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If you ask anybody, they will tell you that data is valuable.
But what they are likely referring to is data analytics (i.e. reporting, dashboarding, predictive analytics, etc.). This is the output of data. To get to this stage, where data is centralized and made easily available for analysis, you need a data pipeline.
This is where data engineering comes in.
If you’re like most companies, you’re collecting large volumes of data from a variety of data sources across different business units. What we typically see from the companies we work with
For us, the most important consideration was scale. We are powering billions and billions of customer interactions, so having a partner like Snowflake who can support that scale was key.
There are mature technologies and there are many that are in the process of maturing that are I think have high potential. If you look at Cohere’s APIs for example or OpenAI’s APIs, there’s some exciting indications of the future of an generative models and natural language generation more broadly.