A guide to better data quality
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Learn how to build confidence in the quality of your data
In this white paper, we’ll explore what we mean by ‘data quality’, why it’s fundamental to data projects such as AI and personalization, and how you can collect complete, accurate data.
Download the white paper to learn:
- The importance of well-defined schemas
- How data ownership can influence data quality
- The roles that validation and testing play in improving data quality
“Data quality is an urgent issue, the most time consuming, the most painful and it slows us down the most. We find that it’s important to detect and surface data quality issues, make them easier to visualize and more transparent, not fix them.”
Rahul Jain, Principal Engineering Manager at Omio
The in-the-trenches, getting-hands-dirty groundwork of data collection and building data confidence is a growing concern for most companies’ data teams because it’s the key to building data quality.
Ensuring data quality is an ongoing effort that offers no easy or exact answers. The output of your data analytics projects is only as good as the data input, meaning that the quality of your data matters.
As more complex data use cases – the ‘exciting’ stuff, like AI applications or personalization efforts – become part of day-to-day analytics work, actively managing data quality as a process becomes essential.
Download the white paper to discover how to get data quality with Snowplow and build trust in your data.