06 Sep Wrangling Data from the Data Marketplace
ne of data science’s dirty little secrets is the time spent data wrangling, that is accessing the data and then transforming it into a form compatible with your data tools and chosen analytical and learning models. Most agree this can take a full 75-80% of data science time. In this episode of the IoT Inc Business Show, I speak with Adam Mayer about data access and form when shopping at your local datamart.
In this episode of the IoT Business Show, I speak with Adam Mayer about data access and form when shopping at your local datamart.
Adam is a Senior Manager of Technical Product Marketing at Qlik, specifically focused on IoT and GDPR. Adam has over 20 years of B2B customer experience within the IT, Automotive and Manufacturing sectors.
When considering data marketplaces or data exchanges or what I call datamarts, how you get the data and the form it’s in can be as important as the data itself. Is it accessed through a feed, an API or by downloading a spreadsheet? Then, is the data packaged or raw, meaning is it normalized, standardized and with accompanying meta data? With so much time spent data wrangling the answers to these questions can significantly affect your data pipeline workflow.
Here’s What We’ll Cover in this Episode
- Why are datamarts needed?
- The different ways to connect to datamart data
- The different business models to buy datamart data
- Data contracts and smart contracts
- What to look out for with respect to GDPR privacy regulation
Mentioned in this Episode and Other Useful Links
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