Bryson Smith’s Post

Most organizations recognize that data is a multi-persona problem, involving everyone from upstream product engineers and PMs to downstream data scientists, data engineers, and business analysts. Strangely, many don't think about Data Quality in the same way. Data Quality is most often left in the hands of a single persona within the org: the central data platform engineer. The central data team is responsible not only for providing high-quality data to power dozens of downstream use cases - BI dashboards, AI/ML algorithms, and more - but also for enabling partner teams to easily find, organize, and use trustworthy data. But are these the right people to own data quality? While product engineers understand the end user, and BI analysts understand business’ needs, the central team is left in the messy middle - further from end use cases and the core business domain. I believe that by leaving out key stakeholders, i.e. the upstream producers and end consumers of the data, you end up with a Data Quality practice that is fundamentally unsustainable over the long term. In order to consistently produce reliable data, Data Quality must be democratized. Quality context must be made accessible (and editable) to the folks who best understand its semantics. Those who produce and consume it, not just those who broker it. In a recent talk called 'Achieving Sustainable Data Quality with Acryl Observe', I dive deep into this topic, covering the status quo of how organizations tackle the data reliability challenge today and introducing a vision for a more sustainable future. Check it out here 👉

🚀Join us live for our 𝗗𝗮𝘁𝗮𝗛𝘂𝗯 𝗧𝗼𝘄𝗻 𝗛𝗮𝗹𝗹 on 𝘑𝘢𝘯 𝟤𝟧𝘵𝘩 @ 𝟫𝘢𝘮 𝘗𝘛/𝟣𝟤𝘱𝘮 𝘌𝘛! 🚀 Folks, this is a big one: we’ll hear from the Netflix team about their upcoming contributions to the DataHub project, then the KPN team will give us a look at their DataHub adoption journey, and so much more! We'll be LIVE on Zoom and streaming to YouTube!