generative AI, the interplay between old and new ideas will be crucial for driving future innovation.
Navigating the Shift in Data Roles and Expectations
A significant point of discussion is the perception that certain data roles may become bottlenecks if not aligned with clearly communicated metrics that define success. Data engineers, for instance, may face pressures to deliver more outputs without a corresponding focus on the outcomes those outputs enable. The disparity in turnaround time for various roles such as data scientists and data analysts versus data engineers is highlighted as an important factor contributing to this challenge. Ultimately, fostering open communication about expectations and the realities of data work may promote more equitable workflows across data teams.
Santona Tuli is the head of data at Upsolver. She's a voice we like to listen to about how data teams assemble: data roles, data culture, the function of data at a company...She stops by Catalog & Cocktails to talk workflows, architecture, and looking at data teams with a 10,000-foot view.
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