There’s a lot of talk in the market these days about data clean rooms, along with some confusion about what exactly a data clean room is and how it differs from data sharing methods. In this blog post, I’d like to shed some light on this topic.
As ThoughtSpot’s SVP of Corporate Marketing I oversee a field marketing team that acts as the glue between our Marketing and Field Sales teams. When people talk about field marketing, they’re often just thinking of events — but we have a far broader remit than that. Each member of the Field Marketing team sits within a specific sales region, acting as a kind of regional CMO.
DBTA recently hosted a roundtable webinar with four industry experts on “Unlocking the Value of Cloud Data and Analytics.” Moderated by Stephen Faig, Research Director, Unisphere Research and DBTA, the webinar featured presentations from Progress, Ahana, Reltio, and Unravel. You can see the full 1-hour webinar “Unlocking the Value of Cloud Data and Analytics” below. Here’s a quick recap of what each presentation covered.
I was chatting with Sanjeev Mohan, Principal and Founder of SanjMo Consulting and former Research Vice President at Gartner, about how the emergence of DataOps is changing people’s idea of what “data observability” means. Not in any semantic sense or a definitional war of words, but in terms of what data teams need to stay on top of an increasingly complex modern data stack.
The data pipeline is at the heart of your company’s operations. It allows you to take control of your raw data and use it to generate revenue-driving insights. However, managing all the different types of data pipeline operations (data extractions, transformations, loading into databases, orchestration, monitoring, and more) can be a little daunting. Here, we present the 7 best data pipeline tools of 2022, with pros, cons, and who they are most suitable for. 1. Keboola 2. Stitch 3. Segment 4.
