What is DataOps? — Definitions and Ambiguities.
DataOps Definition
DataOps can be defined as data pipeline development and execution methodology by assembling people and technology to deliver better results in a shorter time. With DataOps, people, processes, and technology are orchestrated with a degree of automation to streamline data flow from one stage of the data lifecycle to another. With Agile, DevOps, and SPC’s best practices, technologies, and processes, DataOps promotes data governance, continuous testing and monitoring, optimization on the analysis process, communication, collaboration, and continuous improvement.
DataOps, from its concept establishment, has focused on delivering robust, rapid, collaborative, and quality-driven data analytics projects by integrating technology, people, and redesigning processes. DataOps is an advancement of the existing isolated data pipeline to provide better monitoring and results. DataOps is not an absolute method with scientifically proven predefined rules and steps. Instead, it is a progressive approach to create a better work environment for data workers, deliver faster data analysis results for stakeholders, track data movement with the ability to associate change and effect, and reduce cost and effort without compromising results.
DataOps' necessity lies in the fact that data are assets, and assets values depend on how well an organization uses its assets in its operation. Therefore, the term 'DataOps' itself is a combination of 'Data' and 'Operation', which gives the realization of the approach to operate data/assets to deliver business goals in an organization.
If data are assets, then reports, insights, and information are products. Then data analytics is the process of utilizing and converting assets to the product. It is always true that readily available quality product with low cost generates better business value. To deliver quality products faster at a low price, innovation in the business process is necessary. DataOps provides the innovation factor to offer data products better in data analytics. Innovation in terms of redesigning organization culture, creating a collaborative workspace, managing, monitoring process and results, reducing delivery cycle time, and a better product itself.
Ambiguities in DataOps Practices
DataOps is an emerging concept. In recent years experiment and research contributions are progressing in the DataOps through the involvement of DataOps practitioners and enthusiasts. However, there are some misconceptions prevalent in DataOps listed and explained below.
1. DataOps is just DevOps applied in data analytics.
DataOps is not DevOps for data. It takes best practices from DevOps and agile methodology and combines with lean manufacturing's SPC and data analytics specific tasks to streamline data lifecycle and provide quality results. Data analytics projects and software development projects have vast differences.
2. DataOps is all about using tools and technology in the data pipeline.
DataOps is not about automating everything using tools and technologies and keeping human involvement away. Instead, DataOps advocates people's balanced involvement along with tools and technology. In addition, communication and collaboration are highly focused on DataOps to turn data into value for all involved parties.
3. DataOps is an expensive methodology.
Acquiring and running different tools always comes with a price. Data analytics projects will cost an organization whether they follow DataOps or not. Therefore, one should compare their investment with the value received in the near future. Furthermore, proper research on tools and technology before implementing a data pipeline can help make informed decisions to cut the cost to a minimum.
4. With DataOps, there is no need for coding.
Without writing code, data pipeline tasks cannot be formed at all. So coding is always a baseline of data analytics projects. With DataOps, even the coding can be reduced by reusing and versioning codes, algorithms, and configuration scripts. IDEs and source code editors provide easy writing and debugging of codes.
5. DataOps can only use on data analysis tasks.
DataOps is not just about generating reports and delivering fancy charts, templates, bars, and figures using visualization tools. It is also about covering the whole data lifecycle from data collection to disposal. Moreover, it is not just about covering the data lifecycle; it is also about creating a data-driven organizational culture that emphasizes collaboration, communication, transparency, and quality on administrative tasks.
6. DataOps and data pipeline are two completely different ways to handle data projects.
DataOps is an approach to implement a data pipeline. We apply the DataOps principle and practices while developing and executing data pipelines. Therefore, data pipeline with DataOps methodologies is also called DataOps pipeline. DataOps is not an entirely new way of performing data analytics tasks; rather, it redesigns the data pipeline to deliver quality results in a short time with minimal cost and effort.
Author Note: The expert from my master thesis work. Please visit http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1525698&dswid=1594 for in-depth exploration on DataOps.
