To be sure that your data-driven business practices aren’t based on incomplete or corrupt data, it’s crucial to establish data reliability. Masthead’s integration with Looker addresses this challenge by helping you identify data anomalies impacting Looker data products and tracing any issues to their source.
According to 6 Sense, Looker is one of the most widely used data analytics platforms in the world, with a 2.27% share of the global data analytics market. Looker allows users to effortlessly access, analyze, and share data across their organizations and can generate dynamic reports with business-related insights based on data from multiple sources. Looker reports can be interconnected, delivering data to dynamic and interactive dashboards that display business-critical information such as financial projections, performance statistics, and customer satisfaction rates.
Just like any other data analytics tool, Looker very much depends on reliable data. If some data behind your Looker data product is corrupted or missing, errors will emerge.
According to a report from Google Cloud, the most common Looker error messages received by users are:
At Masthead, we have also observed dozens of other errors that happen behind the scenes and affect Looker reports and dashboards. The most common errors our clients encounter are:
If you see such a message (or any other Looker error notification), then something went wrong and you can no longer rely on the Looker data product to which the error refers.
In an even worse scenario, a data error, such as an incorrect table value, can go undetected and won’t prevent Looker from generating a report. As a result, Looker will generate a report or a dashboard with analytical insights based on incorrect data, leading to poor decision-making. This is the kind of situation that you need to avoid at all costs.
Why do data errors occur? To address this situation, we should clarify how good data goes bad.
Companies that embrace data-driven practices take data from hundreds of internal and external sources. They build extensive networks of data warehouses, data lakes, and data pipelines that may be challenging to map out. The problem is that all these sources may change unexpectedly and without notice, pipelines may break, and data can be altered while moving down data streams. As a result, Looker receives compromised and inaccurate data, which causes errors that prevent it from generating analytical data products or, what may be even worse, results in Looker generating data products based on bad data.
This problem is challenging to control without the observability of data streams and dependencies between various data assets. With the growing importance of data, companies require more and more employees to work with data, which means that their data architectures become more expansive and complex, creating new dependencies that may be challenging to track. In such conditions, even a small data deviation can cause the breakdown of complex data systems.
Masthead’s Looker integration allows you to quickly identify Looker data products affected by detected data anomalies. No matter how complex your data architecture is, you can easily get complete visibility of your data dependencies with data lineage by visualizing your data storage, reports, and pipelines. This enables you to track down the root cause of a problem as quickly and conveniently as possible. You can also thoroughly review all your Looker data products and connections among them with Masthead’s dictionary for Looker assets.
Check out Masthead’s changelog to learn how to configure our Looker integration and understand the main benefits it can bring you!
Post Tags :
Data Culture, Looker