DataOps, first termed in 2014, has matured into a discipline born out of mutual enterprise data challenges
What began as a theoretical ideal is now the key to reducing data costs, accelerating analytics, and enabling better machine learning outcomes.
While definitions vary, DataOps generally refers to applying analytic and management processes across the entire data lifecycle in order to optimize the performance of each step in the pipeline from ingestion to analysis. In a world where the amount of data created doubles every 12 to 18 months, and the expectations being put on the burgeoning data rise, DataOps offers the critical promise of efficiently turning raw sources into valuable intelligence. Companies that take advantage of modern DataOps solutions create, as Wayne Eckerson has written, “an environment where ‘faster, better, cheaper’ is the norm, not the exception.”
Increasingly complex data ecosystems face new challenges that stand in the way of enterprise data cost savings, analytics improvement and product acceleration goals. Issues such as siloed data, multi-cloud data environments, and compliance regulations, have caused DataOps developers to design solutions that meet these ever-growing set of needs. As capabilities arise, advantages emerge; including the following seven benefits of modern DataOps.
The convergence of point-solution products into end-to-end platforms has made modern DataOps possible. Agile software that manages, governs, curates, and provisions data across the entire supply chain enables efficiencies, detailed lineage, collaboration, and data virtualization, to name a few benefits. While many point-solutions will continue, success today comes from having a layer of abstraction that connects and optimizes every stage of the data lifecycle, across vendors and clouds, in order to streamline and protect the full ecosystem.
As machine-learning and AI applications expand, the successful outcome of these initiatives depends on expert data curation, which involves the preparation of the data, automated controls to reduce the risks inherent in data analysis, and collaborative access to as much information as possible. Data collaboration, like other types of collaboration, fosters better insights, new ideas, and overcomes analytic hurdles. While often considered a downstream discipline, providing collaboration features across data discovery, augmented data management, and provisioning results in better AI/ML outcomes. In our COVID-19 age, collaboration has become even more important, and the best of today’s DataOps platforms offer benefits that break down the barriers of remote work, departmental divisions, and competing business goals.
Automated Data Quality In June of 2020, Gartner found that 55% of companies lack a standardized approach to governance. As ecosystems become more complex, data increasingly is exposed to a variety of data storage, compute, and analytic environments. Each touchpoint introduces security risks and the most effective way to reduce this risk is to establish automated data governance rules and workflows within a zone-based system that are applied across the entire supply chain. Modern DataOps platforms offer automated, customizable data quality, tokenization, masking, and other controls, across vendors and technologies, so that data is protected and compliance can be verified at every step of the journey.
Self-service Data Marketplace
Perhaps no DataOps benefit offers greater advantages to efficiency, cost savings and AI enablement than self-service data marketplaces. Modern DataOps platforms are offering a shopping cart experience for all entities in the catalog, from standard data types to reports, files and other non-tabular formats. These marketplaces enable entities to be easily discovered, selected, and provisioned to any destination, such as a repository like Snowflake, to direct integrations with analytics tools, like Tableau. The self-service marketplace dramatically reduces the IT ticket burden, accelerates analytic outcomes, and lowers data costs.
A data catalog is only as useful as the descriptive details applied to each entity. As repositories grow, analysts face challenges finding the exact data source they need when entities are not accompanied by precise labels, tags, and notes. Additionally, building the ML-based suggestion tools and other features to recommend data to an analyst is most effective when these details are used to feed the models. In order to achieve data discovery and recommendation success, modern DataOps platforms have added customizable metadata fields, tags, and labels, which can be created by pre-permissioned analysts, data scientists, and engineers. Every company has differences in their data, including its use, source, or destination, and by bringing customizable fields into the catalog, analysts can use unique tags, labels, and notes to collaborate, easily find specific data sets, and recommend sets to their colleagues.
Cloud Agnostic Integrations
As enterprises use DataOps improvements to bring data from more sources, types, and formats into lakes and catalogs, their data ecosystems often need to integrate with a variety of data warehouses and storage platforms. Data stored with one vendor may be hosted on AWS, while another may be on Azure, and so on. In order to successfully unify data sources, discover any entity within the catalog, and provision to the sandbox or destination of their choice, companies need an extensible, single pane of glass DataOps platform that connects to every cloud or on-premises source and scales across new technologies over time.
Extensibility Across Existing Infrastructure
Extensibility is the crux of today’s DataOps platforms. To achieve streamlined, accelerated, optimized data ecosystems, transparency across the entire supply chain is essential. The only way to deliver complete data lineage, standardized enterprise-wide governance, and ML-based workflows and recommendations, is to have a platform that connects to every technology and vendor in the data ecosystem. The best DataOps companies are able to take extensibility one step further by enabling enterprises to keep what is working in their data architecture and replace only what is necessary. This “stay and play” approach to both data and vendors reduces costs, accelerates timelines, and often overcomes hurdles that have previously blocked data project success.
Modern DataOps success gives data engineers, stewards, analysts and their managers both the bird’s eye view across their connected data ecosystem and the ability to quickly execute on new data products and advanced analytics. A strong DataOps foundation scales as data use cases arise, making it essential for today’s highly data-driven enterprises.
About the Author
Susan Cook is CEO at Zaloni. Zaloni simplifies big data for transformative business insights. We work with pioneering enterprises to modernize their data architecture and operationalize their data to accelerate insights for everyday business practices. A leader in big data for more than a decade, Zaloni’s expertise is deep, spans multiple industries, and has proven invaluable to customers at many of the world’s top companies.
Featured image: ©Pablo Lagarto