The history of DataOps
While DataOps is a relatively new concept, there are many different definitions of what it exactly is. DataOps, a combination of Data Analytics and Operations, was first defined in 2014 by Liebmann as “the set of best practices that improve coordination between data science and operations”.
Later, in 2015, it was defined as “DataOps is a management method that emphasized communication, collaboration, integration, automation, and measurements of cooperation between data engineers, scientists, and other data professionals”, by Andy Palmer.
It especially changed a couple of years ago when companies realized that they had to include data in the conversation of DevOps. By now, it was mentioned in books, such as DataOps Cookbook, written about on blogs, DataOps Manifesto, and big companies were promoting the use of DataOps.
DataOps of today
As of now, DataOps is still defined the way it was done in 2015. The DataOps framework consists of three main variables; Agile development, DevOps, and lean manufacturing.
Agile Software Development
In the ’80s a single software project would take up to twelve months. At that time, they still used the Waterfall software development methodology. While, with the help of Agile software development, this now only takes up to 11 seconds. With innovation occurring in rapid intervals, the team can continuously reassess its priorities and more easily adapt to evolving requirements.
Agile software development is an umbrella term for several different repeatable and step-by-step software development methodologies. It delivers updated and valuable features in short intervals, looking for immediate feedback. In a DataOps setting, Agile methods enable organizations to respond quickly to customer requirements and market changes.
DevOps
The term DevOps was established a while before the term DataOps came around. And you might think that DataOps is just DevOps for data. It is not. DevOps stands for software developments and IT operations. DevOps is an IT mindset that encourages communication, collaboration, integration and automation among software developers and IT operations to improve the speed and quality of delivering software.
DevOps is used by software engineers to reduce time to deployment, decrease time to market, minimize defects, and shorten the time required to resolve issues. This is done by reducing its software release cycle. It is about the optimization of building codes and software systems. So, how is DevOps part of DataOps then?
Optimizing code builds and delivery is only one piece of the larger puzzle for data analytics. DataOps seeks to reduce the end-to-end cycle time of data analytics, from the origin of ideas to the literal creation of charts, graphs, and models that will lead to high-value insights for the decision-makers of your business.
Lean manufacturing
Methods like lean manufacturing might not seem fit for the data industry like it is fit for the automotive industry. Yet, lean manufacturing can just as easily be implemented in the field of data operations and analytics.
DataOps approaches data errors the same way that a manufacturing operation controls errors in supplier quality, work-in-progress and finished goods. Using lean manufacturing for your data operations will restrain bottlenecks, improve productivity, and ensure higher quality and consistency.
A part of lean manufacturing is the tool SPC (Statistical Process Control), which is specifically used for measuring and monitoring data and operational characteristics of the data pipeline. Every stage of the data pipeline monitors inputs, outputs, and business logic. Input tests can catch process errors at a data supplier or upstream processing stage. Output tests can catch incorrectly processed data before it is passed downstream.
Benefits of DataOps
We now know what DataOps is, but how will it benefit your business? DataOps is there for data engineers, data scientists, and data professionals. In this section, the main benefits of implementing DataOps in your business will be highlighted.
1. Faster process
By the use of Agile Software Development, as mentioned previously, you now can have data updates within a matter of seconds. This methodology aims to help enterprises implement an approach that makes it possible to manage and use their increasing data volumes effectively while reducing the cycle time of data analytics.
2. Real-time insights
By speeding up the entire data analytics process, you get closer to real-time insights in your data. In the fast-changing world, we live in, we need to have the ability to adapt to any market changes, as fast as we can. DataOps moves code and configuration continuously from development environments into production, leading to near real-time data insights.
3. Focus on import issues
With the time-savings and more accurate data analytics, your data team can now focus on the market needs and changes, immediately. DataOps allows IT leaders to focus more on improving communication, integration, and automation of data flows enterprise-wide.
Without the burden of inefficiencies and poor quality, data science teams can focus on their area of expertise; creating new models and analytics that fuel business innovation and create a competitive advantage.
4. Catch errors immediately
With the help of DataOps, especially SPC, output tests can catch incorrectly processed data before it is passed downstream. Tests ensure the reliability and quality of the final output by verifying that work-in-progress (the results of intermediate steps in the data pipeline) matches expectations.
Conclusion
A lot of data’s value for a company is not uncovered because there is a lack of understanding. A company can invest in new technologies; roll out a full DataOps strategy; and engage in a ‘new collaborative culture’ internally, but if the data scientists, business users, and decision-makers can’t derive proper meaning from the data, business outcomes will get lost in translation.
Remember, DataOps is a process, not a tool itself. However, there are many tools available in the market that can help you build this process. With the right tools and, most importantly, a good understanding of your data, DataOps is an opportunity to improve your enterprise’s data operations and analytics in many ways.
