The Benefits of Integrating a Data Fabric into Your Business
Scalable, Efficient Data Infrastructure
Instant, Real-time Data Access
Build a scalable, flexible infrastructure that unifies data across structured, semi-structured, and unstructured sources. Seamlessly integrate with diverse environments, reducing the complexity of managing distributed data.
Access and analyze all your data in real time—whether from data lakes, data warehouses, or SaaS apps—without moving or duplicating it by using data virtualization. Speed up decision-making by eliminating the need for time-consuming ETL processes.
Enterprise-Grade Data Governance and Security
Enhance your data governance and security with robust, centralized controls. Apply enterprise-grade access policies, track data lineage, and ensure data quality while maintaining compliance with regulations.
Self-Service Analytics and AI
Future-Proof Your Data Strategy
Empower teams with self-service analytics and AI tools. With the help of AI, even non-technical users can tap into the insights hidden in big data, helping your organization unlock its full potential.
Implement a data fabric architecture that evolves with your business. Seamlessly integrate new and existing data sources to ensure consistent performance and reliability, while managing data across all environments.
Why Choose Best in Class for Your Data Fabric Needs?
A best-in-class data fabric solution ensures your organization can scale efficiently, unify data from multiple sources, and provide real-time access. With features like advanced metadata management, robust governance, and AI-driven insights, you can confidently rely on your data while maintaining strict security standards. The right solution future-proofs your data strategy, allowing it to grow and adapt alongside your business.
What Exactly is a Data Fabric?
A data fabric is a modern, unified architecture that connects and integrates data across your organization, no matter where that data resides. It can handle structured data, such as databases; semi-structured data, like JSON or XML files; and unstructured data, such as documents, emails, or multimedia content. This versatile approach allows for seamless data integration from on-premises systems, cloud platforms, or even hybrid environments. A data fabric leverages advanced metadata management, automation, and AI to ensure that all data is discoverable, accessible, and governable in real time, which is key to unlocking its full potential.
By implementing a data fabric, organizations can effectively break down data silos, improve data quality, and simplify access to vast amounts of big data. This provides a solid foundation for faster, more informed decision-making, empowering teams across the enterprise to make smarter business decisions.
Augmented Data Catalog
An augmented data catalog automatically organizes, classifies, and enriches metadata across the enterprise, providing a comprehensive overview of all your data assets. By leveraging AI-powered search and discovery capabilities, teams can not only quickly find relevant data but also understand its context and quality. This improved visibility reduces the time spent searching for data and enhances decision-making speed, while ensuring data remains accurate and governed throughout its lifecycle.
Knowledge Graph
A knowledge graph dynamically maps relationships between different data points, providing a deeper understanding of how data is interconnected across the organization. It visualizes these relationships, helping users to explore data in context and discover previously hidden patterns or insights. This capability is especially useful for uncovering complex insights from diverse datasets, including both structured and unstructured data, supporting more accurate, data-driven decision-making.
Active Metadata
Active metadata continuously interacts with your data environment, constantly updating itself in real time based on data usage, user activity, and operational changes. Unlike traditional passive metadata, active metadata adapts dynamically, which not only enhances data quality but also strengthens governance by automating workflows. It provides intelligent, context-aware recommendations that help optimize how data is used and managed, reducing manual intervention and making data governance more proactive and effective.
Recommendation Engine
The recommendation engine harnesses the power of AI to analyze user behavior, data context, and patterns within your data. Based on this analysis, it suggests relevant datasets, insights, or next actions that align with the user's needs. This intelligent guidance significantly reduces the time needed to find the right data, helping users to make decisions faster and with greater confidence. By ensuring that the most accurate and valuable data is readily available, it enhances overall data quality and operational efficiency.
Data Preparation & Data Delivery
Automated data preparation transforms raw data into clean, ready-to-use insights by applying automated enrichment and transformation processes. This eliminates the need for manual data preparation, allowing teams to quickly move from raw data to actionable insights. Once prepared, the data is delivered seamlessly to the right users or applications, ensuring that trustworthy, analysis-ready data is available when needed. This efficiency speeds up the entire data workflow, from ingestion to insight generation, improving productivity and reducing bottlenecks.
Data Integration
Simplify the integration of data from various sources, whether it's structured, semi-structured, or unstructured. A data fabric acts as a unified framework that connects disparate data systems, ensuring smooth and seamless data flow across all environments. By eliminating the need for complex data processing via ETL, a data fabric enables real-time access and integration, which in turn accelerates data-driven initiatives and enhances operational efficiency.
Data & AI Orchestration
Data and AI orchestration automates the coordination of data workflows and AI processes, ensuring that data is efficiently processed and delivered to the right systems or users. This streamlined orchestration minimizes delays, reduces manual intervention, and ensures that both data and AI models work together seamlessly. Whether you're dealing with structured or unstructured data, AI orchestration optimizes the flow of data to drive timely insights and better decision-making across the organization.
Publish & Consume
A data marketplace provides a centralized platform where teams can publish and consume data products across the organization. With built-in AI-driven recommendations and self-service capabilities, even non-technical users can quickly discover and access the most relevant data for their needs. This democratization of data access empowers every user in the organization to make data-driven decisions, fostering greater collaboration and significantly accelerating the pace of innovation.
Data Fabric Use Cases
Real-Time Data Analytics: Make Informed Decisions Instantly
Leverage real-time data to make faster, more informed decisions across your entire organization. By analyzing live data streams from structured, semi-structured, and unstructured sources, a data fabric enables you to react quickly to changing market conditions or operational challenges. With real-time analytics, businesses can move beyond static reports and dashboards, gaining up-to-the-minute insights that drive agility and responsiveness. This not only helps you stay ahead of the competition but also empowers teams to make data-driven decisions at critical moments, enhancing business outcomes and customer experiences.
Self-Service Data Access with AI: Empower Every User
Empower every user in your organization—whether they are data scientists, business analysts, or non-technical teams—to easily access and query data on demand. A data fabric equipped with AI-powered data discovery tools allows users to tap into big data and perform analyses without the need for IT intervention or deep technical expertise. By simplifying the way data is accessed, you unlock faster insights and improve productivity across departments. With self-service capabilities, users can find, explore, and leverage the data they need to make timely, informed decisions, which can significantly boost operational efficiency and innovation.
Building Data Products: Fast-Track Data-Driven Solutions
Transform your organization's data into reusable, reliable data products that can be easily consumed across teams. A data fabric simplifies the process of building and managing curated datasets, providing a single source of truth for all your data-driven initiatives. By centralizing data into accessible, standardized products, you streamline the analytics process and eliminate inconsistencies that can arise from fragmented data sources. This enables faster, more accurate analytics and fosters a more efficient approach to data-driven decision-making, accelerating your organization's ability to execute on key business strategies.
Strengthen Data Governance and Compliance: Ensure Security and Integrity
Ensure strong data governance and compliance across all environments with automated tools that maintain control over data access, usage, and security. A data fabric helps you implement real-time data governance by automating policy enforcement, tracking data lineage, and ensuring compliance with regulations like GDPR, CCPA, and other industry-specific rules. By maintaining a clear view of how data flows through your systems and who has access to it, you can ensure the highest levels of data integrity and security. This reduces the risk of compliance violations and enhances trust in your data management practices, which is crucial for regulatory audits and business transparency.
Agile Data Development: Accelerate Innovation and Time-to-Market
Support agile development methodologies by quickly integrating and preparing data from a variety of sources. A data fabric enables your teams to prototype, test, and deploy new data-driven solutions with speed and flexibility, accelerating the innovation process. Whether you're developing new machine learning models, building customer insights, or enhancing operational processes, the agility of a data fabric allows you to pivot quickly and adjust to new business needs. This reduces time to market and helps your organization stay competitive in a fast-evolving data landscape.
Future-Proof Data Infrastructure: Minimize Risk While Adopting New Technologies
Adopt new technologies and adapt to future challenges without disrupting your existing operations. A data fabric allows for flexible data integration across various systems, ensuring that as your organization scales or adopts new technologies—such as cloud platforms or emerging data storage solutions—your data strategy remains intact. This means consistent access to data, regardless of where it resides, without the need for costly and time-consuming migrations. By decoupling data infrastructure from underlying storage systems, you can future-proof your architecture, ensuring long-term performance, reliability, and scalability across evolving business needs.
Frequently Asked Questions
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What problem does Promethium solve?Promethium helps companies instantly know the value of their data and what questions their data can answer. This may sound easy but, with over 350 database, data warehouse, data lake vendors and an estimated 180 zettabytes of data by 2025, discovering one’s data, and understanding what it means becomes more complex every day. It’s not uncommon for enterprises to take up to 4 months to answer a question from one of their business units.
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What does Promethium do, and how do you do it?Promethium provides a single, AI-powered context solution that simplifies the data pipeline for BI by allowing users to simply ask a question. Promethium then automatically locates the data, provides instructions on how to assemble that data, reveals the quality of the data and even displays the SQL statement, dramatically reducing the time and costs associated with manual data analytics. This entire process now takes less than 4 minutes. Promethium achieves this in the following manner: 1. Connects to all data sources to abstract metadata. 2. Infers name, meaning, location and relationships of data across sources. 3. Leverages automation, machine learning and AI to automate the mapping of natural language to data and its intent.
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What makes Promethium different from your competitors?Getting value from data is a fragmented task that spans multiple/separate teams who use multiple/separate tools. Unfortunately, these tools frequently do not integrate with each other, making it difficult and time-consuming for users to finally consume the data. For example, there are multiple data catalogs for each data type, different data prep tools for on-premise data sources and cloud data sources, and different ETL/ingestion tools. The list goes on, and integrating all of these tools often costs more than the cost of purchasing them. Promethium is a single solution that, while integrating with existing data catalogs, ingestion, prep and visualization tools, gives the user a single, seamless experience to ask a question, locate the data, see how the data should be assembled, see the SQL statement automatically created and finally see the execution of the query.
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What types of companies benefit most from Promethium?We provide value for customers no matter the size of their data estate because every company that relies on their data is trying to derive meaning from it as quickly and efficiently as possible. That being said, enterprise clients seem to benefit more due to the size, heterogeneous nature, complexity of their data infrastructure, as well as the necessity to comply with stringent governance regulations.
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Is Promethium the next level of data management?Yes. Promethium is a cloud-native data management solution that leverages AI and Machine Learning. It has an extensive API-layer allowing it to connect with existing / new data sources, BI tools and other data management solutions.
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What do I get when I purchase Promethium?We offer a SaaS solution. Sign on, connect to the data sources you want us to contextualize, and that’s it. That is the fastest and easiest way. Promethium can be consumed in the public cloud or in users own virtual private cloud (VPC). A third way allows you to deploy Promethium on-premise in the customer’s own physical data center.
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What industries benefit most from Promethium’s solution?Any industry that uses data to derive business intelligence in the pursuit of competitive advantage. That being said many companies in banking / financial services, as well as insurance, gravitate to our solution.
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How does your AI engine work?Rather than starting from aggregating all of one’s data and trying to figure out what the data means, Promethium starts with the questions one wants to ask and maps the data to the question. One of our customers said, “If I don’t know what I am looking for, and I don’t know what I should be asking, giving me 1,000 options is not helpful.” Promethium’s AI/ML solution guides our customer’s to the few (less than 5) options that are best suited to their task. Our engine helps give context and provides guidance to apply the framework most relevant to your needs.
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Who is the team behind Promethium?We have solved these types of challenges for enterprises worldwide at companies like Oracle, VMware, Google, and Electronic Arts. We understand how to build robust enterprise systems that scale seamlessly for the enterprise, allowing for fast and massive adoption. Furthermore, our expertise in data management, data analytics/BI, and data governance gives us a unique advantage of understanding how to address the pain points specifically related to all things data.
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How are you going to move all of my data?We don’t. We never touch your data. We only move the metadata, which is the data about the data, so your data is never compromised. We pull your metadata directly into a metadata index, allowing us to search for data as if we have the entire file. You can search by name, type, vendor, IP address, context, or even by asking a simple question. Lastly, we only move the tables related to answering your specific question. No more. No less, resulting in a highly efficient, fully automated process.
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Does it only work for relational databases?No. We actually work with any data source, automatically discovering where the relevant data is located across your infrastructure.
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I need to first pre-define the data and join all the data ahead of time, right?No. The whole point of being able to do ad-hoc analysis is that you want to answer questions that haven’t been asked or use data that has not been discovered. Promethium discovers the data and figures out how to assemble the data all in real-time AFTER the question has been asked. This avoids the painful task of identifying ALL of the data and relationships BEFORE the question has been asked.
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But you can only do the query if the data is all on one table, right?No. We do it across multiple tables and across multiple vendors as well, eliminating the time-consuming and costly step of manually searching for your data’s location. At this point in the conversation, there is a long pause, followed by either an almost imperceptible head nod or grunt. The light is coming on, but the skepticism is not completely gone.
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I still have to write the SQL query, right?No. We automatically do that for you, providing step-by-step instructions on how to assemble the query, and with Promethium, you don’t have to know SQL code. What’s more, the directions can be easily attached to an email and sent to the relevant person in your enterprise to run the query.
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What if the data is dirty and the results are wrong?If the results are wrong, you want to know specifically what to clean and where. Many solutions only tell you that in your five petabyte data warehouse, there is some bad data. That’s not very helpful. But since we are only answering a specific question, and know that to do that involves these four tables, and these six rows we can quickly and easily clean that data, which greatly increases the accuracy of the answer you are getting.