Aspiring new role in professional career? — Learn why “data owner” role is booming in tech

In the contemporary digital ecosystem, data stands as one of the most valuable assets for organizations across industries. From driving informed decision-making to fueling innovation, data has the power to transform businesses and lead them towards a sustainable future. Within this context, the role of data owners in data platforms is pivotal. Data owners are individuals or entities responsible for the stewardship, management, and quality of a particular set of data within an organization.

Before we start understand about key responsibility of data owner and soft skills/ hard skills requires to get ready for this role, let’s first understand what an organization can do to better organize their data. Once the framework is set up, Data owner plays an important role to ensure consistent evolution of data products within their domains.

1. Delineate Domains: Recognize the distinct domains within the organization that handle various data categories. These might include functional areas like finance, marketing, and HR, or technical sectors such as data engineering, data science, and analytics.
2. Allocate Domain Teams: Assign specialized teams to each identified domain, aligning them with their expertise and the data they oversee. These teams should have control over their respective domain-specific data and should be capable of crafting data products that cater to their stakeholders’ requirements.
3. Specify Data Products: Collaborate with the domain teams to outline the data products they are expected to deliver. These products should be tailored to address specific organizational needs and should aim at contributing value to the organization.
4. Institute Data Governance: Implement comprehensive data governance policies and procedures to regulate data management and sharing across domains. This step ensures that data is utilized efficiently while safeguarding the organization’s data assets.
5. Develop a Data Mesh Platform: Construct a platform that facilitates the development and distribution of data products by domain teams. This platform should equip teams with the necessary tools and infrastructure for agile development and delivery of data products, while simultaneously upholding data quality and security norms.

Data ownership — One of the big misconception in the industries we have currently is defining / maintaining data product = data ownership.

No it’s not! Ownership of data requires way more responsibilities then just defining filters to create data products. Let’s understand below framework to better define key responsibilities data owner needs to ensure before publishing data product for the business :

Legal Responsibilities:

Compliance with Data Protection Laws: Data owners must ensure compliance with data protection regulations such as GDPR, CCPA, HIPAA, etc. This includes ensuring data is collected, processed, stored, and shared in accordance with legal requirements.

Intellectual Property Rights: Data owners should respect and adhere to intellectual property laws, ensuring that any data used or shared has proper permissions or licenses.

Data Retention and Deletion: They must comply with legal requirements regarding how long data can be stored and ensure proper mechanisms for data deletion or anonymization when necessary.

Data Security: Legal frameworks often require that adequate security measures are in place to prevent data breaches. Data owners must ensure that data is encrypted, access is controlled, and security protocols are regularly updated.

Data Sharing Agreements: When data is shared with third parties, data owners must ensure that legal agreements, such as Data Processing Agreements (DPAs) or contracts, are in place and adhered to.

Ethical Responsibilities:

Privacy and Consent: Data owners should prioritize user privacy, ensuring that data is collected and used only with proper consent and for the purpose it was intended.

Transparency: Ethical considerations dictate that data owners be transparent about how data is collected, used, and shared. Users should be informed and have an understanding of how their data is being utilized.

Data Quality and Integrity: Data owners have an ethical responsibility to ensure the quality and integrity of data, avoiding any manipulation or misrepresentation of data that could lead to misinformation or harm.

Fairness and Bias: Ethical use of data also includes taking steps to prevent and mitigate biases in data collection, processing, and analysis, ensuring fairness and avoiding discrimination.

Responsible Innovation: Data owners should consider the broader societal impacts of data use and ensure that innovations driven by data do not inadvertently cause harm or widen inequalities.

Sustainability: Ethical considerations may also extend to ensuring that data storage and processing practices are environmentally sustainable.

By adhering to these legal and ethical responsibilities, data owners in a data mesh context can ensure that data is managed in a manner that respects individual rights, complies with laws, and upholds ethical standards.

Business vision, Finance and grooming business requirements:

Let’s put all 3 boxes in single bucket as these three goes hand in hand but requires close engagements with many personas.

Business vision : close tie with strategic partners to define now v/s future 
Finance : Understand budget from the stakeholders / product owners and planning with Architects to understand cost to implement end to end. 
Business requirements : discussion with product owner to understand requirements / discussions with analyst or experts to understand data need to measure outcome of the project / discussion with tech team to understand possible scenarios or data points “must v/s can”

Prioritize Requirements: Data owners need to work closely with stakeholders to identify and prioritize data requirements based on their importance to the organization’s goals and strategies.
By categorizing requirements as “must-have”, “nice-to-have”, and “future consideration”, data owners can allocate budget effectively.

Cost Estimation and Budget Alignment: Assess and estimate the cost associated with each data requirement including data acquisition, storage, processing, and management.
Align these costs with the available budget to ensure feasibility.

Optimize Resources: Data owners can look for ways to optimize existing resources. This can include optimizing storage, using open-source tools, or leveraging cloud services that allow scalability without large upfront costs.
Analyzing and optimizing data processing pipelines for efficiency can also lead to cost savings.

Continuous Monitoring and Reporting: Implement continuous monitoring of expenditure against budget allocation.
Regular reporting and real-time dashboards can help keep track of spending and ensure that budget caps are not exceeded.

Leverage Partnerships and Negotiations: Data owners can negotiate contracts and licensing agreements with vendors to better align with budget constraints.
Exploring partnerships or shared resources can also sometimes offer cost-effective solutions.

Agile and Incremental Development: Adopting an agile approach allows for incremental development and release of data products. This way, the most critical features can be developed first, ensuring that the budget is spent on the highest priority items.
This approach also allows for flexibility and adjustment based on feedback and changing requirements.

Future-proofing and Scalability: Design solutions with scalability in mind, ensuring that data platforms can grow incrementally without requiring significant reinvestment.
Consider potential future requirements and ensure that they can be accommodated within the existing infrastructure with minimal additional cost.

Engage in Cost-Benefit Analysis: Assess the return on investment (ROI) of each data requirement. Sometimes, spending more in the short term can lead to long-term savings and benefits.
Evaluating the potential business value versus cost can help in making informed decisions.

Stakeholder Communication: Keep open lines of communication with stakeholders to manage expectations and ensure that any changes in requirements are understood and agreed upon.

Ensuring technological standardization:

Ensuring technological standardization within a data mesh architecture is crucial to maintain coherence, interoperability, and efficiency across different domains. Here are some responsibilities of a data owner in this context:

Establishing Data Standards: Common Data Formats: Data owners should ensure that data is stored and exchanged in standardized formats (e.g., JSON, XML, CSV) to ensure compatibility across domains.
Metadata Standards: Define and enforce standards for metadata to ensure that data is well-described, discoverable, and understandable.

Technology Stack Harmonization: Unified Technologies: Advocate for the use of a consistent set of technologies (e.g., databases, programming languages, tools) across domains when possible.
API Standards: Ensure that APIs used for data access and integration follow standard conventions, such as REST or GraphQL, for consistency and ease of integration.

Integration and Interoperability: Data Integration Guidelines: Develop guidelines to ensure that data from different domains can be integrated seamlessly.
Interoperability Standards: Promote standards that ensure different systems, applications, and data sources can work together cohesively.

Quality Assurance and Validation: Data Quality Standards: Implement and enforce data quality standards to ensure consistency, accuracy, and reliability across the data mesh.
Validation Protocols: Standardize protocols for validating data and processes to ensure that they meet the requisite quality benchmarks.

Security and Compliance Standards: Security Protocols: Ensure that security measures, such as encryption and authentication, are standardized across the data mesh.
Compliance Adherence: Make sure that all data handling and processing practices align with legal and regulatory standards.

Infrastructure and Architecture Consistency: Infrastructure Guidelines: Standardize infrastructure components (e.g., cloud services, network configurations) to ensure uniform performance and scalability.
Architectural Standards: Define and enforce architectural best practices across the data mesh.

Documentation and Knowledge Sharing: Documentation Standards: Implement consistent documentation practices to ensure that technical processes and data are well-understood across domains.
Knowledge Repositories: Establish centralized repositories for sharing standards, best practices, and guidelines.

Continuous Improvement and Adaptation: Technology Evaluation: Regularly assess and evaluate the technology standards to ensure they are up-to-date and meeting organizational needs.
Feedback Loops: Establish mechanisms for continuous feedback from different domain teams to iteratively improve and update standards.

Training and Skill Development: Capacity Building: Ensure that team members are trained and aligned with the technological standards established for the data mesh.
Skill Standardization: Promote a consistent level of skills and knowledge among teams to ensure adherence to standards.

Collaboration and Stakeholder Engagement: Cross-Domain Collaboration: Foster collaboration among different domains to ensure that tech standards are accepted and implemented uniformly.
Stakeholder Communication: Regularly communicate with stakeholders to ensure alignment and understanding of the tech standards.

By taking responsibility for these aspects, data owners can ensure technological standardization within a data mesh, leading to a more coherent, efficient, and effective data ecosystem.

Definition of data product and key responsibilities to make it’s use more impactful for consumers:

Define and Document the Business Context:

Business Definition: Clearly define the purpose, scope, and value proposition of the data product for the organization and stakeholders. This should align with business goals and strategies.

User Stories and Use Cases: Develop user stories and use cases to provide context on how the data product will be utilized by stakeholders.

Maintain a Comprehensive Data Catalog:

Metadata Management: Ensure that the data catalog contains comprehensive metadata for each data product, including descriptions, data types, source, and usage information.

Catalog Accessibility: Make the data catalog easily accessible and understandable for all relevant stakeholders, ensuring that technical jargon is complemented with clear business terminology.

Search and Discovery: Implement search functionality and proper categorization within the data catalog to facilitate easy discovery of data products.

Standardize Business Definitions:

Common Vocabulary: Establish a common vocabulary and standard definitions for business terms and metrics associated with data products to avoid confusion.

Data Dictionary: Maintain a data dictionary that provides definitions, context, and relationships of data elements in clear, business-friendly language.

Ensure Data Quality:

Quality Metrics: Define and monitor key data quality metrics such as accuracy, completeness, consistency, reliability, and timeliness.

Quality Assurance Processes: Implement data quality checks and validation procedures to ensure that the data adheres to the set standards and business definitions.

Feedback Mechanism: Establish a mechanism for stakeholders to report data quality issues and ensure timely resolution.

Document and Manage Data Lineage:

Traceability: Document and visualize data lineage to trace how data is sourced, processed, and consumed within the data product.

Impact Analysis: This traceability helps in impact analysis and understanding how changes in data sources or processes might affect the data product.

Engage with Stakeholders:

Collaborative Definition: Collaborate with stakeholders to define and refine business definitions ensuring that they accurately reflect the needs and perspectives of different users.

Communication: Regularly communicate updates and changes in business definitions, data catalog entries, and data quality status to stakeholders.

Implement Version Control:

Documentation Versioning: Implement version control for all documentation related to business definitions and the data catalog to track changes over time.

Data Product Versioning: Ensure that different versions of a data product are clearly documented and cataloged.

Continuous Improvement:

Iterative Enhancement: Continuously seek feedback from stakeholders to improve business definitions, data catalog entries, and data quality processes.

Proactive Monitoring: Implement proactive monitoring to identify and rectify data quality issues before they impact stakeholders.

Training and Knowledge Sharing:

Knowledge Base: Maintain a knowledge base with FAQs, guides, and tutorials related to data products, business definitions, and data quality practices.

Training Sessions: Conduct regular training sessions for stakeholders to familiarize them with the data products, business definitions, and usage.

Governance and Compliance:

Data Governance: Ensure that all data management practices align with organizational data governance policies and compliance requirements.

Audit Trails: Maintain audit trails for changes in data definitions, data quality checks, and catalog updates.

By managing these aspects meticulously, data owners can ensure that the business definition of a data product is well-aligned with stakeholder needs, and that the data catalog and data quality are maintained to the highest standards.

In conclusion, data ownership is not just a role but a strategic imperative for organizations aiming to thrive in the data-driven era. By ensuring above described framework and key responsibilities, Data Owners serve as the custodians of an organization’s data assets, unlocking value and driving growth.