Data Schema's on the Fly - Is Not a New Concept Standardized schemas are something that we've been after for a long time in tech. The introduction of OCSF (https://github.com/ocsf) recently with the overall strategy to standardize data formatting/management has introduced a new discussion - but this is primarily happening in security circles. There is much more ahead of us in the data field mapping, schema normalization, and field mapping arena. Snowfire is poised to be in front of this thinking with our AI Fusion and Correlation Engine that helps drive decision intelligence. Data schemas are a big decision and impact your business for years down the road. This discussion is happening everywhere - but are we thinking big enough?
A fresh, new line of thinking is that the enterprise schema needs an AI to support the correlation across all of data management. Snowfire's approach to dynamic schema development is revolutionizing how organizations manage and utilize their data. By leveraging artificial intelligence for data field mapping, the Snowfire platform offers cutting-edge solutions that enhance data integration, reduce overhead, and provide significant value to enterprises. This detailed exploration covers key insights into the platforms capabilities, the evolving landscape of schema development, and the strategic importance of AI-driven data field mapping. The future is on fire when it comes to data mapping and decision intelligence.
New, Strong Community Support for OCSF
The Open Cybersecurity Schema Framework (OCSF) has garnered substantial support from prominent industry players such as Splunk and AWS. This vibrant community comprises industry veterans, experts, startups, and established companies collaborating to drive innovation and standardization. Contributors within this community are proactive, supportive, and efficient in implementing changes, fostering an environment conducive to rapid development and adoption. Joining the OCSF Slack channel is highly recommended for those interested in contributing or staying updated on the latest developments.
We are in the Early Stages of OCSF Development
OCSF is still in its nascent stages, having recently released its first major version. During this period of development, much of the information may be stored under generalized fields, making it essential for vendors to catch up with schema versions. As the schema stabilizes and more vendors adopt it, the goal is to achieve a more normalized and ready-to-use framework out of the box. However, in the interim, organizations should be prepared for ongoing adjustments and updates.
The Search for Vendor-Driven Standards: A Future of Interoperability
Adopting an open and accepted standard like OCSF promotes interoperability across the data ecosystem. This reduces the workload for enterprises consuming data and for vendors building connectors, thereby enhancing the overall value of the data ecosystem. Supporting an open standard means less effort for vendors in developing custom connectors while simultaneously offering greater value to customers. Additionally, it allows non-competing services to integrate more seamlessly, increasing the vendor's value within the ecosystem.
Local Normalization for Enterprise Data Fields
Speaking directly about security here... traditional Security Information and Event Management (SIEM) systems have set the expectation that data must always be normalized. However, with platforms like Snowfire.AI, which offer advanced features such as correlation AI that performs native data searching and the ability to handle unlimited concurrent queries, the need for universal normalization may be reduced. This shift can lead to significant cost savings and efficiency gains. But we cannot think about this only in the realm of security - we need to think about this for the entire enterprise software environment. Security is one example, and while its one of the most expensive data sources (storage, compute, compliance historical requirements) - the overall discussion must expand to sales, marketing, operations, financial, human resources, and product related data sets to glue together schemas for the overall gain of gluing together enterprise infrastructure.
We call this enterprise analytics correlation engine - OCEANS.AI - (Open, Common Enterprise Analytics Native Schema - AI Enabled). This is all of your data, combining your data lakes, and then applying a schema across all of your software environments - using AI to field map and correlate those sources/fields. The value extraction of doing this changes the overall cost structure of every single company and how they are charged for software platforms, data storage, data compute, and most importantly the decisions and intelligence that is built on this OCEANS.AI architecture.
What is important is that this glues together the large number of different enterprise engineering teams across every business. Different teams within an organization often require data in various formats - its too much for teams to collaborate on across the business. This is a blocker for moving forward with the enterprise data lake being one lake - we are continuing on the path of forming silos (lakes) - we need OCEANS.AI, where all data fields are correlated and the entire enterprise data set is matching up with common field mapping for non-common data sources that help drive value across all business data investments. For instance, data scientists working on machine learning and AI models may prefer raw data to preserve its granularity, while security threat detection engineers might need normalized data to align with existing detection frameworks, while operations and product development may prefer to have parquet format. This variance causes extreme amounts of cost, engineering cross functional loss, and exponential time to bring these teams together. Furthermore, some log sources, such as VPC flow logs (because nearly everyone has AWS these days as a very relative example), are not suitable for normalization due to the storage overhead. Raw VPC flow logs take up significantly less space compared to their normalized counterparts, which can be up to ten times larger when formatted according to OCSF.
Embracing Custom Schemas = AI Driven Outcomes in Field Mapping
Deploying and maintaining a schema can be challenging, and keeping it up to date with the latest versions is often impractical. Many organizations choose to fork existing schemas to tailor them to their specific needs. This approach is not a failure but rather a strategic customization that simplifies future integrations. Starting with a framework like OCSF and adapting it as necessary allows organizations to meet their unique requirements while maintaining a solid foundation for future development.
Considering Established Schemas: ECS and Beyond
Many enterprise security teams utilize or adapt the Elastic Common Schema (ECS), which has been around longer than OCSF and is more mature and stable. Recently, ECS was donated to Open Telemetry (OTel), making the standard more open and less influenced by a single vendor. This integration means that ECS and the existing OpenTelemetry Semantic Conventions will merge, offering a more comprehensive standard for observability. Given OTel's widespread adoption and industry acceptance as a dominant standard for observability, adopting a future version of ECS could provide significant long-term benefits. However, during the transition, organizations should expect a period of "under construction" as these standards evolve.
Leveraging AI for Data Field Mapping: The Future is Snowfire.AI
The primary value driver for Snowfire.AI lies in leveraging artificial intelligence for data field mapping. This advanced capability allows Snowfire.AI to automate and streamline the process of integrating diverse data sources, ensuring that data is accurately mapped and readily usable across various applications.
AI-driven data field mapping offers several key advantages:
Efficiency and Speed: AI can rapidly analyze and map data fields, significantly reducing the time required for manual mapping processes. This acceleration allows organizations to integrate new data sources more quickly and efficiently.
Accuracy and Consistency: AI algorithms can ensure a higher level of accuracy in data mapping by identifying patterns and relationships that might be missed by manual processes. This consistency is crucial for maintaining data integrity across the organization.
Scalability: As data volumes and sources continue to grow, AI-driven mapping can scale effortlessly to handle increasing complexity. This scalability ensures that organizations can continue to integrate and utilize new data sources without being bogged down by manual processes.
Cost Reduction: Automating the data field mapping process reduces the need for extensive manual labor, leading to significant cost savings. These savings can be redirected towards other strategic initiatives, further enhancing the organization's overall ROI.
Future-Proofing: By leveraging AI for data field mapping, Snowfire.AI positions itself at the forefront of technological innovation. This future-proofing ensures that the platform can adapt to emerging data standards and technologies, providing long-term value to its users.
Snowfire.AI's Comprehensive Solution
Snowfire.AI's platform integrates these AI-driven capabilities with robust schema management tools, offering a comprehensive solution for modern data management challenges. The platform's ability to develop schemas on the fly, combined with its advanced AI capabilities, positions Snowfire.AI as a leader in the field of data integration and management.
Key features of Snowfire.AI's platform include:
Dynamic Schema Development: The ability to create and adapt schemas on the fly, ensuring that data is always ready for use.
AI-Powered Data Field Mapping: Leveraging artificial intelligence to automate and enhance the data mapping process.
Flexible Data Formats: Supporting a variety of data formats to meet the diverse needs of different teams within an organization.
Scalability and Performance: Ensuring that the platform can handle large volumes of data and complex queries without compromising performance.
Interoperability: Promoting open standards and seamless integration with other tools and platforms in the data ecosystem.
The Path Forward: Embracing Innovation and Collaboration
As the data landscape continues to evolve, Snowfire.AI remains committed to driving innovation and fostering collaboration within the industry. By embracing open standards like OCSF and ECS, and leveraging advanced AI technologies, Snowfire.AI is well-positioned to meet the emerging needs of enterprises and deliver significant value.
For organizations looking to stay ahead in the competitive data-driven world, adopting a platform like Snowfire.AI offers a strategic advantage. The combination of dynamic schema development, AI-powered data field mapping, and robust interoperability ensures that enterprises can effectively manage their data, derive actionable insights, and achieve their business objectives.
The Conclusion
In conclusion, Snowfire.AI's innovative approach to schema development and data integration, underpinned by AI-driven data field mapping, sets a new standard for modern data management. By addressing the challenges of traditional normalization, embracing open standards, and leveraging advanced AI technologies, Snowfire.AI provides a future-ready solution that delivers efficiency, accuracy, and scalability. Its time to shift to the enterprise schema, the shift to maximizing data across the entire set of data investments. There's too many icebergs, and not enough value. This is what we are solving at Snowfire.
As organizations continue to navigate the complexities of the data landscape, Snowfire.AI offers a comprehensive platform that empowers them to maximize the value of their data, reduce costs, and stay competitive in an ever-evolving market. Whether through dynamic schema development, AI-powered mapping, or robust interoperability, Snowfire.AI is poised to lead the way in transforming how enterprises manage and utilize their data. OCEANS is here. Don't continue to build icebergs of data. Snowfire is here to help drive your data value extraction, your enterprise business data maximization, and your decision intelligence at scale. Contact info@snowfire.ai to get started on your journey today!
