Just a quick note – I just found out SqlDBM has featured me on their new advisors page. Check it out.
Also wanted to remind everyone that I will be giving a webinar next week, August 17th on Data Modeling for Snowflake, Sign up here for that. It will be fun for sure.
A few weeks back, right after attending Snowflake Summit, I had the pleasure of chatting with Sanjeev Mohan on his podcast – It Depends. We spent an hour or so chatting about trends in modern data management, data mesh, data vault, my now semi-retired life as an advisor to several Snowflake partners, and my love of martial arts.
Please join me on March 30th at 11AM CDT for my first official post-retirement talk!
I will be chatting with Mike Lampa from Great Data Minds about my career and plans post-Snowflake. I expect we will cover a wide range of topics including what community and technical evangelism is all about, as well as what made me jump from nearly 30 years in the Oracle world to a small startup with less than 100 people that claimed to have re-invented data warehousing in the cloud.
Shortly before leaving Snowflake last year, I was interviewed for this post about one of the worst case examples of data siloes I had seen – we called them data puddles!
A few years ago, Kent Graziano joined a big organization to work on its data. The first problem was that nobody really knew what and where all the data was. Graziano took his first three months on the job investigating data sources and targets, ultimately creating an enterprise data map to illustrate all the flows. It wasn’t pretty.
“In the end, I discovered that the same data was being sent to three or four places,” he said. In one case raw data was transformed and stored in a data warehouse, then moved from there into another warehouse—which was also pulling in the original raw data.
Graziano, who recently retired from his post as Chief Technical Evangelist at Snowflake, said this scenario is entirely common. Data scattered and copied in lakes, warehouses, data marts, SaaS platforms, spreadsheets, test systems, and more. That’s mass data fragmentation, or, more colloquially, data sprawl or data puddles.
Indeed, 75% of organizations do not have a complete architecture in place to manage an end-to-end set of data activities including integration, access, governance, and protection, according to IDC’s State of the CDO research, December 2021. This lack of governance combines with legacy systems, shadow IT, and good intentions to pave the road to a lot of fragmentation.
Check out the rest of the post to learn how data sprawl hurts businesses and what to do about it. Read it all here!
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The Data Warrior 
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