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Running an eCommerce store is very much like flying a plane - you can reach unprecedented heights, but you won't be able to do it blindfolded. You have to see where you are going to touch the skies. E-commerce analytics gives you the guidance to make the right choice and scale your online store to new heights. In this article, we will take a deep dive into Shopify Analytics Shopify offers analytics as an out-of-the-box default service to all Shopify store owners and admins.

Ask any analyst how they spend the majority of their work day and they’ll tell you: Performing remedial tasks that provide no analytics value. 92% of data workers report that their time is being siphoned away performing operational tasks outside of their roles. Data teams waste an inordinate amount of time maintaining the delicate data-to-dashboards pipelines they’ve created, leaving only 50% of their time to actually analyze data.

Data paves the way for every strategic move made by banks and insurance companies. Whether looking to create a new service, complying with regulations, or overhauling and re-engineering legacy operations, a massive data project is always central to the effort. For financial services businesses, the pace at which they can reshape and repurpose data has become a key determinant of their ability to predict market trends and meet client expectations.

We’ve upgraded our capabilities for tracking indirect integrations in Databox. All Zapier, Integromat, and Custom Token connections now enable Custom Date Ranges.

Heroku's Postgres open-source database is the most effective service for developers who want to build engaging apps. With all of its features and Heroku's experience managing databases, it guarantees security and compliance with GDPR.

Last week in our BigQuery Reference Guide series, we spoke about the BigQuery resource hierarchy - specifically digging into project and dataset structures. This week, we’re going one level deeper and talking through some of the resources within datasets. In this post, we’ll talk through the different types of tables available inside of BigQuery, and how to leverage routines for data transformation.

Data processors handle an abundance of data — including personal information about individuals. As the collection and use of data become more widespread, governments continue to enact laws that protect personally identifiable information (PII). Failing to comply with such laws means risking serious fines and penalties, and damaging public trust. Masking PII through pseudonymization is one way to protect it.