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Karl Aguilar

How DataOps is Enhancing Data Analytics Efficiency



With businesses producing 40 to 60% more data annually, many companies are facing challenges in managing, analyzing, and interpreting this data. On top of this, data requirements continue to evolve and the need for data access continues to grow as well, which has brought about a pressing need to improve data management processes.

 

That’s where DataOps (data operations) methodologies come in.

 

What is DataOps?

 

DataOps is an agile operations methodology that improves a company’s use of data through better tools, automation, and collaboration, with the aim of aligning data management tools and processes with the data goals and improving communication and integration between data managers and the end-users who consume data.

 

DataOps encourages improvement and innovation by introducing the concepts of agile development into the world of data analytics so data teams and users who work with data can collaborate effectively to create a smooth, hassle-free data pipeline. In turn, DataOps improves the speed and accuracy of data analytics across the enterprise.

 

Components of the DataOps framework

 

The DataOps framework is comprised of three key components that ensure its effectiveness:

 

Data orchestration - This helps manage the flow of data across multiple stages of the data pipeline efficiently, from data ingestion to processing to storage and analysis. One key aspect is the automation of data pipeline tasks, enabling organizations to streamline their data workflows and reduce the risk of human error, while also allowing data teams to focus on higher-value tasks such as data modeling and analysis.

 

Data governance – This ensures that data is accurate, consistent and secure through the establishment of policies, procedures and standards that govern how data is collected, stored, managed and used within an organization. Data governance is built on data quality management, which is the implementation of processes and controls that help ensure the accuracy, completeness and consistency of data, and data security and privacy, which is the protection of sensitive data from unauthorized access, as well as the management of data privacy regulations, such as the general data protection regulation (GDPR).

 

Continuous integration and continuous deployment (CI/CD) - They enable rapid, iterative development and deployment of data projects. CI/CD practices involve the automation of the build, test and deployment processes, so that data teams can quickly identify and resolve issues and deliver new features and improvements, as well as version control, which allows data teams to track changes to their code and data assets so data teams can work on different parts of the project simultaneously and merge their changes without conflicts.

 

Data monitoring and observability – They enable data teams to proactively identify and address issues within the data pipeline through the collection, analysis and visualization of data pipeline metrics, logs and events, which help data teams gain insights into the performance and health of their data workflows.

 

Challenges of DataOps

 

Despite the benefits DataOps presents, organizations looking to implement this methodology should be mindful of the challenges it presents and be able to address them effectively. These include:

 

Shifting mindsets – DataOps presents a new way of working that not everyone will be ready for. In addition, organizations need to have people who know their stuff across different areas and finding them is another challenge in itself.

 

Complex technology – Ensuring that all the different data tools are in sync for DataOps can  be complicated, more so as things become more automated.

 

Cost of implementation – Businesses need to invest money in hiring talent with the correct skillsets and in integrating systems necessary for DataOps. However, there are also opportunities for cost savings via improving a business’ capabilities and data governance process.

 

Energy use - DataOps processes can be very energy intensive even if using AI technology, which while using fewer resources is not necessarily a green technology.

 

How DataOps is transforming businesses

 

DataOps is a transformative approach that enhances the agility, reliability, and quality of data operations, especially when combined with Agile practices. By implementing its principles and best practices, organizations can improve their data analytics capabilities and drive better business outcomes.

 

As businesses continue to evolve in an increasingly data-centric world, DataOps will play a pivotal role in enhancing data analytics efficiency and future-proofing organizational growth

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