Data Governance with Dataiku

Data Quality Is Essential to Data Governance

To address data quality, Dataiku’s visual data flow gives full traceability of data from source to final data product, while visual recipes empower users to quickly cleanse, deduplicate, and transform data. Plus, with IT-delegated self-service capabilities, Dataiku empowers every team member to proactively address and improve data quality challenges, avoiding impact on insights. 

Further, data isn’t static — it’s transformed, cleansed, deleted, etc. by different users for different purposes. Teams should have a way — like they do in Dataiku — to build audit trails and data lineage throughout the entire lifecycle, with all users who interact with data (such as internal data) so that the right people are accountable. This also ensures that data is fully traceable for troubleshooting and compliance with internal controls and external regulations.

Centralize Data and Share With the Dataiku Data Catalog

To amplify the value effect, data must be accessible and utilized. This entails creating a reliable, centralized data catalog where trusted data is readily available for reuse and exploitation by as many individuals as possible.

The Dataiku Data Catalog is a central place for analysts, data scientists, and other data owners to share and search for datasets across their organization. This key step will empower other business departments to derive greater insights, build data products, and unleash the true potential of data, culminating in a profound appreciation for data within an organization.

Extend Data Governance Across the Data Lifecycle

In the world of data governance, there are many data governance tools available at different points of the data lifecycle, from data cataloging to policy management and threat detection.

To expand your enterprise data sharing strategy, Dataiku is designed to easily integrate with your data governance software or tech stack so that you can benefit from Dataiku’s unified analytics and AI platform capabilities — from data preparation to model deployment — without disrupting your current strategy. For example, through APIs and plugins, you can easily connect to metadata management and popular enterprise cataloging tools such as Alation or Collibra. 

From Data to AI Governance: AI Governance Is the New Black

Ensuring effective data governance is crucial in orchestrating and democratizing data across the enterprise. Yet, what about the AI-powered apps at your fingertips? What about the internally built or leveraged models (like LLMs) that drive predictive AI? 

While pure data governance solutions have become widespread in recent years, governance is increasingly transforming beyond the strict perimeter of datasets. It is extending to that of data products such as models, analytics, and soon LLMs to cover all types of AI products, whether generative or descriptive.

Dataiku Govern is a single place where data and analytics leaders and project managers track the progress of multiple analytics and AI initiatives and ensure the right workflows and processes are in place to deliver Responsible AI.

While data governance initiatives and AI Governance both have the same underlying goal, the latter squarely focuses on scaling AI — from technical issues around data quality and ML model maintenance to overall inefficiency, opacity, and risk associated with growing AI initiatives.

Balancing the Defensive & Offensive Approach of Data Governance

Even if data governance can take on different meanings depending on the regions and organizations in which it is deployed, be careful not to focus on governance for governance’s sake. The data governance team must help minimize risks while democratizing uses and serving business needs. It’s a balance each Chief Data Officer needs to strike to simplify data owners’ efforts and meet the data consumer needs. 

To be successful, governance must serve the business and become offensive. It is the link between the data governance teams and the data owners, who have the business expertise to assess whether the data is correct, validated, and certified. This balance is essential to the success of your data governance strategy.