ETR Insights presents an interview with the VP of Business Intelligence, who centralized all data under Snowflake and leverages Azure for Infrastructure-as-a-Service and AI/ML. This company treats data as a direct business line where its CEO personally drives the company’s analytics initiatives. Read on to learn why DataMirror is a more efficient way to clean data within Salesforce, how Databricks’ ability to engage front-line data workers is supercharging the vendor's growth, and much more.
Vendor Specific Commentary
Snowflake + Salesforce (and Tableau). Our guest spoke about the transformation initiative they embarked on in 2021 to get their company's data into the cloud. “Historically, we were all Microsoft. Our cash management and portfolio management, everything ran on the Microsoft stack.” Snowflake now serves as the central hub for all data, including cloud data from Salesforce and on-premise apps that run on Microsoft. The company uses the full Salesforce stack for its CRM, as well as Marketing Cloud and Tableau. This organization chose Snowflake over Azure Synapse for data warehousing due to its analytics focus and a lack of internal Azure DevOps, and takes advantage of Salesforce-Snowflake integrations. “If we were running on Power BI, that probably would have changed things dramatically. But because we don't run Power BI here, we opted to go the Snowflake route.”
Tableau. The organization previously explored Power BI, but chose Tableau as their primary tool for BI. Our guest explained that it made more sense for their small team to stick to one vendor and focus on developing expertise in one tool. “We don't want people to just be proficient, we want experts.” This company centralizes its BI, analytics, and data science functions under the CEO, who, alongside our guest, often directly drives analytics initiatives. “We treat analytics as a line of business. We're usually attached either driving initiatives from a data perspective, or we're working hand-in-hand with the line of businesses to help them.”
ETR Data: This ETR data visualization depicts the best-positioned analytics and BI tools within Snowflakes Data Warehousing respondents. Not surprisingly, Snowflake’s own Streamlit is seeing the highest shared Net Score among the group. Salesforce’s Tableau is losing spending momentum as compared to year-ago and prior-survey levels, while ThoughtSpot is gaining relational share in this analysis over the same time frame, a trend we will continue to monitor. Google’s Looker lands in the third best-positioned placement. Meanwhile, Qlik and Domo remain mired in negative Net Score levels among Snowflake users.
Alteryx v. DataMirror. The company uses Azure Data Factory to pipe its data into Salesforce; for data cleaning, Alteryx and Tableau Prep have been replaced by DataMirror, which sites right on top of Snowflake. “The idea of pulling data out of our data warehouse into a BI or ETL tool, only to then put it back in our data warehouse, just didn't resonate with me. We spent all this time to get everything in Snowflake.” The company still relies heavily on Tableau, but they use DataMirror to create a semantic layer in which 90% of views are based on their raw or curated data. “DataMirror has been around. I think their roots were more Hadoop, and then they pivoted to go all-in on Snowflake.”
Alteryx was previously useful when dealing with a mixture of cloud, Salesforce, and on-premise data in SQL Server, but once the company moved to Snowflake, our guest struggled with the cost of Alteryx’s desktop tool. Alteryx’s focus on enterprise makes them cost-prohibitive for small and medium-sized businesses, leading our guest to DataMirror. “The mixture of pulling data out of Snowflake, transforming it, and then pushing it back to Snowflake, and then it being expensive to do, that's where I said to myself, there's got to be something else out there that will let me do data preparation natively.”
Azure, Databricks, and Dataiku for AI/ML. This organization favors Azure for machine learning, primarily to build models for lead scoring and integrate them into Salesforce. Azure pricing is favorable. Our guest found Databricks to be a more robust but less of a self-service solution that required a higher level of DevOps competency. “With Azure, I was able to get my data scientists, plug them in, and let them start working with the team, and we were able to go from training to deployment in a couple of weeks.” This organization also praises Dataiku’s ability to allow data scientists and analysts to work in the same space and switch between the two without the need for coding skills. However, our guest adds praise for Databricks, suggesting that the vendor is growing so quickly thanks to its effective go-to-market strategy and ability to engage front-line data workers, scientists, and analysts with its platform. “To some extent, some of our guys got a little bit of an education from Databricks on best practices, on frankly doing their job.” By contrast, Azure ML requires bringing in an ISV for support. Databricks provides ample materials to get users engaged, acclimated, and running.
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