What is DataOps?
DataOps, which is short for data operations, is a newer operations methodology. DataOps cultivates data management processes that enhance the speed and accuracy of analytics such as data access. This also applies to quality control, automation, integration, deployment, and management.
DataOps is a process-driven, automated technique, which analytic and data teams can use to bring down the cycle time of data analytics and enhance its quality. DataOps started off as a set of practices that in due time, matured to become an independent approach to data analytics. The merging of software development and IT operations has boosted the velocity, quality, and predictability of operations. Borrowing some methods from DevOps, DataOps promises to bring more and more improvements to data analytics at large.
DataOps aligns the way organizations manage your data with the goals you have for the data (with some overlap with data governance). It reduces churn rates and leverages customer data to build a recommendation engine that brings up products that are suitable to customers, which makes them more likely to buy.
DataOps is associated with operational efficiency. Those improvements are related not to agility alone, but to security and transformation. Companies that have already engaged with DataOps agree that it has a positive impact on their enterprise, and although improved agility and efficiency are associated with DataOps, the biggest priority and benefit is actually related to compliance and safety.
Enterprises that have implemented DataOps are more advanced when it comes to transitioning to the cloud and managing digital transformation strategies. They are better positioned to gain a competitive advantage over their rivals.
Early adopters of DataOps enjoy benefits to the extent that they are doubling to invest even further in services as well as in-process and organizational changes. Survey results reinforce the view that although it is still relatively not known as a mainstream term today, DataOps promises to have a growing impact on markets in the future.
Principles of Dataops
DataOps started off as a set of independent practices which then turned into a DataOps Manifesto. Some main principles of the DataOps manifesto are:
- Help customers consistently: The highest priority is to help the customer with the early and continuous delivery of important analytic insights.
- Change with evolving needs: Customer needs change and embracing that generates a competitive advantage.
- Team spirit: Analytic teams always have a wide variety of roles, skills, and tools that have to work in coordination with one another.
- Interactivity: Customers and analytic teams, as well as operations, work together regularly on all projects.
- Heroism is reduced: Since the need for analytic insights keeps increasing, analytic teams strive to reduce heroism and create sustainable, as well as scalable, data analytic processes.
- Reflection: Analytic teams better their operational performance through self-reflection at regular intervals based on feedback provided by their customers and operational statistics.
- Code is analytics: Analytic teams use the individual tools to access, integrate, and then visualize data. Each of these tools produces codes and configurations which describe the actions taken upon data in order to deliver insight.
- Orchestration: The complete orchestration of data with tools, code and environments, and the analytic team’s work is a major driver of success.
- Reproducibility: Reproducible results are essential, hence everything has to be versioned. The data and low-level hardware or software configurations, the code and configuration are specific to every tool in the chain.
- Environments are disposable: Minimizing the cost for analytic team members to experiment happens by giving easy-to-create, isolated, and disposable technical environments that reflect their production environment.
- Simplicity: Consistent attention to technical excellence and proper design increases agility. Simplicity is essential.
- Analytics is manufacturing: Analytic lines and chains are analogous to manufacturing lines. The concept of DataOps is an emphasis on process-thinking focused on achieving efficiencies in the manufacture of analytic insight.
- Quality and performance monitoring: Performance and quality measures have to be monitored at all times to detect unprecedented variations and produce operational statistics.
More than being a technology platform, DataOps can be understood as an approach or a methodology since it assembles many data technologies and practices into one integrated environment. All the data can flow easily through this system from data sources through the data refinery and the data repository to data consumption, which helps to make a positive impact on business investments. Some of the key components of the platform are:
- Apache Oozie, which is an open-source workflow system to manage Apache Hadoop jobs.
- DBT (Data Build Tool), which is a command-line tool that allows data analysts to transform data more effectively.
- BMC Control-M, which is a solution for digital business automation that aids in simplification of diverse batch application workloads.
DataOps framework combines five important elements that range from technologies to culture change.
The first element is enabling technologies including data management tools, Artificial Intelligence (AI), Machine Learning (ML), and intelligent automation.
The second element is an adaptive architecture for continuous innovations in technologies, services, and processes.
The third element enhances data, putting it into a useful context for accurate analysis. The intelligent metadata that the system creates at ingestion saves time later in the data pipeline.
The fourth element is the DataOps methodology for building and deploying analytics and data pipelines, which follows data governance and management.
The fifth element of a DataOps framework is the most important and difficult: culture and people. To fulfill the potential of DataOps, a culture of collaboration among IT and cloud operations, data architecture, and data consumers has to be created.
How to Implement DataOps
There exist several approaches to implementing DataOps. There are a few key areas of focus such as:
The democratization of data: Experian Data Quality says 96% of Chief Data Officers believe business stakeholders want more access to data, and 53% complain that lack of data access is the biggest barrier to driving better decision making. A lack of data access can create a roadblock to innovation. Self-service data access and the infrastructure to support it are essential. Machine learning and learning applications require constant new data to be fed in order to improve. Any company that strives to be on the cutting edge requires data sets to be easily available.
Leveraging platforms and open source tools: DataOps practices require a data science platform with easy support for languages like Python, R, data science notebooks, or GitHub.
Automation: It’s imperative to automate steps that unnecessarily need lots of manual effort for quality assurance testing or data analytics pipeline monitoring.
Enablement of self-sufficiency with microservices: Giving data scientists the ability to deploy models can integrate that code where needed without refactoring, resulting in improvements in productivity levels.
Collaboration: Collaboration is crucial to implementing DataOps. The tools and platforms which you choose as part of the DataOps journey should help bring teams together to use data more effectively.
DataOps vs. DevOps
DataOps is a newer and much broader concept than DevOps. DataOps simplifies and relies on a newer collaboration methodology between teams. While DevOps builds collaboration between development and operations within IT, DataOps requires collaboration across the whole enterprise, from IT to experts to data consumers. In short, as DevOps makes IT more effective, the other enhances the efficiency of the entire company.
DevOps increases the scope of the problem, seeing it not specifically as a Dev problem or an Ops problem. DataOps does the same thing with organizations through the flow of data from its creation to use but affects far more groups as the entire organization depends on data. DataOps is more complex too. DevOps has only one delivery pipeline which is the code to execution, but DataOps has production deployment and data pipelines to execute the flow of data.
DataOps has the potential to transform the ways organizations analyze and process the data they gather during regular DevOps operations. With a sharp emphasis on goals and mission statements of companies, DataOps has the capability to revolutionize the entire software development cycle and all data analytics processes.
Productive Edge is a leading organization specializing in helping enterprises work with DataOps. We partner with our clients to enable technology-powered experiences that reimagine and transform the way people live and work.
To learn more about how the technology consultants at Productive Edge can help your business implement DataOps, contact us to book a free consultation.