If you work in martech, marketing operations or related roles, you've surely heard colleagues and leadership complaining about data quality and their lack of trust in data. We often place the blame for data quality on the system, because we're not willing to fully say the quiet part out loud: The No. 1 factor in data quality is the people, the processes and the level of rigor in those processes.
When it comes to market segmentation, I don't see truly well-documented cases often. At a more simplistic level, we think of classic matrices such as BCG or McKinsey's. But the real exercise of segmentation is far more complex. In certain contexts, it comes close to the behavior of a tensor: multiple dimensions, cross-dependencies, distinct weights, temporality, and contextual factors that shift the meaning of data depending on the axis being analyzed.
Databricks and Snowflake are at it again, and the battleground is now SQL-based document parsing. In an intensifying race to dominate enterprise AI workloads with agent-driven automation, Databricks has added SQL-based AI parsing capabilities to its Agent Bricks framework, just days after Snowflake introduced a similar ability inside its Intelligence platform. The new abilities from Snowflake and Databricks are designed to help enterprises analyze unstructured data, preferably using agent-automated SQL, backed by their individual existing technologies, such as Cortex AISQL and Databricks' AI Functions.
So the thing that we think about all day long - and what our focus is at Box - is how much work is changing due to AI. And the vast majority of the impact right now is on workflows involving unstructured data. We've already been able to automate anything that deals with structured data that goes into a database.
Many customers say, 'I don't really have an AI problem, I have a data problem.' They need to prepare their data. Files here, images there, videos elsewhere - they have these legacy platforms that don't support unified access. The challenge becomes quite complex because most enterprise data is unstructured: contracts, invoices, videos, presentations, and it's scattered across different systems. The real value comes from bridging unstructured and structured data.
As with anything, having the correct setup is critical. For AI, that means establishing a robust, centralized data platform - combining both structured and unstructured datasets - so brands can improve the relevance of their communications and enhance customer experiences. Accuracy and governance are fundamental Whether you're setting up a simple customer segmentation or a complex lifetime value model, the core tenets of strong data foundations remain the same.
