If the events of 2020 taught us anything, it’s that being able to make better decisions faster is critical for organizations that want to remain resilient as they confront unexpected challenges and accelerating rates of change. The global COVID-19 crisis highlighted the value that data and analytics-driven insights can bring when they’re available in near-real-time. These insights will enable business leaders to assess and manage novel risks, capitalize on shifts in the marketplace, and quickly adapt their core business strategies.
To achieve this end, however, decision-makers across multiple functional areas and business units need ready access to high-quality data that’s usable for analytics. In the real world, most data analytics pipelines aren’t up to the task.
DataOps was created to solve this problem. It assembles architectural patterns, best practices, technologies and workflows from agile development, DevOps and lean manufacturing into a new paradigm for building data analytics pipelines and solutions. It’s designed to increase automation and speed — and decrease errors and data defects — to give the business access to more reliable analytics output.
What is DataOps?
DataOps is an emerging methodology whose practitioners ask: What would happen if we approached the task of building data pipelines in the same way that we approach software development?
Just as DevOps broke down silos by bringing together development and IT operations teams, DataOps strives to infuse DevOps practitioners, data engineers and data scientists with a sense of common purpose: to improve the way that data is managed across the enterprise by creating better processes and structures to support data-driven decision-making.
According to the DataOps Manifesto, which is like an Agile Manifesto for data science and business intelligence, the purpose of DataOps is to generate actionable insights. DataOps teams are also expected to work together, embrace change, value customer satisfaction and collaboration, employ automation and orchestration wherever possible, and pay attention to quality, technical excellence and good design.
The DataOps methodology is grounded in rigor: formalized, repeatable steps are to be followed whenever possible, and continuous testing, monitoring and benchmarking is to be employed to facilitate efficiency and continuous improvement.
Resolving long-standing data analytics challenges
Historically, most data analytics pipelines were built in piecemeal fashion by heavily siloed teams. Data warehouses supporting general-purpose analytics and reporting were designed to be separate from financial reporting pipelines that supplied auditors and investors with numerical data. As organizations added new analytics solutions, including complex and data-intensive machine learning (ML)-driven applications, it was common practice to engineer a separate pipeline for each. The result was that there was little collaboration or re-use, and a great deal of inefficiency, manual effort and repeated labor. Errors were abundant, datasets often conflicted with one another, and cycle times were frustratingly long.
As a result, data scientists famously spend approximately 80% of their time cleaning, preparing and organizing data, leaving only 20% for the high-value exploratory and analytics activities for which they were actually hired.
Data architectures and pipelines are inherently complex. Before it’s ready for analysis, data must be captured, standardized, validated, cleansed, transformed, aggregated and catalogued – to name just a few of the tasks that data preparation workflows comprise. And, as organizations collect increasing amounts of data, each of these jobs becomes more and more challenging. Furthermore, organizations are deploying growing portfolios of data management tools to aid in completing these tasks. Many times, however, these tools – ranging from extract, transform, load (ETL)/extract, load, transform (ELT) solutions to data cataloging products – are administered and used by discrete groups who don’t collaborate with other stakeholders in the enterprise.
DataOps attempts to unify and standardize these processes and workflows to promote efficiency and minimize waste.
DataOps frameworks: streamlining and consolidating data pipelines
Data pipelines typically consist of three phases: data ingestion, data engineering and data analytics. In a DataOps framework, the activities within each phase are integrated into a single data supply chain that can source, refine and enrich data for consumption across the business.
To facilitate integration, many enterprises source all their data pipeline components from a single software vendor or cloud provider. Prebuilt data workflow automation platforms are also available: their vendors promise a single solution that will integrate existing data tools into a unified end-to-end workflow leveraging automation and orchestration to speed deployment, pipeline monitoring and testing. This provides a basic control center where complex data landscapes – including those complex multi-cloud or hybrid architectures – can be managed centrally.
Just as cloud-native practices and environments fit naturally within the DevOps philosophy, DataOps platforms are a good match for cloud data architectures. Cloud providers offer pre-built tools and managed services that can be used strategically to support data quality testing, version control, reuse and parameterization across multiple environments – all key facets of a DataOps framework. Together, these form the foundation for a new approach that can take your data analytics to a new level.
For more information on how Cloudreach can help you prepare your business to harness the power of its data and become more data-led, click here.