The Rise of DataOps ??
Prukalpa ?
Co-Founder at Atlan –?Home for Data Teams | Forbes30 & Fortune40 lists | TED Speaker
Let’s face it —?traditional data management doesn’t work. Today, 75% of executives don’t trust their own data, and only 27% of data projects are successful. Those are dismal numbers in what has been called the “golden age of data”.
DataOps promises to solve this problem, and it’s on fire right now. In the last couple of months, Forrester and Gartner recently made major shifts toward recognizing the importance of DataOps with their?Wave report ?and?Hype Cycle .
But the rise of DataOps isn’t just coming from analysts. At Atlan, we work with modern data teams around the world. I’ve personally seen DataOps go from an unknown to a must-have, and some companies have even built entire strategies, functions, and even roles around DataOps. While the results vary, I’ve seen incredible improvements in data teams’ agility, speed, and outcomes.
Let’s dive into everything you should know about DataOps. Happy reading!
??Spotlight: The Rise of DataOps
What is DataOps?
The first, and perhaps most important, thing to know about DataOps is that it’s not a product. It’s not a tool. In fact, it’s not anything you can buy, and anyone trying to tell you otherwise is trying to trick you.
Instead, DataOps is a mindset or a culture — a way to help data teams and people work together better.
There’s no standard definition for DataOps. However, you’ll see that everyone talks about DataOps in terms of being beyond tech or tools. Instead, they focus on terms like communication, collaboration, integration, experience, and cooperation.
In our mind, DataOps is really about bringing together today’s increasingly diverse data teams and helping them work across equally diverse tools and processes. Its principles and processes help teams drive?better data management, save time, and reduce wasted effort.
The four fundamental ideas behind DataOps
Some people like to say that data teams are just like software teams, and they try to apply software principles directly to data work. But the reality is that they couldn’t be more different.
In software, you have some level of control over the code you work with. After all, a human somewhere is writing it. But in a data team, you often can’t control your data, because it comes from diverse source systems in a variety of constantly changing formats.
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The way we like to think about DataOps is, how can we take the best learnings from other teams and apply them to help data teams work together better? DataOps combines the best parts of Lean, Product Thinking, Agile, and DevOps, and applies them to the field of data management.
How do you actually implement DataOps?
Every other domain today has a focused enablement function. For example, SalesOps and Sales Enablement focus on improving productivity, ramp time, and success for a sales team. DevOps and Developer Productivity Engineering teams are focused on improving collaboration between software teams and productivity for developers.
Why don’t we have a similar function for data teams? DataOps is the answer. Here are the three key steps to implementing it:
???More from my reading list
I’ve also added some more resources to my data stack reading list. If you haven’t checked out the list yet, you can find and bookmark it?here .
See you next week!
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