How Should An Organisation Build An Effective DataOps Team
Ever since companies have understood how imperative data is, numerous designations and job roles have emerged in the data science domain. Data Operations or DataOps is one such role that has gained significant traction. According to a report, DataOps is currently witnessing a 60% higher rate of revenue and profit growth. DataOps is all about being an add-on to an organisation’s data management domain. It helps organisations inculcate better data management practices and processes in order to reach the data goals. Whether it’s about speed and accuracy of analytics, or data access or quality control etc., DataOps takes care of almost everything that falls into the data management domain. First Step Towards Your DataOps A-Team In this article, we are going to look at how to build a great DataOps team: Key Roles The first thing you have to do before you go onto building the team is to figure out the key roles you would need. And it has to be based on the projects that need data-intensive development and somebody who has an upper hand on the nuts and bolts of data. While the roles differ based on the company and the project, there are four roles that are considered to be the most important one: Data scientistData engineerData analystDataOps Engineer Furthermore, a particular role may have more than one professional, depending on the work and the roles can also be filled by other professionals if they have the knowledge and skill. For example, a data scientist might also have data engineering skills and can perform the required tasks. Do Not Have To Hire Professionals The next thing to understand is that when you go about building your DataOps team, you don’t necessarily have to hire fresh talent. If you already have a data science department with a significant number of professionals working, you can also make a DataOps team by picking professionals from the department. However, you have to be very specific when you are choosing the professionals. Functions To Keep In Mind Building your DataOps team is one aspect of the entire scenario, while the other aspect is the key functions that every organisation must know about and work closely with in order to make sure that the data foundation is solid enough. The functions are: Data supplyData preparationData consumption One has to make sure that owners of the data sources are also in terms with the team. This might require folks from the DataOps team to have a strong relationship with the owner in order to work together and come up with ways of making sure that each and every aspect of data is available to the business. “In a DataOps world, source owners must work together across departments, and with data engineers, to build the infrastructure necessary so the rest of the business can leverage all data,” Mark Marinelli, head of product with Tamr, stated in one of his articles. Data preparation is one of the main functions of the DataOps team. The team must make sure that every raw data is transformed into high-quality insights that can be used for analytics as well as for other applications. Coming to the last function — data consumption, it is more about making the best use of the data that the team has — it could be digging the hidden insights or even making use of the left-out data for other applications. Data is expensive and an organisation that understands this is definitely going to reap the benefits. Outlook With time, every domain is transforming — not only in terms of technology but also in terms of work processes and job roles. And data science domain is no exception. Being one of the most important aspects of almost every business, data science requires organisations to keep abreast with all the transformation. It has the power to take a business to a whole new level if done right, so it’s definitely high time that organisations understand the imperativeness of DataOps and invest enough time and effort to make the best out of it.
Ever since companies have understood how imperative data is, numerous designations and job roles have emerged in the data science domain. Data Operations or DataOps is one such role that has gained significant traction. According to a report, DataOps is currently witnessing a 60% higher rate of revenue and profit growth. DataOps is all about […]
Data Operations or DataOps has gained significant traction and is currently witnessing a 60% higher rate of revenue and profit growth.
["Data Engineering", "Data Science", "DataOps"]
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["AI", "data science", "analytics", "RAG", "R", "Go", "data engineering", "GAN", "programming_languages:R", "programming_languages:Go"]
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