In the world of AI and analytics, having the right data is everything. But to really make the most of it, data needs to be well-managed and flow seamlessly across systems. This is where enterprise-grade connected data management steps in, serving as the backbone for any project that relies on data. Even the best companies find it tough to get data into their AI models effectively. Addressing data deficiencies has emerged as a crucial step in the generative AI (GenAI)-specific demands of data architectures. To modernize their data-related capabilities, organizations are enhancing data security, improving data quality practices and updating data governance frameworks and/or developing new data policies, according to Deloitte.¹ That’s why good data management strategies are essential.

Connected data management is key to making sure data flows seamlessly across disparate systems. It helps break down silos and lets companies see a unified, holistic view of their data. This makes the data not only accessible but also ensures it is relevant, responsible and robust. However, getting connected data management right is no small feat. Often, companies struggle to put their data management plans into action with precision and agility. This also ends up slowing down the time to market with their GenAI apps and use cases. Common challenges include inadequate code-versioning capabilities, disjointed communication, a lack of automation and limited scalability and flexibility in their DevOps process.

Companies need to release products and features securely and with agility at scale. This requires a simplified release experience with a robust, collaborative and transparent DevOps framework that is easy to use and transparent. The right DevOps approach can operationalize data management projects and ensure data is accessible, accurate and reliable. It's the ultimate enabler, transforming how data is integrated, deployed and maintained, allowing your data management capabilities to work their wonders without a hitch. (See Figure 1.)

Some important facets of establishing a comprehensive DevOps framework are:

• Structured and organized teams for enhanced collaboration
• Easy integration with industry-leading versioning systems
• Simplified environment management for seamless upgrades
• Automation with best-in-class DevOps to support CI/CD

Let's examine how these features within the enterprise-grade, cloud-native and AI-powered data management platform, Informatica Intelligent Data Management Cloud™ (IDMC), benefit the organization.

The whole code development, testing and deployment process can be chaotic and soon turn into a nightmare if data management workflows and assets are not organized. You need a dynamic framework where enterprises can seamlessly manage and organize their data management assets across diverse teams and departments. With customizable organizational structures, businesses can use IDMC to design, deploy and govern workflows, ensuring secure collaboration through role-based access control on projects and folders. This ensures you only see what you should, in line with the principle of least privilege, helping with compliance. For global operations, separate regional organizations enhance data security and compliance, while centralized licensing streamlines administration. Large enterprises can take advantage of parent and sub-organizational hierarchies to efficiently manage workflows; OEM customers can effortlessly configure client sub-organizations under a single parent entity. Check out the following video (Figure 2) to learn about the best ways to structure your organizations.

DevOps teams often struggle to accelerate their CI/CD processes due to inadequate code versioning capabilities. This can make it nearly impossible to identify code changes and execute rollbacks in case of code issues. An enterprise-grade data management platform should have native integration with industry-standard version control systems such as Github, Bitbucket and Gitlab. This approach helps ensure that users with the appropriate privileges can push new versions or pull earlier versions of code with ease during the software development lifecycle.

IDMC provides robust source control management, simplifying versioning within a GitHub repository without leaving your development canvas. This allows full control over your repository while preventing vendor lock-ins. This plug-and-play feature is versatile and links your GitHub, Bitbucket or Gitlab repository to your IDMC organization within seconds, allowing users to check in, check out, pull, undo checkout and unlink actions directly from IDMC.

There is a growing need to simplify the management of production and pre-production environments, including development and testing, so that teams can address business needs quickly and easily. Identifying assets for team-based deployment should be straightforward since the last release.​ IDMC facilitates this by supporting code branches aligned with organization structure, allowing data management innovations across different departments or teams to continue with minimal impact. With both project-level and organization-level code branches, teams can easily collaborate at the time of development and deployment. This setup provides environmental isolation and controlled promotion of data management assets across environments, offering the much-needed flexibility to scale CI/CD processes.

With an increasingly frantic pace of innovation, maintaining a continuous development and deployment process is essential. However, the ongoing challenge of developing, testing and deploying code reliably and securely remains glaringly evident. To address this, it’s crucial to facilitate straightforward team communication and feedback through code reviews, approvals and built-in best practice rules within workflows.

IDMC supports agile development and deployment by placing collaboration at the center of the CI/CD process. Users can confidently design and test their assets continuously and iteratively, easily meeting timelines.
What makes all this possible? Automation. By reducing manual effort through automated testing and release processes, users can seamlessly develop, test and deploy with just a click of a button. And it does not stop there. Monitoring capabilities ensure observability and transparency, facilitating proactive issue mitigation strategies.

Many users rely on different tools and vendors for CI/CD. With well-documented and managed REST APIs, IDMC allows easy for easy integration and enables interoperability. These REST APIs can function with Cloud Application Integration (CAI), a service of IDMC, or with leading CI/CD automation tools like Jenkins, Github actions or Harness, to automate and enable continuous deployment.

As businesses navigate challenges to accelerate data management assets to harness the full potential of their data, the Informatica Intelligent Data Management Cloud and its robust DevOps framework provides a systematic approach to operationalizing data workflows. By ensuring versioning flexibility, seamless collaboration, environment management and automation-based continuous integration and deployment, IDMC addresses key pain points in managing complex data landscapes. The platform's native integration with leading 3rd-party applications further streamlines the development process, allowing companies to innovate with agility and confidence. Ultimately, IDMC empowers enterprises to break down data silos, achieve data security and compliance and maintain a competitive edge by getting their data ready for their AI journey.

Join us at Informatica World 2025 and learn about our latest innovation in the DevOps space.

Visit our Demo Center and stay up to date with IDMC with engaging demo videos.

Check out our documentation page to know more about IDMC’s DevOps capabilities.

View this on-demand webinar to learn about a cool in-product wizard that can help simplify your “Secure Agent” installation experience.