Transform your outdated database into a profit-generating powerhouse with the help of AI tools. Our cutting-edge solutions are designed to automate data management, streamline processes, and uncover valuable insights that are often buried in old, underutilized systems. By optimizing and modernizing your database, we help businesses unlock hidden opportunities, reduce inefficiencies, and drive smarter decision-making, all leading to increased profitability. Our expert team stands out because of our deep knowledge of AI technology and data architecture. We’ve helped countless professionals and businesses harness the power of AI, providing tailored solutions that meet specific needs. With us, you’re not just updating your database—you’re setting the foundation for sustainable growth. Let us help you turn your data into a strategic asset.
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🔍 Unlocking Industrial DataOps: A Journey to Operational Excellence 🚀 With data as a foundation for unlocking AI-powered insights, Industrial DataOps is a critical part of a journey that involves: 1. Breaking Down Data Silos: Merge IT, OT, and engineering data to ensure accessibility and reliability, laying the groundwork for impactful analysis. 2. Contextualizing and Unifying Data: Transform disparate data into a unified, contextualized resource, making it comprehensible for diverse users. 3. Scaling AI-powered Insights: Harness advanced models to generate actionable insights and empower users, driving optimal decision-making. By embracing Industrial DataOps, industries pave the way for a more informed, efficient, and innovative future. Read the blog: https://lnkd.in/gxf95nb3 #DataOps #IndustrialRevolution #DigitalTransformation 🛠️📊
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Highly recommend downloading this content/playbook from CIO Dive 👇 🔗 https://lnkd.in/dnkihKkF 🔍 AI is Exposing IT Architecture Limitations! 🔍 🚀 Discover how AI is highlighting weaknesses in IT architecture and learn actionable steps to overcome them. 📈 Critical Insights: Explore key questions to ensure your IT architecture supports high data quality. 🛠️ Expert Strategies: Understand how to modernize your data infrastructure, eliminate silos, and democratize AI for better adoption. 🌐 Future-Ready: Equip your organization to handle massive, unstructured data volumes. #AI #ITArchitecture #DataQuality #TechLeadership #FutureReady #PrivateEquity #PE #VentureCapital #VC #Investment #TechInvestment #BusinessGrowth #Innovation #DataStrategy
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I've recently been in discussions with a lot of companies who are looking at the obvious scenario: How do we use #data and #AI to drive our #business outcomes. In those conversations we unravel what they have been doing, how they have been using data to drive business performance, what typical products they have delivered, what's working, what's not, where are their biggest opportunities/challenges etc. What I've found is that many are realising that data and AI are exposing the cracks in their businesses. Cracks in processes, ways of working, data not being good quality, systems not fit for purpose, etc. These two are revealing years of inefficiency or what I like to term as 'Spaghetti Junctionitis', which is typically a tangle of systems and siloed data holding back progress. Yes, you might think that this is about #technology, and it is to a point, but then it also isn’t about technology. Stick with me. It’s more about missed opportunities to create real business value. The trends in the conversations are that they have invested way too much in technology that they thought would fix everything, and now the mess is difficult to untangle, it can be done, however, some find it costly! But, my view, if you get your Enterprise and data architecture right now, then there is hope. As many have often put these two disciplines on the back burner, it's put them backwards and that's where many find it difficult to differentiate. Honestly, fixing this mess isn’t optional if you want to stay competitive. The question in my mind is this: will your business act now to create clarity and value, or risk being left behind? Thoughts in the comments please. #datastrategy #strategy #dataarchitecture #enterprisearchitecture #valuecreation
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Explore the key features of a modern #datafabric and how it can lay the foundation for your Artificial Intelligence journey. https://lnkd.in/gQfQur88
A Fabric for Data Driven Success — Fulton Analytics
fultonanalytics.com
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AI continues to be the hottest topic around, and it's now a business imperative. 💯 Find out what the successful data architecture patterns for organizations like yours who want to leverage the power of AI are in this webinar with experts from Forrester and Snowflake. The best part? You don't even need to be a tech genius to unlock the power of generative AI! https://bit.ly/49nhZrd #AI #DataDriven
Democratizing Data and Insights in The Current AI-Powered Landscape: A Fireside Chat with Snowflake + Forrester
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AI continues to be the hottest topic around, and it's now a business imperative. 💯 Find out what the successful data architecture patterns for organizations like yours who want to leverage the power of AI are in this webinar with experts from Forrester and Snowflake. The best part? You don't even need to be a tech genius to unlock the power of generative AI! https://bit.ly/49nhZrd #AI #DataDriven
Democratizing Data and Insights in The Current AI-Powered Landscape: A Fireside Chat with Snowflake + Forrester
streamsets.shp.so
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"Every year for the last 10 years we've purchased another data quality tool and ripped out an old one that didn't solve data quality. Don't sell me another data quality tool." - CDO F100 company this week This sentiment encapsulates why we built Monte Carlo. From day one we believed two things: 1. Within 5 years data would become a high visibility customer facing product — much like your website, infrastructure or applications. 2. Modern data quality management has failed to evolve with the enterprise data estate. 5 years later, both of these have proven to be true — and its why we work with hundreds of enterprises to unify the monitoring of their data, systems and code into one platform. The outcome is an 80%+ reduction in what we call "data downtime". We believe 5 years from now it will be table stakes for data teams to maintain the same levels of uptime as their counterparts in engineering. We're building a platform to make that possible — come join us!
The data estate is evolving. Data quality management needs to evolve too. As the complexity of data architecture, data systems, and even data teams continues to explode, the limitations of testing and traditional data quality monitoring become more and more apparent. In the early days of the cloud era, we needed visibility into software. In the AI era, we need visibility into data - and then some. To achieve true data reliability in the age of AI, modern enterprises need a data observability solution that provides end-to-end visibility into your data, systems, and code. Here’s why: ✅ End-to-end coverage: A tool that’s limited to a single point in your pipeline—or constrained to the data itself—will always have more blindspots than safeguards. Data observability monitors the data, systems, AND code - not just one piece of the puzzle. ✅ Extensibility: Data observability is ready to go out of the box and is broadly extensible to maximize coverage as your data volumes and domains grow. ✅ Future coverage: Data observability is designed for modern data quality needs—with a dedicated data observability solution, your coverage will evolve with your data environment so you always have the most current solution for the most prescient data quality challenge. As the category creator, Monte Carlo is recognized by industry analysts and 400+ enterprise customers as the undisputed leader in data observability. Want to know how data observability is powering the next evolution of data quality? Read more: https://lnkd.in/dTkVkuDc #dataobservability #dataquality #AI #GenAI #dataengineering
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As the demand for more intelligent, real-time applications grow, organizations are realizing the need for data to be not only secure but easily discoverable across diverse teams. A well-architected data catalog goes beyond simple documentation—it lays the groundwork for dynamic, cross-functional collaboration, enabling data engineers, privacy experts, and application developers to operate from a single, coherent source of knowledge. By centralizing data schemas, businesses can scale governance efforts, ensuring policy compliance and data security audits happen in parallel with innovation, rather than becoming bottlenecks. This leads to faster product iteration, empowering teams to develop solutions that leverage accurate, up-to-date data without manual intervention or policy friction. What’s more, modern AI workflows depend on this seamless access to trustworthy data to train models, fine-tune algorithms, and deliver insights that matter. AI-driven data discovery and recommendation systems now bridge the gap between data producers and consumers, ensuring valuable data reaches the right hands at the right time. As businesses increasingly embrace AI and cloud-native strategies, creating scalable, intelligent data infrastructure is quickly moving from a luxury to a necessity for staying ahead of the competition. I’d love to hear perspectives!
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WEBINAR | "Like with any technology, you need to define the use cases. You need to think who is going to use my data catalog? What are they going to use it for? And why does that matter? The why is certainly the most important. If the outcomes are unclear you start to venture into panic mode where you're delivering disjointed use cases." Many organizations have tried to adopt, buy or build a data catalog. But many of those initiatives are abandoned or fizzle out and fail. But understanding your data has never been more important, especially in the age of AI. So how can you ensure that a project as critical as data catalog and metadata management is successful? In this webinar, David Neil from Amplifi and Ozge Mertyurek from Alex Solutions take a deep dive all things data catalog and metadata management and cover a host of topics including: ▶ Common reasons data catalog projects fail ▶ Practical tips on how to prepare so they don’t ▶ The crucial role people play in successful data catalog implementations ▶ How to assess your readiness for a data catalog initiative You can watch the full session here ▶ https://lnkd.in/eScGvx2e #DataCatalog #MetadataManagement
Amplifi x Alex Solutions | How to get data catalog right
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Some practical prompts from Amplifi (Europe)'s very own David Neil to inspire you to stay resilient in your #DataGovernance journey - particularly when adopting a Data Catalog. We can't recommend this session enough! https://lnkd.in/gd8AZhDW
WEBINAR | "Like with any technology, you need to define the use cases. You need to think who is going to use my data catalog? What are they going to use it for? And why does that matter? The why is certainly the most important. If the outcomes are unclear you start to venture into panic mode where you're delivering disjointed use cases." Many organizations have tried to adopt, buy or build a data catalog. But many of those initiatives are abandoned or fizzle out and fail. But understanding your data has never been more important, especially in the age of AI. So how can you ensure that a project as critical as data catalog and metadata management is successful? In this webinar, David Neil from Amplifi and Ozge Mertyurek from Alex Solutions take a deep dive all things data catalog and metadata management and cover a host of topics including: ▶ Common reasons data catalog projects fail ▶ Practical tips on how to prepare so they don’t ▶ The crucial role people play in successful data catalog implementations ▶ How to assess your readiness for a data catalog initiative You can watch the full session here ▶ https://lnkd.in/eScGvx2e #DataCatalog #MetadataManagement
Amplifi x Alex Solutions | How to get data catalog right
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