Why Data Operations Platforms Can’t Survive without Governance

Why Data Operations Platforms Can't Survive without Governance

We explore the three pillars of governance: trust, interoperability, and collaboration.

Data operations (DataOps) is a data management methodology that improves an organization's efficiency and profitability by applying agile techniques to the workflows that derive business value from data. It leverages a combination of processes and technologies to reduce data friction, improve business agility, and increase security, integrity, and reliability throughout the data pipeline. However, to achieve increased productivity, a DataOps system has to ensure that the data is governed throughout its life cycle as it moves through increasingly complex pipelines and analytics workflows.

Data governance is another data management concept that focuses on the necessary people, processes, and technologies to ensure the availability, usability, integrity, and security of enterprise data, based on internal data standards and policies. Effective data governance ensures that data is consistent, trustworthy, and appropriately used by data consumers. Data governance focuses on the organizational strategies, roles, and policies that define who can take what action on what data, in which situations, using what methods. This governance framework is then operationalized and executed within the organization's data operations.

The three pillars of governance are trust, interoperability, and collaboration.

Mechanisms for trust include but are not restricted to:

Access control: Different data types have different sensitivities that determine how and with whom the data can be shared. For example, open data can be shared widely while sensitive data is typically restricted to select roles in an organization. A DataOps environment that supports fine-grained data access controls ensures trust by making sure that data will only be accessed according to set policies.

Access restrictions can also apply to software and applications because much of it comes from third-party providers. Being able to sandbox these programs so they only access the data they are approved for and share the results according to policies will also help establish trust.

Authentication and identity management: Authentication and identity management is essential for data Access control, and a prerequisite for any system that helps organizations ensure trusted data operations. As data from Internet of Things (IoT) devices becomes more prevalent in DataOps environments, ensuring the provenance of this data is increasingly important to ensure trust. Extending authentication and identity management to connected devices that actually contribute data is an essential element in establishing data provenance. A unified system brings together data, devices, and users under a single umbrella and increases overall data security resilience.

Audit trails: For all the partners in the ecosystem, including regulatory agencies and oversight groups, the ability to prove that certain actions have been performed will be a core requirement. Auditing capabilities will be essential to providing transparency to the appropriate stakeholders, which in turn promotes greater levels of trust.

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