What is DataOps?
What is DataOps? You’ve heard the term. Is it marketing-speak for old processes, or something new and important? In this post, we answer the question what is DataOps to help you understand how it can benefit your company.
Data Operations (or DataOps) controls the flow of data from source to value, accelerating time to value. What began as best practices when handling data, DataOps is now emerging to become its own field.
So, What Is DataOps Exactly?
The word “Operations” brings to mind efficiency, automation, reliability, and scale. At its very core, the idea of operations is to take a functioning unit and make it scale.
We believe that the essence of DataOps lies in scalability and repeatability.
At Nexla, we define DataOps as an organization-wide data management practice. It controls the flow of data from source to value, with the goal of accelerating time to value. The outcome is scalable, repeatable, and predictable data flows for data engineers, data scientists, and business users. DataOps is as much about people as it is about tools and processes.
A DataOps practice can open data access to more stakeholders within an organization. Tactically speaking, DataOps takes care of the grunt work placed on IT or data engineers. This includes integrating with data sources, performing transformations, and delivering data. DataOps also encompasses the monitoring and governance of these data flows.
DataOps: Operations for the Machine Learning Age
Ops roles for infrastructure have been common starting with Network Operations in the 1960s. Security Ops, and more recently DevOps, is helping companies ship better software faster. Yet, when it comes to data, the stakeholders that care about and interact with data in a company are far broader.
Another example of Ops success is AdOps at companies like Google and Facebook. Advertising Operations (AdOps) specialists ensure ad campaigns are delivering results for large advertisers. AdOps managers maximize campaign performance and troubleshoot issues. They are not data engineers, but they must process data nonetheless. Because tools have been built to help scale their work, they are able to solve problems before they land on product or engineering’s plate. Online advertising could not scale without the AdOps function.
At Nexla we believe that restricting DataOps to the purview of the engineering team or data team can be a narrow view. The more “data leverage” you can create in an organization, the more likely you are to be successful.
DataOps is not about tools and processes. It represents a greater cultural shift that breaks down the silos between data producers and consumers. Data producers are the data engineers. Data consumers are the analysts or business users of data. DataOps is as much about people as it is about processes.
The Current State of DataOps
DataOps is no longer a tech trend, but a business trend.
Every year, Nexla publishes the benchmark Definitive Data Operations Report. With an increase in machine learning, AI, and real-time streaming DataOps isn’t going anywhere. Companies are beginning to realize the need for data operations to maximize their resources.
As a growing field, 73% of companies surveyed reported plans to hire in the DataOps function within the next year. This is up from 70% last year. DataOps is no longer a tech trend, but a business trend. That’s why more and more data professionals are asking, what is DataOps?
To better understand what is DataOps, consider these insights
Why the demand for data operations? Machine learning, AI, real-time streaming
85% of companies report that their company is working on machine learning and AI — that’s up from 70% in 2017
83% of companies say they will do even more in machine learning and AI next year
To feed models more data, 85% of companies are ingesting from third-party partners. The data operations survey found 54% of companies are ingesting from more than 10 partners
Real-time streaming data is critical to these efforts. 58% of companies ingest data real-time
Data teams need data operations to capitalize on big data opportunity
50% of respondents reported they don’t have enough backend data engineers
The average company only has one data engineer for every five business users. It’s processing 2.7 GB of new data a day, and manages 4,300 data sets
To keep up with demand, 73% of companies report they have plans to hire DataOps professionals
Overworked data teams want automation — cue data operations
Data engineers spend 18% of their time troubleshooting — on average, a company loses 180 hours a week to troubleshooting. That’s a waste of valuable data resources
Data pros see an opportunity for automation throughout their work. 56% think data clean-up, 47% think analytics, and 46% think integration can all benefit from automation in the next two years
The number-one challenge for data pros? Data format consistency at 39%. 60% of companies ingest data in three or more formats, creating complexity
Data integration and reliability of data pipelines were second and third at 36% and 35% respectively. Data pros are spending 18% of their time on these activities
The Future of DataOps
The DataOps revolution is here. We now know that company-to-company data transfer is only going to grow. Companies would be wise to put the processes and tools in place now to prevent data heartache down the road. It’s not an easy feat, but Nexla is here to help.
Nexla helps automate your DataOps. We scale your data operations infrastructure, so you can scale your business. As the easiest way to integrate, transform, and monitor data, Nexla is mission control for inter-company data operations.