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How to Generate Customer Success Analytics in Snowflake

As the distinction between data professionals and non-data professionals becomes smaller and smaller, the need for technology that bridges the gap between the two parties is crucial. The benefits of interacting with a data warehouse, especially with large amounts of data, are unquestionable, but as a peripheral member of the core technology team who might not be very technical, it is not always practical to generate SQL queries on the fly. 

This poses a problem, especially when departments such as sales, customer success, account management, etc., want the robust insights that could come from the vast amount of data that a company is storing, but they don’t necessarily know how to quickly gather these insights. 

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Shareable Data Analyses Using Templates

Photo by Joanna Kosinska / Unsplash

Our friend Benn Stancil recently wrote a great post about templates—his term for sharable, pre-built dashboards and reports. Do yourself a favor and read it. The basic idea is that shared, reusable analyses for data has been a pipe dream for years and aren't yet on their way:

Even though our data is the same, and our companies are the same, there’s no one-click way to spin out an entire suite of dashboards

Templates do seem inevitable: the concept of reusable code is something software developers have relied on for literally decades. It's fundamental to how all software is built. The data community has been borrowing best practices from the software world since the beginning, from version control in Git to staging environments to testing. But we still can't use their single most powerful technique.

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Adopting DataOps for Agile Data Management Processes

As businesses become AI-ready, efficient data management has acquired an unprecedented role in ensuring their success. Bottlenecks in the data pipeline can cause massive revenue loss while having a negative impact on reputation and brand value. Consequently, there’s a growing need for agility and resilience in data preparation, analysis, and implementation.

On the one hand, data-analytics teams extract value from incoming data, preparing and organizing it for the production cycle. On the other, they facilitate feedback loops that enable continuous integration and deployment (CI/CD) of new ideas.

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3 Takeaways From the 2019 Gartner Market Guide for Data Prep

Gartner has recently released its 2019 Market Guide for Data Preparation ([1]), its fourth edition of a guide that was first published in the early days of the market, back in 2015 when Data Preparation was mostly intended to support self-service uses cases. Compared to Magic Quadrants, the Market Guide series generally covers early, mature, or smaller markets, with less detailed information about competitive positioning between vendors, but more information about the market itself and how it evolves over time.

While everyone's priority with these kinds of documents might be to check the vendor profiles (where you'll find Talend Data Preparation listed with a detailed profile), I would recommend focussing on the thought leadership and market analysis that the report provides. Customers should consider the commentary delivered by the authors, Ehtisham Zaidi and Sharat Menon, on how to successfully expand the reach and value of Data Preparation within their organization.