- Look at behavioral data specifically, with a focus on web and mobile
- Explore the challenges around data quality, and describe why data quality is fundamentally a people rather than technology problem
- Examine some of the approaches Snowplow and Tasman Analytics have seen companies adopt to build assurance in the accuracy and completeness of their behavioral data
- Explore how the organisational structure of a data team impacts data quality
The road to better data quality:
How to build assurance in every stage of your data pipeline
In the last 10 years behavioral data, including data from web, mobile, connected devices and wearables has been leveraged in more and more use cases. Different teams across the business need this data to understand and tailor experiences to smaller segments of users and individuals, and using new techniques like AI.
This growth in usage and importance of behavioral data means that companies have stricter requirements on the quality - the accuracy and completeness of that data, than ever before. Delivering on that quality is hard, because it requires a concerted effort from everyone involved in the data production pipeline.
Co-founder and CPO at Snowplow
Co-founder at Tasman Analytics
London / GMT+1