The Role of DataOps in Modern Data Management
DataOps is proving to be a game changer for data-led organizations across industries. It addresses the long-standing challenges faced by all stakeholders of organizational data management - the data creators, managers, and consumers.
Traditional data management, often riddled with complexity, could result in more costs, inefficiencies and even chaos across the data life cycle, instead of delivering on the promise of data-led decision-making.
Difference Between DataOps and Traditional Data Management
A successful DataOps practice that makes a concrete, long-term impact on data management performance and outcomes, is based on a deeper understanding of the inherent difference between DataOps and traditional data management. Traditional data management focuses on what needs to be done in data management, such as:
- Data ingestion
- Data integration
- Data preparation
- Data modeling
- Data quality
- Data governance
- Master data management
DataOps, on the other hand, focuses on how they are done, especially the processes and people involved in data management, including:
Collaboration and coordination across stakeholders to align and prioritize outcomes.
Data integration and workflow automation for optimal efficiency, speed, accuracy and scale.
An iterative approach to data analytics and product development for agility and innovation.
Incremental deployment and success monitoring.
Process measurements
Key Challenges DataOps Addresses in Data-Led Organizations
Most of the data-led organizations face the following challenges:
Disconnected data and applications: With business data and applications fragmented across silos, each function and department uses its applications and sees its own (limited) data as the ultimate truth. Customer service turns to call logs, sales teams rely on Salesforce data and marketing only leverages campaign data. As a result, the organization misses out on the compounding effect of connected data and applications for decision-making.
Lack of collaboration across data teams: When each team has a different agenda, there is no consensus on successful data management outcomes. IT wants to sort out data quality, security and access; data engineers want automated data pipelines so they can efficiently handle diverse requests for data sets; line of business users demand timely, accurate, and trusted insights for smarter decision-making; DevOps is focused on CI/CD pipeline for the data lake; and so on. This lack of collaboration and communication leads to inefficiencies, redundancies, and misaligned expectations.
Inefficient data integration workflows: When multiple data engineering teams build data pipelines using hand-coding or point solutions to connect and analyze diverse data, this can create operational inefficiencies, redundancies, data governance issues, and security vulnerabilities.
Lack of enterprise-wide capabilities: Most of the data engineering pipelines built for analytics and AI use cases don’t scale beyond the PoC stage due to a lack of automation, scalability and operationalization and don’t have the required framework to support enterprise-scale use cases.
Creating a Strategic Approach to DataOps and Data Management
DataOps is a proven success factor for data management, but without the right approach, it is at risk of becoming just another shiny new concept that adds to the complexity and inefficiencies around accessing, preparing, integrating and making data available as a strategic organizational asset.
When DataOps is built on strong strategy and execution foundations, it can greatly strengthen and elevate data management outcomes.
DataOps strategy is a systems thinking approach to managing the end-to-end data management lifecycle, streamlining processes and aligning stakeholders to a unified outcome.
Systems thinking is a way of making sense of the complexity by looking at any situation as an interrelated whole rather than by splitting it down into its parts and addressing each in isolation.
A systems thinking approach to DataOps similarly veers away from the traditional approach of addressing specific fragments of the data management process1. Instead, it takes a holistic view of the end-to-end data management lifecycle, including key processes such as data integration and automation; stakeholder collaboration and alignment; data product development and delivery; and data observability, governance and security.
As illustrated in Figure 1, systems thinking-led DataOps focuses on the inter-relationships and dependencies between the data processes, people, technology and end products that operationalize the enterprise data platform, rather than any one element in isolation, leading to compounded performance outcomes.
Figure 1. A connected approach to data management with a systems thinking led DataOps.
DataOps is neither a new product nor a new team. Although it may be less tangible than either of those, its impact on the efficient and effective integration and automation of data flows between all stakeholders has been concrete and highly measurable.
Measuring the Benefits of DataOps
By measuring the benefits of DataOps, businesses can not only validate their investment in DataOps practices but also drive continuous improvement and innovation. This can be done in the following ways:
Faster time to value of data management investments, with predictable delivery, data observability and reusable and scalable data models
Productivity improvements in terms of automation of key data integration processes
Improved data quality and more trusted business-ready data available for analytics
Fewer renamed and easily resolved service tickets
Lower error rates in reports generated from the data
Higher adoption of data self-serve products across data consumers
Cost and operational efficiencies across all data management workflows and processes
More innovative data analytics and predictive models to generate new business insights
Greater business impact from faster, more timely and accurate business decisions based on data-led insights and predictive analytics
How to Implement DataOps
Embedding DataOps capabilities into a unified data platform seamlessly operationalizes agile, automated and continuous data integration, delivery and deployment.
The full data management lifecycle is a multi-step process involving continuous data integration, delivery and deployment loops.
At traditional organizations, the various data management capabilities required at each stage of the lifecycle are either managed manually or with custom-built ad-hoc solutions. Some point solution vendors also offer specialized one-off capabilities in limited areas such as orchestration, observability or deployment automation.
However, a systems-thinking-led DataOps shows us how such a fragmented approach would create more complexities and challenges than value.
Adopting a unified cloud data management platform simplifies and streamlines data management processes and aligns with the holistic approach to data management advocated by systems thinking. Unlike hand-coding or point solutions, this unified approach weaves DataOps capabilities into the platform and enables you to share, deliver and democratize data across your business lines and other enterprises with a foundation in governance and privacy.
Next Steps
DataOps offers a new approach to creating and operationalizing data analytics in an increasingly complex data management environment. To learn more about how DataOps baked into your unified data management platform can leapfrog your data analytics outcomes, get our white paper, “Lead Your Data Revolution: Unlocking Potential with AI, DataOps and MLOps.”
