Data is the powerhouse fueling innovation and strategic decisions that drive substantial growth across organizations and their data ecosystems. However, a lack of guardrails and governance, such as clear ownership, accountability, and oversight, can quickly make data a liability.
As organizations increasingly rely on historic, present and future data to power strategic decisions, artificial intelligence ventures, customize customer experiences, and achieve operational efficiency, it’s crucial that data remains accurate, trustworthy, secure, and leveraged in accordance with regulatory requirements. This is where data stewardship comes in.
What is Data Stewardship?
IBM defines data stewardship as a collection of data management practices designed to help ensure high data quality and accessibility. In short, it’s the practice of empowering data stewards (individuals who manage corporate data) and assigning them responsibility and accountability for effective data management. This includes ensuring top-notch data quality, implementing data privacy and security measures, and using data appropriately throughout its lifecycle.
Data stewardship helps organizations ensure their on-premises systems, cloud, SaaS applications, warehouses, and other data ecosystems are accurate, properly maintained, secure, and used in accordance with established corporate rules, data governance policies, and regulatory standards.
This makes data stewardship a critical operational component of a broader data governance strategy. While data governance administers organization-wide rules by establishing data-handling policies, usage standards, and accountability frameworks, data stewardship supports the implementation of those rules in everyday data management processes.
Types of Data Stewards
Data stewardship delegates responsibilities to data stewards, who are then categorized into distinct roles based on the organization’s requirements, functions, size, operating model, technical requirements, and level of data governance.
a. Business Data Stewards
Business data stewards are experts responsible for managing critical business data within a particular business department, such as marketing, finance, customer success, or human resources. They help identify and rectify data errors, form a unified language, define scope, and ensure data privacy and security.
b. Technical Data Stewards
Technical data stewards are IT and data professionals who specialize in managing the technical aspects of data, including how it is created, processed, stored, evolves over time, transferred, and integrated across data pipelines and other data environments. They collaborate with relevant stakeholders to implement governance requirements at a technical level.
c. Domain Data Stewards
Domain data stewards are domain experts who specialize in managing data-related activities within an enterprise data domain. This could include multiple departments, such as customer service, product marketing, third-party suppliers, employees, or finance. Like other stewards, they ensure top-notch data quality, accuracy, privacy, security, and transparency.
d. Enterprise Data Stewards
Enterprise data stewards manage the overall organization-wide data. They collaborate with other data stewards to implement data governance rules, policies, standards and frameworks.
Why is Data Stewardship Important?
Organizations today are collecting, processing, storing, and sharing massive volumes of data. This data rarely remains within on-premises systems or a single application; it travels across various cloud systems and environments, including business departments, analytics tools, hybrid cloud platforms, third parties, and, to top it all, AI systems.
Without data ownership and transparency about data locations, organizations struggle with data lineage, quality, accuracy, discovery, and classification, unauthorized access, and more, which heighten data exposure risks.
a. Improve Data Accuracy and Quality
Ensures data is accurate by establishing data standards, making data usage reliable. Proactively identifies and resolves data quality concerns to build trust in data for improved decision-making.
b. Establishes Accountability
Establishes clear responsibilities for handling sensitive data and makes data stewards accountable to address data-related issues so that data is reliable and fit for business use.
c. Strengthens Data Governance & Compliance
Data stewardship supports the development and implementation of data-use policies and standards to ensure regulatory compliance.
d. Supports Confidence in Decision-Making
Data stewardship empowers data owners to make strategic decisions while ensuring smooth operational management. This bolsters confidence that data usage, insights, reports, and decisions are backed by accurate, reliable, and trusted data.
Difference Between Data Stewardship and Data Governance
Data stewardship and data governance are closely related. However, they operate differently. Where data governance defines how organizations should manage data through policies, standards and frameworks, data stewardship takes a practical approach by implementing those rules in practice.
Data Governance
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Data Stewardship
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| Establishes clearly defined rules, policies, standards, frameworks, workflows, and roles for managing data. |
Enforces the data governance rules by implementing policies and standards across daily operations. |
| Emphasizes the broader strategic direction towards data management and an overall data security posture. |
Primarily focuses on the day-to-day operational management of data, ensuring top-notch data quality, lineage, accuracy, and reliability. |
| Defines the individuals and systems that have the authority and accountability over data. |
Data stewards ensure that assigned data responsibilities are carried out effectively. |
| Considers the holistic data approach by addressing data lineage, quality, privacy, security, accuracy, compliance, and ownership. |
Data stewards operationalize data governance rules by monitoring data lineage, ensuring top-notch data quality, privacy, security, accuracy and proper data usage. |
Put simply, data governance sets the organization-wide rules and direction, and data stewardship puts those rules and corporate direction into practice.
Data Stewardship Use Cases
Data stewardship supports a wide range of use cases, enabling organizations to operationalize data ownership and accountability and strengthen trust in data. Most common use cases include:
a. Data Quality Management
Having a massive pool of poor-quality data is of no use to the business, as it undermines business intelligence, leading to faulty operational processes and incorrect regulatory reporting.
With data stewards in place, organizations can establish uniform data quality standards across departments, identify data issues, collaborate with stakeholders to address root causes and minimize operational downtime.
b. Data Ownership and Accountability
Data is sprawling across digital platforms. From on-premises systems to cloud environments and AI tools, data is scattered across the digital landscape, often without guardrails and transparency into data ownership and accountability.
Data stewardship establishes guidelines and processes that define clear ownership, responsibilities, and accountability for maintaining data quality across datasets and ensuring data operates in accordance with governance policies throughout its lifecycle.
Data on its own lacks depth. Metadata provides context, structure and other details about data, such as what the data is, what it means, where it resides, how it is classified, who owns it, and how it should be used. The understanding and metadata standards might differ across departments.
Data stewardship helps establish a unified definition of metadata across the organization, enabling teams to find common ground and to discover, understand, govern, and use data appropriately.
d. Data Classification & Sensitive Data Management
Data discovery is the initial step to determining the existence of data across multiple data environments. Classification sorts and labels the discovered data into distinct categories (such as public, internal, confidential, or restricted) based on its sensitivity.
Data stewardship facilitates this management by building a structured approach to categorizing data based on its sensitivity, criticality, and regulatory requirements. This helps organizations ensure data is secured and leveraged as intended throughout its lifecycle.
Data is leveraged across multiple corporate domains but lacks clear ownership, lineage, metadata, and classification, resulting in poor data quality, gaps, limited context and visibility, and inadvertent data exposure.
Data stewardship addresses these concerns by managing data from its inception to its ultimate deletion, destruction, or disposal. Data stewards maintain a dedicated log that monitors data across its lifecycle and remediates issues at their source.
Challenges to Data Stewardship
Managing data across multiple data environments can often be challenging due to complex systems, tight security and access controls, meeting regulatory requirements, and more. This makes it challenging to implement data stewardship effectively. Common challenges include:
a. Data Quality
Ensuring data is accurate, up to date, complete, readily available, and compliant is no easy feat. As organizations collect, process, store and share massive volumes of data, a huge chunk can be incorrect, incomplete, duplicated, and outdated, resulting in inconsistencies and poor quality data.
b. Data Privacy and Security
Estimates suggest that roughly one-third of the total data organizations collect is sensitive or confidential. Protecting that data from unauthorized access, cyberattacks, data breaches, and misuse is a massive challenge, especially when adequate privacy and security measures are lacking.
c. Data Silos and Integration
As organizations grow, much of the data begins residing in silos and disparate systems. This makes it challenging to understand where data exists, what its properties are, and which systems it is integrated with. Lack of common grammar and visibility results in multiple teams working around the same data.
Best Practices for Data Stewardship
Effective data stewardship requires organizations to define clear policies, standards, rules, processes, and responsibilities that ensure data remains accurate, complete, and secure and accessible whenever required for intended purposes.
a. Establishing Clear Data Ownership
Without ownership, there’s no accountability and data governance. Organizations should define who is responsible for a particular data set. Data stewards should understand their role in data management and ensure that data is handled in accordance with corporate policies and regulatory requirements.
b. Maintaining High Data Quality
Processes should be implemented that periodically assess data quality. This helps ensure data is reliable, trusted, accurate and consistent. Any errors identified during quality standards assessment can be promptly corrected before poor-quality data is used for strategic decision-making.
c. Implementing Robust Data Privacy and Security Measures
Data is the lifeblood of organizations and an ultimate goldmine for cybercriminals. Robust privacy and security measures must be implemented to ensure sensitive data is always secured. This includes implementing access controls, encryption, impact assessments, and periodic security assessments.
Strengthening Data Stewardship with Securiti
Securiti helps organizations scale data stewardship by providing unified visibility and intelligence across complex data environments.
Securiti DataAI Command Platform enables teams to discover and classify data, understand metadata and lineage, identify risks, and automate governance, privacy, and security controls, helping data stewards maintain trusted, well-governed data across the enterprise.
Request a demo to learn more.
