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In simple words, DataOps is all about aligning the way you manage your data with the objectives you have for that data. Letu2019s know in detail what actually DataOps is!<br>
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Data operations is a cutting-edge agile operations methodology that assists both IT and big data professionals. • The primary objective of it is to cultivate data management practices and processes that enhance the speed and precision of analytics. This encompasses quality control, integration, automation, data access, and model deployment & management. • In simple words, DataOpsis all about aligning the way you manage your data with the objectives you have for that data. • For example, if you wish to reduce the customer churn rate, it is essential to leverage your customer data to develop a recommendation engine that produces products that are according to the requirements of the customers. • This would make them engaged in the buying process for a pretty long time. However, this is only possible if your data science team has access to the data they require to build that system.
They should also have the requisite tools to deploy it, integrate it with your website, continuously feed it with new data, monitor performance, etc. These are all ongoing processes that will require inputs from your engineering, IT and business teams. • DataOps is beneficial to everyone. With the help of better data management, it is possible to gather more and more data. • More and more data results in better scrutinisation, which ultimately leads to better insights, business strategies and higher profitability. • It fosters collaboration between data scientists, engineers and technologists in such a way that every team works in unison to leverage data more efficiently and at quick turnaround time. • It has been observed that organisations that are good at the prompt and thoughtful approach to data science are four times more likely than their less data-driven counterparts to observe growth that exceeds the expectations of their shareholders.
Hence, in present times, we see companies across the board, making data management alterations that support more receptiveness and innovation. Companies like Netflix, Facebook, Stitch Fix, and others have already tried approaches that fall under this umbrella. Now, let’s look at the process of implementing it. Democratise Your Data It has been observed that a lack of data access can create a significant roadblock towards innovation. Therefore, you need to have self-service data access and the infrastructure to support it. Any company that strives to be innovative needs datasets to be available at all times. Leverage Platforms & Open Source Tools To become agile, it is imperative not to waste time building things that your team is already aware of like open-source tools. Contemplate carefully about your data needs and then create your tech stack accordingly.
Automate This is the part of the DevOps world wherein to achieve a quicker time to value on data-intensive projects; it becomes essential to automate steps that typically require unnecessary manual work like data analytics pipeline monitoring and quality assurance testing. You can even enable self-sufficiency with microservices. A prime example is, by giving your data scientists the acumen of deploying models as APIs means engineers can integrate that code where required without refactoring, which results in productivity enhancements. Careful Scrutinisation It is no surprise to see more and more organisations adopting the centre of excellence approach when it comes to data science management. Here, the blueprint of success is measured as per the tools, processes, infrastructure, priorities and key performance indicators.
Smash Silos Collaboration is the key to executing DataOps flawlessly. The tools and platforms you utilise in the journey need to support the broader objective of bringing the teams together to use data more effectively.
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