What Snowflake Can Teach Us About Data Structuring

Photo by Christopher Burns on Unsplash

Over the past week, Snowflake has been all over the news, and not just on sites about technology and software. CNBC and other major news outlets watched the company’s IPO carefully, and it turned out to be a record-breaking first day on the market, with Snowflake’s valuation ending up at $70 billion after its stock soared 112%. Shares dropped the following day, and critics now say that Snowflake lost an opportunity to invest more money in the business itself. Regardless of your opinion on the traditional IPO process and Snowflake’s results, its investors and executives are certainly happy right now.

But why is Snowflake important? What does the company actually do, and what does their success tell us about the future of SaaS?

In the simplest terms, Snowflake provides cloud-based data lakes to move data that is currently in siloes into a single place. They take enterprise data stores and move them into the cloud, where they are no longer siloed and are more accessible. Snowflake has competitors — they are far from the only company providing this service — but they sell their offering as a full service with little work required on the part of the client: DWaaS (Data Warehouse as a Service). Cloudera is the product most often directly compared to Snowflake, with the main difference being that Snowflake runs on Amazon Web Services and Cloudera runs on Hadoop.

Snowflake’s success indicates two important things about the future of SaaS.

The cloud is the norm.

More enterprises are offering their products in SaaS form to satisfy the increasing desire among customers for on-demand product access, more speed, and extreme agility. Customers want 24/7 product access on any device, and they want products to work almost instantly. Companies that are based in the cloud are much more able to deliver on these fronts. As far as security, enterprises are coming to appreciate the huge investments that cloud providers are continually making in data protection efforts. The cloud can offer better security through enhanced authentication techniques and a broad range of continually updated security technologies.

Cloud organization is crucial.

Once data is stored in the cloud, it can be organized so that automation technologies are able to leverage it. The end goal might be AI-powered solutions to business problems, but simply structuring cloud-based data can give businesses access to critical information that was hidden by a mess of siloed data in a variety of inaccessible formats: PDFs, various image files, videos, audio, and more. Once this data is fully structured, it can be used to create more thorough product catalogs, better search capabilities for customers and employees, more accurate inventory management, and more accurate (and therefore speedier) order fulfillment.

What next?

Once datasets have been aggregated into a cloud-based Data Warehouse, companies will need access to resources that can structure that data and make them ready for analysis and the training of AI models. That’s where DSaaS (Data Science as a Service) platforms will become valuable. Companies like CrowdANALYTIX will provide services beyond the cloud migration and storage offered by Snowflake and the like, and help enterprises structure their data, automate their data structuring processes, and ultimately implement AI solutions to increase profits, enhance efficiency, and lower business costs.

This piece first appeared on CrowdANALYTIX.

Writer and Content Manager for CrowdANALYTIX. PhD from UT Austin.