DataOps: A Comprehensive Guide to Principles, Benefits, and More
Data is growing at an exponential rate all over the world with rising complexity levels, the quality of data is suffering, and the gap between data engineers and data analysts is increasing. Businesses and data teams must be more responsive to altering conditions and move towards modern-day project methodologies. This can be done with the next-gen concept – DataOps which is trending worldwide.
Much has been said about DevOps, but DataOps is different. DevOps focuses on the collaboration between the development and operations teams. DataOps focuses on collaboration across the entire business unit, from the data experts to the IT task force to data users.
DevOps makes the IT department agile and effective whereas DataOps makes the entire organization so. DevOps helps in the effective release of code for data analysts and scientists whereas DataOps optimizes the entire business flow and offers a holistic view of the entire business lifecycle.
What is DataOps? An Overview
DataOps is a set of practices, processes, and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics.
DataOps, abbreviated for Data Operations, is a trending agile approach to design, implement and maintain a distributed data architecture, supporting a variety of tools and frameworks, with a focus on getting enhanced business value from data. This approach works on speeding up the production of apps executing on huge frameworks.
It frees the silos between the teams – data management, software development, and IT teams to ensure optimal usage of the organizational data in the most flexible manner. The concept of DataOps is created on the basic four fundamental concepts of Lean, Product Thinking, Agile, and DevOps, for enhancing the performance, quality, and speed of data-driven activities.
DataOps teams are managed by a group of well-trained data scientists, chief analytics officers, data engineers, BI analysts, analytics managers, data architects, and data analysts.
DataOps offers data integration, data validation, observability, and metadata management. It facilitates data operations to offer actionable insight into information with a shorter delivery span. It aligns your data management procedures with your organizational expectations.
It is a well-established collection of architectural patterns, technologies, workflows, and cultural practices that empowers faster innovation and research, high-quality data, low error ratios, high collaboration between teams, and accurate measurement of output. IBM, Informatica, DataKitchen, Delphix, and GoodData are some of the famous examples of DataOps.
Major Components of a DataOps Framework
For a DataOps framework to operate effectively, there are certain inclusions that must be present, here are:
- Collaborative Atmosphere
It is essential to create a collaborative environment between all the involved teams. The right kind of culture among people will ensure a smooth transition and integration of data for optimal organizational value.
- Cross-Functional Communication
The basis of DataOps lies in ensuring comfortable and effective communication between all involved teams. The cross-functional communication is a must-have component while implementing DataOps.
- Data-Driven Environment
Organizations must adapt to a continuous data literacy drive that can come up with newer approaches to use data in various ways that can help businesses to prosper and earn more revenue.
- Agile Approach
For the DataOps to function as per requirements, it is essential to adopt the agile approach of breaking down different procedures into smaller elements ensuring a continual way of development through continuous integration pipelines.
- Adapting Newer Technologies
To go with modern-day advancements, organizations must implement technologies like AI, ML, DevOps, AR, VR, data management, etc. This will help better utilization of data from different sources.
Key Advantages of DataOps
Here are some of the salient benefits that DataOps brings along:
- Enhanced data quality and visibility
- Faster decision-making and analytics deployment
- Automation of tasks involved in data management
- Lesser need for manual coding because of pre-created components
- Competitive edge over the others
- Augmented collaboration between teams
- Reliable, robust, secure, and real-time data insight
- Faster data pipeline
- Easy access to all types of data
- Reduction in cycle time of applications
- Code reusability
- Increased transparency in operations
The DataOps Manifesto and Principles
There are certain key principles that DataOps operates on. Here are the thoughts regarding the embedded principles of DataOps:
- Keep satisfying your customer continuously
- Adapt to changes keeping in mind customer needs
- Embed a set of data analytics tools and techniques as a team
- Ensure a daily collaboration between stakeholders
- Self-organize and work towards sustainability and scalability of teams
- Analyse outputs based on reports, statistics, and performance analysis
- Orchestrate data, environments, tools, and teams with proper versioning
- Simplify work and work toward continuous process improvement
- Ensure high-end quality with continuous feedback and monitoring
- Use more reusable components saving time and efforts
- Go in for minimum time to solve any issue or client need
- Implement layered interfaces
- Use the best tools, processes, storage, and access
Key Best Practices While Implementing DataOps
DataOps can best be implemented if the following tips are implemented as best practices:
- Define semantic data rules from the beginning
- Implement project progress measurements at all stages of the project
- Create a DataOps team with a balanced variety of skills and responsibilities
- Go in for a continuous model of operation to ensure increased efficacy
- Optimize all involved procedures for handling data silos
- Make the best use of feedback loops for data validation
- Automate processes as far as possible with modern-day data science tools
As We Wind Up
DataOps is a newer perception that is increasingly being used in the era of data analytics. It has been enhancing organizational performance and solving all basic issues faced by businesses. It just needs the right kind of implementation and support from an experienced IT partner to implement it correctly in your organization.
The future of DataOps is bright! It will see increased and seamless integration with other data systems. The involvement of AI and ML will help the DataOps pipelines in managing data catalogs. DataOps tools will help pipelines via development, integration, etc.
Ridgeant’s data analytics consulting helps you unlock actionable insights and evolve into a data-driven organization to optimize performance and increase growth. As a managed service provider, we offer data visualization services, analytics and BI modernization, BI implementation support, data mining, self-service BI, and more.
Contact us for any implementation of the latest and trending processes like DataOps; we are sure to help with our experience and expertise.