How to Optimize DQM Implementation in your Snowflake Warehouse

Logo
Presented by

Amit Bhattacharyya, Vox | Amy Reams, Anomalo | Keith Smith, Snowflake | Tall Kanfi, Anomalo

About this talk

The new demands of digital-first strategies have pushed many companies to adopt new data platforms, in order to keep operations running smoothly. Snowflake is one of the most popular choices in the market, but many users are only just scratching the surface of its potential. Despite the best of intentions, McKinsey reports that less than 30% of digital transformation strategies are successfully implemented. Without the right support in place, a majority of companies are struggling to get a return on their technical investment. This doesn’t need to be the case — and certainly not with your Snowflake warehouse. DQM implementation can be tricky but there are a few key ways to optimize the rollout. The right third-party solutions are designed not just to work within Snowflake, but to maximize the performance of both products. Partnering with compatible services will allow you to unlock the next level of your data insights. Tune into this second installment of Anomalo’s 3-part series, ““Producing Quality from Quantity: Making Data Quality Management Work for You,” for a masterclass in Snowflake optimization. Our expert panel will dive into: — The most common mistakes companies are making with DQM in Snowflake — How to take your implementation from functional to optimal — Why Anomalo is the perfect partner for Snowflake customers Speakers: Amy Reams, Vice President of Business Development at Anomalo Keith Smith, Senior Partner Sales Engineer at Snowflake Tall Kanfi, Data Solutions Architect at Anomalo If you'd like to learn more about the partnership and meet members of the Anomalo team, you can register for The Snowflake Summit at the link below and visit Anomalo at booth 914: https://www.snowflake.com/summit/ https://www.snowflake.com/summit/

Related topics:

More from this channel

Upcoming talks (0)
On-demand talks (3)
Subscribers (292)
Anomalo data quality management automatically detects data issues and understands their root causes so you can quickly and efficiently solve them.