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Oct 10 2022 | by Abhilash Poovanadka

Examining DataOps – The Whys, Hows, and What Fors in Data-Driven Enterprise 

Business initiatives today are only as good as the amount and quality of data at the disposal and the speed at which it is processed. Data-driven organizations are able to think critically and make informed decisions faster, with the right kind of data to support them all the way through. Along those lines, this article looks at the concept of DataOps, its key principles, and how to go about its implementation. 

What Is DataOps? 

DataOps is an emerging practice of applying the principles of  DevOps  to data management. It's a way to improve the quality, speed, and reliability of data-driven processes by making them more agile and efficient. 

DataOps is about helping enterprises move beyond data as a static thing (a bunch of Excel sheets) and instead make it a living, breathing part of the organization. It's about treating data not as something that just needs to be stored but as something that can help businesses make better decisions. 

All in all, the goal is to create a culture in which people are empowered to use data as part of their everyday workflows to make better decisions faster. 

What's the Value and Utility of DataOps? 

DataOps is a movement that aims to create sustainable data-driven organizations. The value of DataOps is clear: it helps organizations become more efficient and effective and allows them to do so in an environment where data is constantly changing. With DataOps, companies can focus on the core issues that matter most to their business without worrying about "keeping up" with technological advances or incurring the cost of hiring multiple people specializing in data analysis. 

DataOps also makes possible a whole new level of collaboration between teams within organizations — and even between different companies. The ability to quickly collaborate with colleagues and partners around shared goals can make all the difference in getting work done. 

How To Implement DataOps? 

DataOps requires that you have the right people, processes, and tools to optimize your company's data usage. Therefore, the first step to implementing DataOps is hiring the right people. You need employees who understand how to gather and analyze data and are willing to collaborate with other teams within the company to implement solutions based on their findings. 

The second step is implementing processes allowing these employees to do their jobs effectively. For example, an effective process would allow a business analyst to seamlessly work in tandem with IT professionals to access critical systems or software. The third step involves creating or adopting tools to help employees gather relevant information from different sources, analyze it using DataOps techniques, and effectively contribute to  data analytics  endeavors. 

What Are the Benefits of DataOps? 

The benefits of DataOps are numerous. Here are just a few: 

  • DataOps helps create consistency in storing, processing, and accessing data. 
  • DataOps improves efficiency by automating tasks that previously required manual intervention. 
  • DataOps can help an organization make better decisions by providing accurate real-time information, allowing leaders to make quick decisions based on objective data rather than gut feelings. 
  • Altogether, DataOps helps improve collaboration, increase the speed of innovation, and reduce costs by eliminating unnecessary duplication of effort across teams. 
What Are the Key Principles of DataOps? 

The key principles of DataOps include:

  • "DataOps is a mindset, not a tool or technology." It's important to understand that DataOps is a mindset. If you want to be successful, you must approach it with the right attitude. 
  • "DataOps is about people and collaboration." It's essential to have the right people in place to implement DataOps. And they must work collaboratively within and across teams and have a good understanding of their role in the process. 
  • "DataOps is about  automation  and monitoring." Automation helps streamline the processes, while monitoring ensures everything is working as expected. 
  • "DataOps is a process." This involves creating an ecosystem for the data—where it lives and what happens to it after it leaves the source system—and ensuring that every step of this ecosystem has been designed for efficiency and reliability.
Best Practices in Implementing a DataOps Strategy 

DataOps is a framework that helps businesses manage their data in a way that is more agile and efficient. It combines the best practices of DevOps, Agile development, and Lean Six Sigma to help companies operate more effectively by focusing on continuous improvement, data quality, and automation. 

To that end, the best practices for implementing DataOps strategies include: 

  • Defining roles and responsibilities for the DataOps team as well as other stakeholders in the organization 
  • Creating a data governance committee that oversees the data management strategy and its implementation throughout the company 
  • Establishing baseline metrics for measuring performance against goals set by leadership 
  • Identifying processes that can be automated to save time on manual workflows 
  • Protecting the confidentiality and integrity of the company's customer data by setting up firewalls and other security measures 
Conclusion 

The future of DataOps is undoubtedly bright as it continues to expand in capabilities and show an upward trajectory of adopters. In other words, DataOps will live up to its name by continuing to gather speed and gain steam within organizations that wish to stay competitive (and get the most out of their  data analytics). 

To get started with DataOps, connect with experts at Novigo  today. 

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