Agile Manifesto And DataOps Manifesto
Exploring the influence of one on the other
DataOps is a relatively new field that focuses on streamlining the entire data lifecycle, from acquisition to consumption, with the ultimate goal of delivering business value to the end user. An agile methodology is a proven approach to software project management that emphasizes flexibility, adaptability, and customer satisfaction.
This blog post explores how Agile methodology can be applied to DataOps (and perhaps Agile methodology-inspired DataOps!), and the benefits that result from this combination.
What are the agile manifesto principles?
The Agile Manifesto outlines the core values of Agile, including individuals and interactions over processes and tools, working software over comprehensive documentation, customer collaboration over contract negotiation, and responding to change over following a plan.
In addition to the core values, the Manifesto for Agile Software Development also outlines twelve principles:
focusing on customer satisfaction,
welcoming changing requirements,
frequent delivery of working software within a few weeks to a few months
close collaboration between business people and developers
empower motivated individuals with the environment and support they need to successfully complete projects.
a face-to-face conversation is the best way to share information within a development team.
measuring progress primarily through working on software
sustainable development
continuous attention to technical excellence and good design
simplicity is key to maximizing productivity by minimizing unnecessary work.
self-organizing teams
regularly check on how to be more effective
The Agile Manifesto has common themes that emphasize end-users, collaboration among non-technical and technical users, and within their teams, as well as providing excellent, working code consistently and frequently. These themes encourage software engineers to work more efficiently, respond more effectively to changing requirements, and deliver higher-quality software that meets the needs of the end user.
Yet, these agile principles can be applied not only to software development, but also to other fields such as project management, marketing, and data management, among others.
Agile principles in data
In our pursuit of data insights and a high return on investment (ROI), we often neglect the processes that enable us to produce them. By interweaving Agile principles in the data world, we can gain a better understanding of the business and its needs without neglecting the processes as a whole.
Most importantly, Agile methodology emphasizes a customer-centric approach. Data teams play a critical role in ensuring that the insights generated from data are accurate, reliable, and actionable. By analyzing data and identifying patterns and trends, data teams can help businesses make informed decisions and develop effective strategies. It is important for data teams to not only focus on the data itself, but also on how that data is presented and communicated. Data should be represented in a way that is easy to understand and that highlights the most relevant insights. By doing so, businesses can ensure that the insights generated by their data teams are both relevant and actionable, helping them to make informed decisions and drive success.
Secondly, Agile software development methodology emphasizes constant collaboration between business and developer teams. This collaboration helps ensure alignment between the project goals and the development process, which in turn leads to higher-quality products. This kind of collaboration is particularly important when working with data teams, as it is essential to have a shared understanding of the business objectives and data requirements in order to build effective data solutions. By using Agile methodologies, data teams can be sure that they are building data solutions that meet the needs of the business and that these solutions are developed in a way that is efficient, effective, and sustainable over time.
Agile methodology is a way of working that emphasizes continuous delivery and improvement. This principle is particularly relevant in data because the data is constantly changing, and new insights are being discovered. By using Agile methodology, teams can respond to changes in the data more quickly and adapt their processes accordingly. One way to achieve continuous delivery and improvement is through the use of automated testing and deployment. This can help to ensure that the data is always up-to-date and relevant to the end-user, and can also help to reduce the risk of errors or inconsistencies in the data. Another way to achieve a continuous improvement is through regular retrospectives, where the team reflects on its processes and identifies areas for improvement. By continuously iterating and improving their processes, teams can ensure that they are delivering value to the end user and becoming a better data team for themselves and the company.
Why DataOps then?
As we just walked through how Agile Methodology can be seen as beneficial in data teams, you might wonder why we have DataOps.
Manifesto for Agile Software Development was created in 2001. And it is exactly for that. Software Development. The software development field did not debut until about the 1950s. [1]
Data science as a field started around the time that the Manifesto for Agile Software Development was created [2] and has only diverged from software engineering since. Data science is a rapidly growing and constantly evolving field that has emerged in response to the increasing amount of data generated by modern businesses. It seeks to streamline the entire data lifecycle, from acquisition to consumption, with the ultimate goal of delivering business value to the end user. Although code is used to derive business value in this endeavor, it is not the primary focus. Rather, the data itself is the main focus.
Here are some additional principles that were added to the DataOps Manifesto [3]:
Value working analytics: It is important to not only focus on analytics as a concept but to also prioritize the practical applications and ensure that they are effective.
It's a team sport: Analytics is not a solo effort, but rather a collaborative one. It is important to build a team with diverse expertise and experiences to work together to achieve common goals.
Analytics is code: Just like software development, analytics requires coding skills to create models, algorithms, and pipelines. It is important to cultivate a strong foundation in coding to excel in analytics (no pun intended!).
Analytics is manufacturing: Analytics is not just about generating insights, but also about deploying the pipelines that create the insights. It is important to treat analytics as a production process and ensure that it is scalable and sustainable.
Quality is paramount: Analytic pipelines need to have a base that can automatically detect abnormalities and security issues in code, configuration, and data. They should also provide continuous feedback to operators to avoid errors.
Monitor quality and performance: To maintain quality, it is important to continuously monitor the performance of the analytics models and pipelines by generating operational statistics and identifying unexpected variations.
Reuse: Whenever possible, it is important to reuse existing models, algorithms, and pipelines. This not only saves time and effort but also ensures consistency and accuracy across projects.
The DataOps Manifesto combines the principles of Agile methodology with a few new ones to create a framework for teams to create flexible, efficient, and effective processes for managing data.
Conclusion
In summary, DataOps is a newer field than software development, and while it shares many principles with Agile methodology, it also has some unique requirements. The DataOps Manifesto adds principles such as valuing working analytics, treating analytics as a production process, and monitoring quality and performance. These principles create a framework for teams to create flexible, efficient, and effective processes for managing data. By applying Agile principles to DataOps, teams can ensure that the data is accurate, timely, and relevant to the business needs, while also improving the overall efficiency of data teams.
[1] https://www.hackreactor.com/blog/the-history-of-coding-and-software-engineering
[2] https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/