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Unstructured Data Management for AI

Komprise empowers developers and architects with unstructured data management for AI, multi-cloud, and compliance to drive innovation.



As the volume of unstructured data continues to grow at an unprecedented rate, organizations face new challenges in managing this data while keeping costs under control and extracting value for AI and machine learning applications. I recently had the opportunity to catch up with Krishna Subramanian, co-founder and COO of Komprise, to discuss how the company has been making life easier for developers, engineers, and architects since our last conversation in February 2022.


Addressing the Challenges of Unstructured Data Growth

Subramanian highlighted that the biggest challenge with unstructured data is managing its explosive growth while budgets remain flat. This problem has become more urgent over the past year as data growth accelerates. Komprise enables organizations to manage their unstructured data estate by analyzing all their data, regardless of where it resides, and then right-placing data by migrating and tiering it non-disruptively. This approach can help businesses save up to 70% on unstructured data storage and backup costs.


Enabling AI and Machine Learning With Unstructured Data

As AI and machine learning continue gaining traction, unstructured data management enables these technologies. With 90% of data being unstructured and not fitting neatly into a schema, harnessing this data for AI is becoming increasingly important. Komprise provides the framework to index unstructured data and enables workflows that automate feeding the right data to AI with proper auditing and data governance.


Subramanian emphasized the symbiotic relationship between AI and unstructured data management. While the right unstructured data can enhance and improve the accuracy of AI results, AI can also enhance unstructured data by inspecting the contents and providing additional tags and context to aid searchability and precise management. This relationship fundamentally changes how organizations operate and serve their customers but requires significant governance and data protection mechanisms.


Customer Success Stories: Leveraging Unstructured Data for AI

Komprise has been helping customers leverage their unstructured data for AI initiatives. One example is a customer creating a chatbot to answer employee benefits questions. Komprise finds the relevant benefits documents across different sites and feeds them into the cloud to augment the retrieval prompt, helping the chatbot provide accurate responses with the latest corporate data.


Another example is a university using Komprise Smart Data Workflows with a cloud AI image recognition process to tag images from their digital collections. Without leveraging AI, every request for image retrieval would cost the university library archivists months of manually searching through thousands of images. Komprise has developed an automated data workflow to search across the university's global data estate and stream it through image recognition services, saving 90%+ time while enabling self-service for various departments.


Simplifying Multi-Cloud Data Management

As more companies adopt multi-cloud strategies, managing unstructured data across different cloud environments becomes a key consideration. Komprise simplifies this process by providing a single control plane to manage data no matter where it lives. Customers can analyze and manage data on each cloud with a single multi-site management console. Komprise moves data in a format native to each cloud, allowing all the AI and data services in the cloud to operate on the data.


Empowering Developers With APIs and Automation

APIs and automation are crucial in unstructured data management, empowering developers and engineers to build custom workflows and applications on the Komprise platform. Komprise Smart Data Workflows allows users to create custom workflows from any Deep Analytics query, and the platform supports API connections to third-party tools such as cloud AI services.


Automation is built throughout the platform, from managing and executing policies to creating automated data workflows. This is critical due to the volume of unstructured data in most enterprises. Enterprise customers can run multiple plans simultaneously, "set and forget," then check back later via reporting, ensuring everything is executed as planned without errors.


Navigating Data Privacy and Compliance

Data privacy and compliance remain top concerns for many organizations. While Komprise is not a security solution, it can help organizations discover anomalies in storing data that could indicate a ransomware attack, other cybercrime, or compliance issue. Komprise users can search for files that are out of policy for corporate storage or quickly investigate a ransomware breach by searching on targeted files and directory names.


Subramanian emphasized that extracting value from unstructured data requires adequate protection. Komprise helps organizations manage unstructured data assets with granular search and classification, ensuring compliance and avoiding regulatory fines and data loss. This foundation is essential for the ultimate goal of extracting value from data through AI and end-user collaboration.


Conclusion

As unstructured data grows and AI initiatives gain momentum, the need for effective unstructured data management solutions has never been greater. Komprise has been at the forefront of this challenge, empowering developers, engineers, and architects with the tools to manage, protect, and extract value from their unstructured data.


With its ability to provide visibility across heterogeneous storage, simplify multi-cloud data management, and enable AI and machine learning workflows, Komprise is well-positioned to help organizations navigate the complexities of unstructured data in the AI age. As the symbiotic relationship between AI and unstructured data management continues to evolve, Komprise's approach and platform will be essential in driving innovation and efficiency in data-intensive workflows.

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