In today’s digital world, businesses generate vast amounts of data. But simply having data isn’t enough. To unlock its true potential and generate tangible outcomes, that data needs to be transformed into 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬 that serve users, solve problems, and 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐦𝐞𝐚𝐬𝐮𝐫𝐚𝐛𝐥𝐞 𝐯𝐚𝐥𝐮𝐞.
This is precisely where the 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐦𝐢𝐧𝐝𝐬𝐞𝐭 becomes not just beneficial, but absolutely essential.
We are operating with a “traditional mindset” that often leads to:
❌ 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞-𝐟𝐨𝐜𝐮𝐬𝐞𝐝 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐩𝐫𝐨𝐝𝐮𝐜𝐭-𝐟𝐨𝐜𝐮𝐬𝐞𝐝: We celebrate the pipeline’s completion, not the value it creates.
❌ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲, 𝐧𝐨𝐭 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲: Once the data is “delivered,” the project’s over, and we move on.
❌ 𝐎𝐧𝐞-𝐨𝐟𝐟 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭, 𝐧𝐨𝐭 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩: We build it once and rarely revisit it for improvements or maintenance.
❌ 𝐃𝐚𝐭𝐚 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬 𝐚𝐬 “𝐬𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬,” 𝐧𝐨𝐭 “𝐮𝐬𝐞𝐫𝐬”: We forget that the people consuming our data are just like the users of any other product — they have needs, pain points, and expectations.
❌ “𝐃𝐚𝐭𝐚 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲 = 𝐬𝐮𝐜𝐜𝐞𝐬𝐬,” 𝐧𝐨𝐭 “𝐨𝐮𝐭𝐜𝐨𝐦𝐞 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 = 𝐬𝐮𝐜𝐜𝐞𝐬𝐬”: We tick a box when data is delivered, regardless of whether it’s actually adopted and making an impact.
❌ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐬 𝐚𝐧 𝐚𝐟𝐭𝐞𝐫𝐭𝐡𝐨𝐮𝐠𝐡𝐭: We often bolt on governance rather than embedding it from the start.
𝑨 𝒅𝒂𝒕𝒂 𝒑𝒓𝒐𝒅𝒖𝒄𝒕 𝒊𝒔 𝒏𝒐𝒕 𝒂 𝒅𝒂𝒕𝒂𝒔𝒆𝒕 — 𝒊𝒕’𝒔 𝒂𝒏 𝒐𝒖𝒕𝒄𝒐𝒎𝒆-𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒏𝒈, 𝒖𝒔𝒆𝒓-𝒐𝒓𝒊𝒆𝒏𝒕𝒆𝒅 𝒂𝒔𝒔𝒆𝒕.
Embracing a data product engineering mindset means adopting key principles that shift our approach.
✅ 𝐎𝐮𝐭𝐜𝐨𝐦𝐞-𝐟𝐨𝐜𝐮𝐬𝐞𝐝 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠: Build with the end in mind. Clearly define what value this product delivers and to whom.
✅ 𝐔𝐬𝐞𝐫-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 𝐝𝐞𝐬𝐢𝐠𝐧: Treat data consumers as genuine users. Deeply understand their needs, pain points, and how they will interact with the product.
✅ 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲: Deliver in small, manageable increments. This enables rapid feedback loops and agile adjustments based on user needs.
✅ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐛𝐲 𝐝𝐞𝐬𝐢𝐠𝐧: Embed governance from the very start. This ensures data products are not only usable but also secure, compliant, and trustworthy.
✅ 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐦𝐢𝐧𝐝𝐬𝐞𝐭: Treat data products as you would with software products. Apply rigorous engineering principles like testing, versioning, and continuous integration/delivery (CICD).
✅ 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠: Build data products on a robust platform that supports scalability, reusability, and interoperability.
This article was originally published at https://medium.com/@aradsouza/data-product-engineering-core-principles-8366b71eda04