“Get the data into your warehouse” with Fivetran and Segment.
“Transform the data to be useful” with Snowflake and dbt.
“Analyze the data” with Amplitude and Mode.
“Get the data out into other platforms with Census, where it can be used and utilized to add value to the business.” (View Highlight)
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Note: There is something more at the “analyze the data” step. Something about modeling and forecasting.
“data quality monitoring and meta analytics on your data.” Addressing the data quality functionality is one of the next big challenges for data teams. (View Highlight)
Data teams have long had a core responsibility of managing data infrastructure. But, Jamie says, as access to data spreads around the organization, a new responsibility will emerge that manages how data stakeholders interact with that infrastructure. (View Highlight)
data leaders sometimes focus too much on the data quality and availability challenge. “People running data teams are thinking more about data quality and ‘Do I have all the data?” and not so much about ‘Are we providing maximum value to other functions in the org?’” (View Highlight)
