Atlan, a leading data and AI governance platform, has raised $105 million in a Series C funding round led by GIC, with contributions from Meritech Capital, Salesforce Ventures, and Peak XV Partners. This brings Atlan’s total funding to over $206 million, boosting their valuation to $750 million. Atlan integrates diverse data sources, enhancing data collaboration and trust across enterprises. Their platform is used by major companies like Cisco, Autodesk, and Unilever, highlighting its growing industry influence. The new funds will support Atlan’s mission to tackle data privacy, lineage, and quality challenges in AI implementations. Atlan is a modern data workspace designed to streamline data operations and foster collaboration across diverse data teams. Unique features that make Atlan popular include its Google-like search functionality, automated data quality profiling, and extensive data governance capabilities like granular access control and column-level lineage creation. Atlan also offers seamless integrations with various data sources and BI tools, enhancing interoperability within the data ecosystem. Its user-friendly interface supports collaboration through link sharing and chat plugins, while the business glossary and metadata management provide clear, consistent data definitions and context. https://lnkd.in/gsE8mBq9
Rohan Wickremesinghe’s Post
More Relevant Posts
-
🚀Atlan Secures $105 Mn in Series C to Revolutionize Data & AI Governance. 🔍 Overview: Atlan, a pioneer in data collaboration software, has successfully raised $105 million in a Series C funding round. This investment is led by GIC and Meritech Capital, with contributions from existing backers Salesforce Ventures and Peak XV Partners. 🔑 Key Details: - Valuation Boost: The new funding elevates Atlan's post-money valuation to $750 million. - Revenue Growth: Atlan boasts a 7X revenue increase over the past two years, highlighting its robust market presence. - Enhanced Sales Metrics: The startup has experienced a 400% growth in enterprise sales in Q1 2024 alone, coupled with a 75% win rate in competitive trials. 🌐 Strategic Insights: - Market Positioning: Positioned as a next-generation platform for data and AI governance, Atlan is addressing critical challenges in data management across disconnected systems and silos. - Product Innovation: The startup's solutions integrate trust and context into digital infrastructures, simplifying data cataloguing and utilization across enterprises. 🏢 Company Background: - Founders' Vision: Launched in 2018 by Prukalpa Sankar and Varun Banka, Atlan aims to unify data management and enhance collaboration within enterprises. - Customer Base: Atlan's clientele includes major enterprises like Cisco, Unilever, and Autodesk, among others. 💼 Market Evolution: - AI and Data-Driven Focus: With businesses increasingly relying on AI and data, Atlan’s solutions for trustworthy data integration are more relevant than ever. - Competitive Landscape: The funding positions Atlan advantageously against competitors in the data governance and AI readiness spaces. 📈 Economic Impact: - Business Efficiency: By enabling effective data collaboration, Atlan helps companies leverage their data assets more efficiently, potentially reducing costs and accelerating decision-making processes. - Investment Attraction: The substantial funding round reflects strong investor confidence in Atlan's potential to lead in the data governance market. 🔮 Looking Ahead: With this latest funding, Atlan is poised to further enhance its product offerings, expand its global reach, and solidify its position as a leader in the data and AI governance sector. 🌟 Conclusion: Atlan's successful Series C round not only underscores its potential in transforming data governance but also sets the stage for its next phase of innovation and market expansion. As companies increasingly prioritize data integrity and efficiency, Atlan's solutions are set to play a pivotal role in shaping the future of enterprise data management. Atlan #DataGovernance #AI #Fintech #Atlan #SeriesCFunding #TechInnovation #EnterpriseSolutions #BusinessStrategy #StartUpNews #BusinessNews #MicroShots #NewsUpdates
To view or add a comment, sign in
-
🚀 AI’s Emerging Trillion-Dollar Opportunity: Transforming Enterprise Data Integration 🌐 Data is the lifeblood of every enterprise, but integrating it across systems remains one of the most challenging tasks for businesses today. As a Data Consultant, I’ve seen first-hand how AI-driven data integration is set to revolutionize this space, creating massive efficiency gains and economic value. Breaking Down Data Silos with AI 🧠 Most AI applications today are designed for narrow tasks—writing content, summarizing calls, or generating code. However, AI’s true potential lies in its ability to streamline complex business processes, particularly in data integration. Companies like InMoment are already grappling with integrating data from countless sources—emails, Zoom calls, Slack threads, and finance reports. The challenge isn’t just collecting this data, but normalizing it across silos to unlock its full potential. Intelligent Data Mapping: The Future 🌍 Imagine a future where AI automates data exchange between businesses. AI systems could negotiate and transform data seamlessly—what we call Intelligent Data Mapping. Right now, data integration consumes over 50% of large B2B projects. Reducing that time by even half would save billions, even trillions of dollars. Advanced AI models like LLMs and Lang Chain are already moving us towards machine-to-machine data interactions, which could drastically cut project timelines from months to weeks. AI-Driven Data Integrations: The Path Forward 📊 The financial impact is clear: AI-driven data integration will reshape industries, reduce costs, and unlock new capabilities in cross-enterprise analytics. While security and compliance remain top concerns, the sheer economic value will drive adoption. What’s Next? 💡 Within the next 2-3 years, we’ll see AI systems transforming enterprise data workflows. By investing in AI-driven data integration, businesses can stay ahead of the curve, reducing inefficiencies and harnessing the true power of their data. 💡 Takeaway: The time to start preparing for AI-driven data integration is now. For businesses looking to streamline data processes, AI is the key to unlocking massive value. Are you ready to embrace it? #AI #DataIntegration #LLMs #LangChain #BusinessTransformation #EnterpriseData #AIInnovation #FutureOfWork #DataConsultant
To view or add a comment, sign in
-
The effort and investment to centralize data across ALL their systems for many organizations was out of reach. → Clients had a myriad of data pipelines to manage and the larger enterprises had data sitting across different cloud vendors. → IT decision makers feel “locked in” to a single cloud vendor either because that is all their team has experience with or they have made a significant investment. → Many organizations wait for systems or vendor partners to integrate their tools to a centralized ERP, which is unrealistic. → There are hundreds of vendors for different business needs, and expecting them to build integrations across the board is impractical. This is where Fabric OneLake comes into play: the ability to Shortcut or Mirror your data across clouds and data platforms to a single-pane of glass WITHOUT expensive and hard to manage data pipelines. These features only work if your data is in the cloud in the first place (or using a cloud native back-end). You can integrate your on-premise data to Fabric no problem, but will have to use a data pipeline. Sorry hybrid folks! What is Unified Data Analytics? The goal of centralizing your data is to make it accessible for analysts, data engineers, data scientists, and organizational AI – providing insights exactly when needed. What are the benefits you ask? Cost-Effective Data Analytics • Avoid repetitive data pipelines and reduce costs by building a strong data foundation. • Leverage the cloud skills your team already knows, even if it is outside the Microsoft ecosystem. • Deliver projects faster, better, and more reliably. Less IT Handholding • Enable business units to access the data they need without delays. • Focus IT efforts on securing data, adding sensitivity labels, and provisioning resources with strong governance. Unlocking Generative AI • Ground AI with RAG on specific business data to provide tailored insights. • Improve decision-making processes by leveraging AI to analyze and interpret complex datasets. How does your org unify and centralize data? Are you still trying to shove everything in one place or is it time to reconsider your approach? Follow me here on LinkedIn for your weekly AI and data tips!
To view or add a comment, sign in
-
🚀 Exciting news in the tech world! Salesforce has officially signed a definitive agreement to acquire Israeli company Zoomin, a leader in managing unstructured data, for $450 million 💼. This acquisition goes beyond just expanding Salesforce's portfolio—it's a game-changer for the future of data analytics. By integrating Zoomin's cutting-edge data management capabilities with Salesforce's Data Cloud, we can expect a revolution in how companies handle, analyze, and leverage AI Capabilities 🌐📊. This move will not only enhance how organizations manage unstructured data but will also unlock new opportunities for delivering insights and driving business success. Looking forward to seeing how this powerful combination will reshape the future of data-driven decision-making!
Salesforce is acquiring Zoomin for $450 million. This continues Salesforce’s acquisition spree focused around data and AI. Zoomin is a data governance and LLM readiness platform that enables organizations to seamlessly integrate unstructured enterprise data into AI applications, handling ingestion, enrichment, and RAG readiness. The move highlights Salesforce’s heavy investments in tools that enable both structured and unstructured enterprise data being used in trusted AI applications. This is especially important given how much unstructured data enterprises have today that could be leveraged for AI purposes. There are numerous benefits this acquisition will bring to Salesforce and its customers. 1. Strengthens Salesforce’s AI capabilities Zoomin’s expertise in unstructured data will enhance Salesforce's AI engines and autonomous models. 2. Accelerates digital transformation Zoomin’s technology will be integrated into Salesforce’s Data Cloud and Service Cloud, aiding in digital and AI transformations for enterprises. 3. Additional key benefits include: ↳ Enhanced AI capabilities with unstructured data processing ↳ Integration into Salesforce’s Agentforce platform ↳ Improved customer experiences with personalized responses ↳ Accelerated innovation in Salesforce’s Data Cloud With this acquisition, Salesforce is set to revolutionize how businesses leverage AI and unstructured data. —— Follow for more insights on how Salesforce customers, business leaders, and professionals can learn about and leverage AI, Data, and Analytics tools, plus real-world examples from across industries. —— #Salesforce #AI #ArtificialIntelligence #DataGovernance #UnstructuredData #DigitalTransformation #AIInnovation #EnterpriseAI #ServiceCloud #TechAcquisition #CustomerExperience
To view or add a comment, sign in
-
Uniphore acquires ActionIQ and Infoworks: Uniphore, an enterprise-class and AI-native company, has acquired two data companies, ActionIQ and Infoworks. These strategic acquisitions extend its comprehensive end-to-end Enterprise AI platform and aim to deliver the industry’s first Zero Data AI Cloud, Uniphore said in a press release. According to Uniphore, with these acquisitions, customers will gain access to a comprehensive suite of AI capabilities. This will allow organisations to move beyond proof-of-concept AI trials into full-scale implementation, accelerating their digital transformation. Uniphore states that its Zero Data AI Cloud is built on an infrastructure-agnostic architecture that creates a seamless data fabric across any data platform, enterprise application, or cloud environment. ActionIQ is a composable zero-copy data Platform, enabling enterprises to connect all data, from anywhere, and make it AI-ready without the need for lengthy integration or ETL projects. While Infoworks contributes its Enterprise Data Platform, which includes intelligent AI data agents capable of discovering, identifying, organising, cataloguing, and cleaning enterprise data with minimal human intervention. With these acquisitions, its combined expertise and technology will enable enterprises to leverage all their data for AI initiatives without the need to move, transform, or copy it, significantly accelerating results from years to weeks. Uniphore says it offers multi-layered AI architecture, consisting of a composable data layer, knowledge layer, model layer, and agentic layer. Each layer is equipped with feature-rich solutions, with continued investments planned in each area. With this 4-layer approach, customers can fully leverage its suite of end-to-end AI capabilities through an open architecture, ensuring data sovereignty and enterprise-grade scalability. Organisations maintain complete control while deploying AI solutions at any scale. Uniphore claims that it empowers over 750,000 end-users across 1,600 enterprises in 20 countries. #Uniphore #AI #DataAnalytics
Uniphore acquires ActionIQ and Infoworks
entrackr.com
To view or add a comment, sign in
-
🚀 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐌𝐨𝐝𝐞𝐫𝐧 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 Part1 In today’s fast paced digital world, data is the backbone of innovation and decision making. However, traditional data architectures often struggle to keep up with growing demands. Enter Data Mesh, a groundbreaking approach that addresses these challenges head on, transforming the way organizations manage and utilize data. But why does this matter, and how can it benefit modern enterprises? 𝐊𝐞𝐲 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐰𝐢𝐭𝐡 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 As organizations grow, their data needs expand, often leading to high costs, complexity, and bottlenecks in managing centralized data platforms. Some common issues include: 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Existing infrastructure can't scale easily as data volumes increase. 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐂𝐨𝐬𝐭𝐬: Maintaining centralized data systems becomes expensive, often requiring large, specialized teams. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐓𝐫𝐮𝐬𝐭:Lowquality or poorly governed data leads to bad decisions and loss of trust in data driven processes. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐚𝐜𝐭: When data systems fail, it directly affects business operations, highlighting the critical need for robust, reliable infrastructure. 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 Data Mesh flips the traditional approach by decentralizing data ownership. Instead of a single, centralized team managing all data, Data Mesh assigns ownership to individual domains within an organization. These domains treat data as a product, meaning they are responsible for creating, maintaining, and delivering high quality data that is discoverable and usable by others. 𝐖𝐡𝐲 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐌𝐮𝐬𝐭 𝐀𝐜𝐭 𝐍𝐨𝐰 With the rise of AI, machine learning, and data driven decision making, businesses must treat their data as a strategic asset. Adopting a Data Mesh framework ensures that companies remain agile, scalable, and equipped to handle the complexities of modern data demands. Data Mesh isn’t just a technology shift—it’s a cultural and operational one. It requires businesses to rethink how they view data, moving from a centralized model to one where every domain plays an active role in creating, governing, and leveraging data for business success. #DataMesh #DataOwnership #DigitalTransformation #AI #DataGovernance #DataAsAProduct #Scalability #BusinessContinuity #DataQuality
To view or add a comment, sign in
-
#DataArchitecture: #DataDriven #Sustainability, Future Profitable Growth and Ambitious goals 1. #Scalability and Flexibility for Growth - Modular Architecture - #DataFabric or #DataMesh - Elastic Storage and Compute 2. #DataGovernance and #Compliance - Data Governance Framework especially for sustainability and environmental, social, and governance (#ESG) goals. - #DataLineage and Transparency - Privacy and Ethical AI 3. #AI and #Analytics Driven Decision Making - #AdvancedAnalytics and AI - Real-Time Data Processing - AI for Sustainability 4. Sustainable #DataInfrastructure - Energy-Efficient Design - #Cloud Native and Hybrid Architectures - #CarbonFootprint Monitoring 5. #DataMonetization and Profitability - Data as an Asset - Customer-Centric Data Solutions - AI for Efficiency 6. #Innovation and Future-Proofing - #Agility and Adaptability - Innovation Ecosystem - #DataDemocratization 7. Sustainability Reporting and #KPIs - Automated Sustainability Reporting - Data-Driven KPIs for Growth Details with case studies are available in our Premium Content. To subscribe to our Premium Content, please either DM on LinkedIn or email: [email protected] for subscription details. Transform Partner is your trusted navigator in the turbulent seas of data and digital transformation. In an era where businesses are inundated with information yet starved for #insights, we stand as a beacon of clarity and innovation. Our consultancy brings together a team of seasoned experts with deep industry knowledge spanning government, banking and finance, healthcare, retail, and manufacturing sectors. When you partner with us, you're not just getting a roadmap; you're gaining a co-pilot for your digital journey. We provide hands-on support at every stage, from strategic planning and change management to technical implementation and post-launch optimization. Let's collaborate to turn your digital vision into reality. With Transform Partner, you're not just keeping pace with the digital revolution – you're leading it. Reach out today to schedule a personalized consultation and take the first step towards a transformative future. Image Source: AWS #TransformPartner – Your #DigitalTransformation Consultancy
To view or add a comment, sign in
-
"Centralizing to decentralize" 💡 A lot of times when I talk to data teams, they are pursuing a data mesh approach. Almost 100% of the time, a fully federated or decentralized model where EVERY domain is owning their own data and data product just isn't realistic. At least not in the near term... Now, every organization is starting on a different point in the central/decentral balance -- some traditional orgs may have all data and analytics activities centralized creating bottlenecks, while some midsized or digital native orgs might be quite decentralized lacking consistency and governance. In either scenario, I've seen a trend that was described well by one customer as "centralizing to decentralize." I really like this. Essentially what they are saying is, we know we have a goal of federated ownership of data but don't have the maturity, platform, or governance to support this just yet. In the meantime, let's invest in building out that centralized platform, best practices, and automated governance features that will act as the guardrails for any decentralized team (when ready) to access, own, and utilize their own data products. Back at Gartner we called this the Franchise Approach. I think this is a much more realistic approach to progressing on a data mesh journey. No one I have talked to is a 100% data mesh org, but many can be effective across a number of their domains. What "centralizing to decentralize" does NOT mean is that a centralized team attempts to own and deliver on all enterprise data requests. Centralized teams have to evolve to more of a platform and enablement mindset, rather than a siloed data delivery machine. What do you think? #data #analytics #datagovernance #AI #genAI #activemetadata Atlan
To view or add a comment, sign in
-
Something to consider as you are building out an approach and framework for your data teams ... and business units. #centralizetodecentralize #federatedcatalog #dataproducts #getyourdomains #datameshthis #activemetadata #webringthedataback
"Centralizing to decentralize" 💡 A lot of times when I talk to data teams, they are pursuing a data mesh approach. Almost 100% of the time, a fully federated or decentralized model where EVERY domain is owning their own data and data product just isn't realistic. At least not in the near term... Now, every organization is starting on a different point in the central/decentral balance -- some traditional orgs may have all data and analytics activities centralized creating bottlenecks, while some midsized or digital native orgs might be quite decentralized lacking consistency and governance. In either scenario, I've seen a trend that was described well by one customer as "centralizing to decentralize." I really like this. Essentially what they are saying is, we know we have a goal of federated ownership of data but don't have the maturity, platform, or governance to support this just yet. In the meantime, let's invest in building out that centralized platform, best practices, and automated governance features that will act as the guardrails for any decentralized team (when ready) to access, own, and utilize their own data products. Back at Gartner we called this the Franchise Approach. I think this is a much more realistic approach to progressing on a data mesh journey. No one I have talked to is a 100% data mesh org, but many can be effective across a number of their domains. What "centralizing to decentralize" does NOT mean is that a centralized team attempts to own and deliver on all enterprise data requests. Centralized teams have to evolve to more of a platform and enablement mindset, rather than a siloed data delivery machine. What do you think? #data #analytics #datagovernance #AI #genAI #activemetadata Atlan
To view or add a comment, sign in