What is DataOps?
What is it not is DevOps for data analytics. More and more businesses are using their data to make better decisions, and with a the DataOps methodology that brings together the DevOps teams with the data engineers to support the new data-focused enterprise.
This is just one piece of the very large puzzle for the data-focused businesses. The number one area where DataOps helps, is to reduce the end-to-end cycle time of data analytics, from the beginning of ideation to the creation of the charts, graphs and models that create meaningful business value.
In addition to the tools used for data analytics, the data lifecycle relies upon people. The most effective way for DataOps to help your business is simple, it must manage collaboration and innovation. To make this happen, DataOps must have Agile Development power the data analytics so that data teams and users work together more efficiently and effectively.
The primary goal for DataOps is very much like the goals for DevOps, it is to satisfy the customer. Taking many cues from the agile methodology, the approach values continuous delivery of analytic insights.
The best part of a through DataOps plan is that business teams value analytics that work; they measure the performance of data analytics by the insights they deliver. As with DevOps teams the DataOps teams embrace change because of the inherent goals of their team, they seek to constantly understand their evolving customer needs.
Having a DataOps team helps to facilitate and orchestrate data, tools, code, and environments from beginning to end. The most important rule for this is to have reproducible results. DataOps teams tend to view analytic pipelines as corresponding to lean processes.
DataOps is best for…
As enterprises increasingly inject machine learning into a vast array of products and services, there are more uses for using DataOps as an approach geared to supporting the end-to-end needs of machine learning.
All data-centric enterprises can benefit from the use of a DataOps approach. According to Adam Wilson, CEO of data-prep specialist Trifacta, “Poor data quality is Enemy #1 to the widespread, profitable use of machine learning, and for this reason, the growth of machine learning increases the importance of data cleansing and preparation. The quality demands of machine learning are steep, and bad data can backfire twice — first when training predictive models and second in the new data used by that model to inform future decisions.
Although, the use of the DataOps approach is not limited to machine learning. Any data-oriented work will be made easier by taking advantage of the benefits offered by building a global data fabric.
Another area where it will be helpful, and a great fit is if your enterprise uses microservices architectures.
DataOps in the real world
To take advantage of the real-world scenarios which include large scale data capture, enterprises are adopting emerging data technologies such as these to improve their ability to scale their plans and customer service.
The ability for this DevOps approach to bring together specialists in software development and operations will help to more closely align development with business objectives and to shorten development cycles and increase deployment frequency.
It highlights cross-functional teams that cut across business operations like software engineering, architecture and planning, and product management. DataOps adds data science and data engineering teams to the mix, with the ultimate goal of increasing collaboration and communication among developers, operations professionals and data experts.
Many of the top innovators in DataOps emphasize that attaining the alignment promised by DataOps will require having an embedded data scientist in the DataOps team. This embeds the ability to have solutions to the problems at record speed. But, these embedded resources do not need to be full-time.
How to build a DataOps team
Building a DataOps team doesn’t necessarily mean you must begin to hire new specialists. The specialists you need are most-likely already in your existing enterprise team! Identifying the best project for DataOps is the next step, it will have to be a data-intensive development and then somebody with data training. That person may even be a data engineer rather than a full-on data scientist.
The important part, she says, is improving collaboration between skill sets for efficiency and better use of people’s time and expertise.
Keep a note that the roles may be filled by more than one person, but it’s also common that some people will cover more than one role. Operations and software engineering skills may likely overlap; team members with software engineering experience also may be qualified as data engineers. Often, data scientists have data engineering skills. It’s rare, however, to see overlap between data science and operations.”
What’s most important is that you will need to set goals well. Engineering teams and the data-engineers need to have a common goal, once there’s a common goal for solving a problem, then the team organizes itself very often toward solving that problem. The difficulty comes when different people see different aspects of the problem. But if they’re trying to solve the same problem and they’re willing to compromise on how it’s solved.