How to Implement Agile DataOps — A Strategic Move for Future-Proofing Your Organization

How to Implement Agile DataOps — A Strategic Move for Future-Proofing Your Organization

In the fast-evolving landscape of data management, the mastery of Agile DataOps (ADO) emerges as a critical cornerstone for organizations aiming to navigate the complexities of modern data ecosystems.

Seasoned data professionals find themselves at the forefront of this transformative journey, where refining the orchestration of data-flows and processes is not just an option but a strategic imperative, especially if they wish to embrace advanced analytical techniques like AI/ML.

Embracing ADO transcends the mere optimization of operations; it embodies a commitment to innovation and a pledge to redefine the standards of efficiency and competitive advantage. As custodians of this digital renaissance, your role is pivotal in harnessing data's potential to chart a course toward sustained organizational excellence.

The Producer/Consumer data model

Frameworks and models exist to help establish order and standardization. There are some that are quite complex, and probably unnecessarily so. The producer/consumer data model, serving as the bedrock of the Agile DataOps process, mirrors the decentralized tenets of Data Mesh, advocating for the treatment of data as a product.

This model emphasizes the nuanced roles and responsibilities aligned with Data Mesh principles, ensuring data is not just produced and consumed blindly, but managed with precision to cater to the dynamic needs of various stakeholders.

Spotify is often cited as a leader in the real-world implementation of Data Mesh and other advanced data methodologies. In a review on Data Mesh Architecture and its Impact, several examples explored the producer/consumer relationship at scale. Consider Spotify’s application of domain-oriented decentralized ownership, a principle intrinsic to Data Mesh.

Here, data “producers” within domain squads are designated for the end-to-end management of their data products, much like in Agile DataOps. Spotify’s approach underscores the significance of accountability and domain-specific knowledge in efficiently producing high-quality, relevant data products.

In the same vein, LinkedIn and Zalando have embraced similar paradigms, decentralizing data ownership and enabling domain teams to 'produce' and 'consume' data autonomously. These implementations highlight the power of giving domain-specific teams the tools and autonomy to manage their data assets effectively.

A key challenge within the producer/consumer model is maintaining data quality and ensuring appropriate access. This is where the principles of Data Mesh offer profound insights. By adopting a self-serve data infrastructure and establishing clear data contracts (as employed by companies like ThoughtWorks) organizations can ensure data is not only accessible but also meets the requisite quality standards before consumption.

The success stories of Netflix and Zalando in adopting principles akin to the producer/consumer data model underscore the potential for organizational cultural transformation.

By emphasizing data as a product and prioritizing domain-specific data management, these companies have demonstrated the feasibility of a more agile and responsive approach to data operations. The amalgamation of Agile DataOps with Data Mesh principles provides a strategic framework for navigating the complexities of modern data ecosystems.

By embracing the producer/consumer data model, underscored by real-world examples, data leaders and practitioners can drive their organizations towards a more efficient, adaptive, and collaborative data management paradigm. Agile DataOps ensures a process is in place for data set intake, metadata management, change management, data transparency, data lineage, and data sharing.

Additionally, the model ensures a faster path to cultural change.

Agile DataOps (ADO) as a Service

The success of Agile DataOps (ADO) hinges not only on technological platforms, such as a data catalog rich in content and bolstered by well-defined processes but also on a holistic approach that integrates the human element — data stewardship and literacy programs play pivotal roles here.

Recognizing ADO as a socio-technical challenge, akin to models like Data Mesh and Data Fabric, underscores the need for a trifecta of people, processes, and technology. 

Managed and measured ADO practices, when augmented with robust data stewardship and literacy programs, create an ecosystem where data services are not just aligned but optimized for impact. Data stewards, tasked with the care and governance of data assets, become the linchpin in this strategy. They ensure data is accurate, accessible, and secure, bridging the gap between technical capabilities and business needs.

Moreover, a strong emphasis on data literacy across the organization empowers all individuals — from engineers to managers, and from sponsors to users. Literacy programs designed to elevate understanding of data principles, tools, and analytics drive informed participation in the governance process. Decisions are based on a solid understanding of data’s value and potential, thus leading to more effective usage and innovation.

Leadership, steering committees, and working groups play a critical role in setting the stage for this integrated approach. They establish policies and standards that underpin data access, usage, and controls among other foundational elements. However, the effectiveness of ADO relies on creating an environment where data users, stewards, and all stakeholders are not only transparent about the data lifecycle but are also engaged and knowledgeable participants in the governance framework.

Operationalizing standards, monitoring adherence, and driving accountability are essential functions that require concerted effort from the entire organization. Data stewardship and literacy programs are indispensable in this endeavor, as they cultivate a data-centric culture where every member understands the significance of their role in data management and governance.

Fostering collaboration across Agile DataOps (ADO) roles

In the Agile DataOps (ADO) framework, the symbiotic relationship between roles and responsibilities is pivotal for the adept management of data products. This intricate web of tasks includes but is not limited to:

  • Crafting schemas and models that elucidate the data's semantics and usage guidelines.

  • Anchoring data in its context, leveraging insights from various authoritative sources to enrich interpretation.

  • Overseeing the data catalog, ensuring efficient data ingestion and distribution mechanisms are in place.

  • Implementing agile methodologies to ensure the continuous delivery of reliable data through iterative refinement.

The Data Product Manager is central to this ecosystem; a linchpin in guaranteeing the integrity, timeliness, and accessibility of data within their domain. Beyond mere oversight, this role demands a proactive approach to managing the data landscape, employing advanced metadata and data management tools that facilitate the systematic curation of data products.

Data Product Managers become cross-functional conductors of an orchestra, orchestrating the talents of diverse teams comprising data engineers, stewards, analysts, and scientists. As a result, data products are not only infused with domain-specific expertise but also evaluated through various lenses, enhancing their quality and applicability.

Such collaboration extends to the utilization of automated data lineage tools, which equip each team with a unified, comprehensive understanding of the data’s journey through the analytics lifecycle. This shared visibility fosters an environment where tedious, manual tasks are minimized.

The integration of cross-functional teams is instrumental in the implementation of global Data Governance policies. Through the collective effort of all involved, anchored by the stewardship of Data Product Managers, ADO practices adopt a holistic approach toward governance.

This is evidenced by the strategic use of end-to-end Key Performance Indicator (KPI) monitoring to oversee and drive change management processes, ensuring adherence to these policies across diverse and decentralized domains, be it in Mesh or Fabric frameworks.

In essence, the ADO framework thrives on the premise of collaborative dynamism, where the convergence of varied expertise and perspectives breeds innovation. It places a premium on roles that facilitate cross-functional integration, leveraging the collective intelligence and agility of teams to navigate the complex data landscape efficiently.

Just-In-Time data sharing

Within the data quality (DQ) and master data management (MDM) ADO frameworks, Just-In-Time Data Sharing ensures that key, trusted data elements and feeds are made available to end users as and when needed while preserving the importance of data quality, integrity, and security of the data.

Just-in-time data sharing emphasizes timely access to data without compromising data security and quality. It involves implementing mechanisms such as data access controls, encryption, and authentication protocols to protect sensitive information.

How does the DQ/MDM framework support data sharing? A robust ADO platform brings the policies, standards, procedures, and controls to ensure that data is properly managed throughout its lifecycle. Data Governance focuses on the establishment of controls and measurement methods for data quality, integrity, and security, enabling organizations to define and enforce standards, guidelines, and best practices for data handling.

The MDM component of the framework ensures that data is integrated, consolidated, standardized, and synchronized across systems and sources, and establishes a single source of truth for key data elements, reducing redundancies and inconsistencies. Through their MDM, organizations can define and manage data-sharing rules and policies, specifying who can access specific data elements and under what conditions.

“Just-in-time" data sharing is a desired deliverable within a DG and MDM framework that enables organizations to strike a balance between data accessibility and data security. By implementing robust Data Governance practices, organizations can define and enforce standards while establishing data-sharing rules. Underpinning the DG framework, MDM facilitates the consolidation and synchronization of data, ensuring a single source of truth.

Agile DataOps and artificial intelligence

Various forms of Artificial Intelligence have enabled a wide range of operations since before Y2K, but new developments in Generative Artificial Intelligence (GenAI) and machine learning (ML) are bringing AI to the market and are already revolutionizing data management and governance practices.

Agile DataOps frameworks must be developed or modified to help control and automate routine but intricate tasks like zero-trust access controls, data classification, quality control, and compliance checks, thereby optimizing the utilization of valuable data assets.

Machine learning algorithms can analyze vast amounts of data at high speed, classify them based on defined parameters, and subsequently ensure they meet specific quality standards. This not only optimizes processes but also mitigates the risk of human error, ultimately enhancing accuracy and efficiency in data governance.

AI and machine learning can revolutionize data privacy and security measures, two critical components of data governance. For example, AI systems can use predictive analytics to identify potential cyber threats or breaches by analyzing patterns and anomalies in data usage.

When unusual activity is detected, the system can raise alerts and even initiate protective measures.

AI can also play an integral role in enforcing compliance with mandated privacy regulations like GDPR and CCPA by accurately identifying and tagging sensitive information, automating consent management, and tracking data processing activities.

A Data Governance framework can further leverage AI by allowing it to provide comprehensive, data-driven views of how data flows and is consumed across an organization. By identifying data bottlenecks AI can help in refining Data Governance policies for better alignment with business goals.

The challenge with these new AI tools is that they still often depend upon statistical models that aren’t always accurate, or make incorrect assumptions based on the available data. These errors may not always be immediately apparent due to the “black box” nature of many of these tools.

With proper Agile DataOps tooling and support, the downside of AI can be turned into a much more transparent, auditable, and explainable outcome. This will require a paradigm shift in both culture and technical approach (e.g., incorporation of a knowledge graph that can yield 3x the accuracy of LLM systems over relational data alone), but the efficiency benefits are absolutely unparalleled. 

The incorporation of AI technologies and predictive capabilities is a key consideration when designing a holistic Data Governance framework. AI technologies will begin to enable organizations to improve day-to-day operations and anticipate future data requirements, future-proofing the Data Governance framework.

ADO and DevSecOps

While worthy of its own discourse and paper, this article would be remiss if it didn’t mention DevSecOps and its importance to a fully encompassing Data Governance framework.

In short, the goal of DevSecOps involves creating a “security-always” culture with ongoing, flexible collaboration between release engineers and security teams.

It's an evolution of the DevOps philosophy and is sometimes described as "shifting security left" to address security concerns early in the DevOps lifecycle rather than at the end. Security is a shared responsibility from start to finish.

When successfully integrated with ADO, DevSecOps can provide numerous benefits including:

  • Automated Security Checks: Automated tools can be used within the continuous integration/continuous deployment (CI/CD) pipeline to check for security issues during the development process. Examples of checks could include static code analysis, dependency checking for known vulnerabilities, and dynamic application security testing (DAST).

  • Early Detection of Vulnerabilities: By integrating security into the development lifecycle, vulnerabilities can be identified and addressed earlier. This is much more efficient than the traditional approach of discovering security issues after deployment.

  • Coordinated Incident Response: In case of any security breach or incident, having a DevSecOps approach allows for a quick, coordinated response, leveraging established protocols and procedures for rolling out patches or fixes.

  • Ongoing, Continuous Monitoring and Compliance: Continuous monitoring is a critical aspect of DevSecOps. By continually monitoring applications and infrastructure, security incidents can be identified and resolved quickly. Additionally, automated compliance checks can ensure that data management practices meet all relevant regulations and standards.

We propose shifting ADO left and within the DevSecOps framework, because, after all, technology is all about the data. This new paradigm pushes data requirements to the very left of the DevSecOps framework into what we now call DataDevSecOps.

Typically, data requirements are defined by users or analysts and interpreted by developers and engineers, but now we know we need to inform the developers of the complete data source, data processing, metadata, lineage, and quality requirements before coding begins, typically in a catalog that includes provenance, lineage, full metadata (remember, no littering), and quality requirements.

The incorporation of Artificial Intelligence (AI) and machine learning into Data Governance practices offers immense potential for accelerating routine tasks, such as data classification, quality control, and compliance checks, and AI analytics can enhance data privacy and security measures.

As AI technologies are developed and adopted, organizations gain comprehensive, data-driven insights and automate ADO and Data Governance strategies.

The concept of "just-in-time" data sharing within a Data Governance (DG) and master data management (MDM) framework strikes a balance between data accessibility and security. Robust Data Governance practices leveraging MDM should enable the establishment of access control frameworks and data sharing rules and policies while ensuring data quality, integrity, and security.

ADO should eliminate the long life cycles typical of data governance programs as innovation drives a streamlined governance model empowering users to incorporate governance and stewardship into normal daily workflows.

Finally, DataDevSecOps, an evolution of the DevOps philosophy, plays a pivotal role in the ADO framework. By creating a “data first, security-always" culture and fostering collaboration between stewards, analysts, developers, operations, and security teams, DataDevSecOps ensures that responsibility is shared throughout the development lifecycle.


As we stand on the threshold of a transformative era in data management, the imperative for Chief Data Officers, their teams, and fellow executives to adopt and champion Agile DataOps principles has never been more pressing.

The adoption of ADO marks a pivotal shift towards, not merely enhancing operational efficiencies, but fostering a holistic culture of innovation grounded in data-driven decision-making. This journey towards adopting ADO is an investment in your organization's future, promising to unlock unparalleled insights and operational agility that can propel your enterprise to new heights.

It is a strategic move—a commitment to excellence and leadership in the digital domain. Let this moment be the catalyst for change, both technically and operationally, as you lead your organization in embracing the transformative power of Agile DataOps. The future of data management is here, and it depends upon the leadership and effective implementation to shape its direction.

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Authored with credit to Diane Schmidt, Ph.D., Chief Data Officer, Government Accountability Office, Justin S. Magruder, Ph.D., Chief Data Officer, Science Applications International Corporation, Daniel Pullen, Ph.D., Chief Data Scientist, Science Applications International Corporation, and Steve Sieloff, VP of Product Development & Thought Leadership, Vérité Data.

About the Author:

Patrick McGarry is the General Manager for US Federal at, bringing a rich background in open source and open data, establishing partnership organizations, and spearheading startup ventures. His entrepreneurial spirit is complemented by his strategic and advisory roles across the industry and at organizations like the Data Foundation.

McGarry is also a frequent contributor of thought leadership in the data ecosystem, as a frequent speaker and content creator, delving into a spectrum of topics from data and open source, to community building, agile data operations, and data governance. His expertise not only drives innovation and growth at but also shapes the broader dialogue on harnessing data for transformative impact.

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