Five worthy reads: Is DataOps the next big value driver in the analytics ecosystem?

Five worthy reads: Is DataOps the next big value driver in the analytics ecosystem?

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. In this edition, we’ll learn about DataOps, an interesting methodology that can help organizations fast-track their data analytics operations.

Five worthy reads: Is DataOps the next big value driver in the analytics ecosystem?
Credits- Illustrator: Balaji K R

By Monisha Ravi

With the current data trend, there is an ever-increasing demand for data professionals to transform raw data and convey insights to address various business needs. Similarly, to deliver faster results and improve customer experience, DevOps teams have become increasingly popular as they provide end-to-end support due to their agile working processes. A newer methodology, called DataOps that combines data teams and DevOps teams, has paved the way for a stronger data-driven approach in organizations. This methodology includes people, technology, and processes as important components to unlock the value from data streams and realize business goals.

Under DataOps, experimentation, iteration and feedback on data analytics are emphasized. These help to increase the quality and confidence in data, and ultimately, to achieve the goals of the organization, all through the continuous integration/continuous delivery (CI/CD) method. CI/CD uses automation tools to help deliver faster results. So, when CI/CD is introduced in every stage of the data analytics project, it accelerates the delivery of results and feedback of that stage. This helps streamline the design, development, and maintenance of the project in each stage of the data analytics cycle. DataOps also enables teamwork and productivity, while reducing errors and project times.

Of course, to use the data it must first go through a cycle involving various steps including cleaning, validating, analyzing, and reporting. But once this cycle begins, DataOps is introduced in every step to orchestrate its movement, develop a new model, monitor issues and errors, test the results, or even deploy results into production. With DataOps, not only will the project benefit from optimized results, the entire organization’s work culture is often enhanced as implementing DataOps improves people skills due to the nature of the methodology.

For those organizations planning to opt DataOps, here are some hand-picked reads that will help with the process:

1. What is DataOps and how does it improve data anlaysis

With strategic management practices, organizations can yield data and, in turn, gain more insights. This is the methodology that the likes of Facebook and Netflix use to outperform competitors. While trying to execute it, ensure data is accessible to all, automate lengthy processes to improve productivity, and implement DataOps to enhance data analysis.

2. Understanding DataOps

As a functional strategy for analytics, DataOps inspires employees to stick to the goals of the organization. Not only does it promote quality and problem-solving capabilities, it also encourages collaboration and curiosity to find new opportunities. In addition, continuous data analytics supports real-time insights and enhanced data analytics enables customer feedback to be received in a shorter amount of time, both of which are needed to provide better customer service.

3. The Top 3 Ways to Get Started With DataOps Pipelines

To transform data into valuable insights, DataOps should be considered. One way to transform is by empowering employees and giving them the responsibility of self-service, as goals will be driven faster in this manner. Also crucial is reducing the risk of changes made during the process. By using automation, the quality and trustworthiness of the data will increase in due time.

4. Key Principles of a DataOps Ecosystem

To avoid being trapped under a single vendor’s services and to use quality data to its full potential, focusing on key principles is necessary. Shifting to the cloud is a principle that will help scale out, improve computing power, and reduce project times. Another important principle is increasing confidence in the data by maintaining metadata lineage to track inputs and outputs.

5. Benefits & Challenges Of DataOps In Data Science

Playing an essential role in delivering results, DataOps helps develop best practices within organizations. It also helps promote a shift in the working culture as it encourages team collaborations. The downside of DataOps is, however, unrealistic expectations that can hinder growth.

While it is extremely important to move from a siloed data approach to employ DataOps, it is also important to review the organization’s mindset and operational infrastructure before pursuing it. If the process is taken lightly, KPIs will not be met. Therefore, the three elements—people, technology and processes—must be kept in mind. Building a DataOps team with the right expertise is essential. Team members should be made aware of their roles and responsibilities. Their skills gaps must also be addressed. The supporting infrastructure and technologies, like the cloud and open source applications, must be procured and configured in advance so the third element, processes, won’t be hindered. For all DataOps projects, the output is important as it will be fed back and/or moved to production. To achieve optimized results, a checklist should be maintained from the beginning to ensure important elements aren’t missed.

** Optrics Inc. is an Authorized ManageEngine partner

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