If you work in data, these questions are probably a common occurrence:

“What happened to my dashboard?”

“Why is that table missing?”

“Who in the world changed the file type from CVS to XLS?!”

And these just scratch the surface. As the number of data sources and complexity of data pipelines increase, data issues are an all-too-common reality, distracting data engineers, data scientists, and data analysts from working on projects that actually move the needle.

In fact, companies spend upwards of $15 million annually tackling data downtime, in other words, periods of time where data is missing, broken, or otherwise erroneous, and 1 in 5 companies have lost a customer due to incomplete or inaccurate data.

So, how do you prevent broken data pipelines and eliminate downtime? The answer lies in traditional approaches to reliable software engineering.

#data #data-analysis #data-science #data-engineering #data-quality