Organizations face the challenge of making their data management more agile and transforming their data into usableinformation based on their activities; creating what we now know as DataOps . DataOps is a methodology that utilizes Agile development practices to create, deliver, and optimize data products quickly and profitably.
In essence, DataOps gives speed and agility to the data channeling process from end to end and from collection to delivery. Its objective is to accelerate the gathering of data along with its processing, integration, and analysis. It is, in other words, a methodology for taking advantage of data efficiently while managing it in line with commercial objectives.
With DataOps, organizations continually strives to create better ways to manage its data. This practice collaborates with the success of digital transformation and includes every part of the life-cycle of data management. It benefits companies by fostering collaboration between teams of data scientists, data engineers and analysts, operations, and product owners.
DataOps encompasses various disciplines, such as data development, transformation, and extraction, data quality control, data governance and access control, data center capacity planning, and information system operations.
Like DevOps, this methodology is not associated with any specific software tools or technologies. Rather, it consists of “frameworks and groups of related tool collections that enable a DataOps approach to collaboration and greater agility. It also includes automation process practices.
A study was done and found that 73% of the companies surveyed planned on investing in DataOps. These companies had to manage enormous quantities of data (big data) and turn them into knowledge. They needed to put an end to useless and wasteful management processes. A DataOps team that understands the data requisites of the entire organization can do this by orchestrating the centralization of all the flows.
DataOps enables an increase the capacity to offer predictable and reliable commercial value on the basis of data assets. Companies can make greater and better use of their data for making decisions, developing products, and providing services. Some of the benefits this practice brings are better communication and collaboration between different work groups and between members of the same team, optimization of data quality, information on data in real time, reduction of data science application cycle times, and the capacity to predict different scenarios through data analysis.
Are the people in your organization using DataOps practices?