data is the driver for just about every modern Business, and as companies consume more data more intelligently, there’s a need for a better...
Data is the driver for just about every modern Business, and as companiesconsume more datamore intelligently, there’s a need for a better community and higher buy-in. DataOps stands to do to data what DevOps did to development.
Changing to DataOps isn’t just DevOps though. Whole manifestos center around getting businesses ready to switch the DataOps, but some pitfalls still existand many businesses fail. Let’s take a look at five common dataops mistakes you could be making and how that could be affecting your success.
DataOps isn’t just DevOps applied to data analytics. If you’re using DevOps to iterate and reduce the lifecycle of your data modeling, you’re missing the point.
DataOps has a critical component with data pipelines. Think of your system as a factory: Data comes in and is processed through the pipeline set out by the analytics side of DataOps, Agile Development uses that data to accelerate product development, and DevOps continually innovates the algorithms that handle the data pipeline. If you’re stuck on DevOps, you’re missing the other two critical components.
Here’s what to do: Adopt thethree core branchesof DataOps andsign the manifesto.
Speaking of treating DataOps like DevOps,collaboration is even more criticalwhen it comes to the data pipeline. Making a move to DataOps requires the cooperation and buy-in of not just your data team, but other departments in your business and significant buy-in from appropriate stakeholders.
Without proper collaboration, it can take months to move through your data pipeline and into production. Businesses have to give teams the freedom to collaborate and execute processes themselves. Otherwise, you’re “performing” DataOpsin a waterfall environment.
Here’s what to do:Bend your silos.
Your data scientistsneed unprecedented accessto data that might have been labeled irrelevant ten years ago to produce the kind of scalable solutions you’re looking for. If you’re dealing in AI, this is even more critical as unsupervised and semisupervised systems need massive amounts of data for training sets.
