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This is just some announcement which people will want to pay attention to! Learn More

What Snowflake Can Teach Us About Data Structuring

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.

A couple of decades ago, storing data in the cloud was an interesting alternative to traditional on-premise data lakes. But the widespread adoption of technologies like those offered by Snowflake signals that cloud-based data lakes are becoming universally accepted among enterprises. Companies are beginning to recognize the advantages of cloud-based data, which provide better security and better accessibility. 

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. 

As more enterprises move to the cloud, this step is becoming widely recognized as the first in the process of digital transformation. Moving to the cloud certainly benefits companies by enabling them to offer customers speed, agility, and security, but it also sets enterprises up for further digital transformations that can reduce spend and make processes far more efficient, saving on costs. 

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?

Snowflake’s success as a DWaaS company heralds a mass migration to cloud-based data lakes for enterprises of all sizes. Because the second step in digital transformation is data structuring, the move to the cloud will increase demand for these structuring services as companies hope to take advantage of their data stores through analysis. Before any analysis of enterprise data can be accomplished, a lot of data preparation efforts are required to normalize and clean data. Even more important, companies will require feature engineering and data structuring for different use cases, so that they can ultimately build predictive models and other AI solutions which will leverage their data to enhance business processes and improve decision making. 

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.

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