5 Key Insights from the Webinar Bringing Knowledge Graphs to Life
Here are five key insights in case you missed the recent Quantexa webinar, Bringing Knowledge Graphs to Life 1. Introducing Knowledge Graphs The webinar gave a comprehensive introduction to knowledge graphs and why they’re hot right now. , , , and delved into how knowledge graphs, enabled through improved technology, bring a profound representation of entities and their relationships at scale, fostering great opportunities for impactful analysis and visualization. Knowledge graphs are applied in different ways, for graph analytics, semantic encoding, and delivering context to Large Language Models (LLMs) in AI. 2. Advantages and Challenges of knowledge graphs While there are numerous advantages of knowledge graphs, there are challenges on the journey. Depending on where and how data is sourced, knowledge graphs can be incomplete. The team emphasized the critical necessity of data quality and efficient entity resolution, because any failures diminish the ability to extract accurate and novel insights. Also, as knowledge graphs are normally very large, that compounds the need for accurate entities given the storage and compute investment, but also brings challenges of interacting with, visualizing and analyzing graph information, as Aaron noted, important for data scientists. 3. Is a Graph Database Needed? The panel noted the perceived intersection of knowledge graphs with graph databases, a domain well-marketed by graph database vendors. Ben remarked on how graph databases efficiently store and query knowledge graph data, but as knowledge graphs adapt with your organization’s data and needs, it’s important not to lock information away and be constrained by a single database. Ana highlighted how knowledge graphs can and should work across your organization’s data platforms and software. Aaron noted how data scientists, who thrive on iterative ad-hoc investigation and batch processing, benefit from direct access to knowledge graph structures. 4. Practical Implementation of knowledge graphs Knowledge graphs can be implemented across a wide spectrum of industries ranging from drug discovery to telecommunications and supply chains, and into functions like risk modeling, fraud detection, sales and marketing opportunities. Whatever the use case, it’s only a hop, skip and a jump from mainstream “tabular thinking” to “thinking graph,” given the elevation of expanded relationship information, i.e., edges, across entities, i.e. nodes. 5. Transformational Effects of knowledge graphs The panelists shared how knowledge graph technology had revolutionized their respective roles before and during their time at Quantexa. Ana pointed to the innovation opportunities leading to increased work satisfaction. Ben too appreciated how knowledge graphs unraveled unique solutions and allowed them to tackle complex problems. Steve pondered how the evolution of computational knowledge can drive change and unlock value across decision-making, science and engineering. This webinar is well worth watching to learn about the increasingly prominent role of knowledge graphs in data analysis, AI and decision-making. View the full webinar recording at Bringing Knowledge Graphs to Life Explore QKnowledgeGraph capability in the Quantexa Documentation
Exploring the Challenges of Achieving a Single View in Datawarehousing
When it comes to achieving a single view of individuals or businesses in Datawarehousing, several key insights emerge: 1️⃣ Data Integration: Integration is a critical aspect. Organizations often struggle with merging data from disparate sources such as customer databases, transaction systems, and marketing platforms. Ensuring seamless data integration is essential for a unified view. 2️⃣ Data Quality: Data quality plays a vital role in establishing a reliable single view. Inaccurate, incomplete, or inconsistent data can hinder decision-making and analysis. Implementing data cleansing processes and validation mechanisms are crucial steps towards maintaining high-quality data. 3️⃣ Data Silos: Data silos, where information is isolated within different systems or departments, pose a significant challenge. Overcoming these silos requires breaking down barriers, implementing data governance practices, and establishing data sharing mechanisms. 4️⃣ Business Context: Contextual understanding is crucial for creating a comprehensive view. Data needs to be interpreted within the specific business context to derive meaningful insights. Adapting to evolving business requirements and aligning data consolidation efforts accordingly is vital. Questions I always have and continue to ask are: 🔸 How do you address the complexities of data integration when combining data from diverse sources? 🔸 What approaches have you found effective in ensuring data quality throughout the process? 🔸 Have you encountered challenges in breaking down data silos? How did you overcome them? 🔸 How do you incorporate the business context into your data consolidation efforts? 🔸 Are there any specific tools or technologies you recommend for achieving a single view in Datawarehousing? Please share your thoughts and let's learn from one another!To use a Graph DB or not?
Graph databases (Graph DB) are gaining popularity as an alternative to traditional relational databases due to their ability to manage highly interconnected data. However, the decision of whether or not to use a Graph DB is dependent on several factors and is not straightforward. Some users of Graph DB technology have expressed frustration due to a lack of tangible business outcomes after investing in projects for two years or more. They are now questioning the need to continue using the technology and paying for licenses and maintenance. Some of the most complained about aspects were performance issues, complexity and flexibility. It is essential to note that Graph DB technology is not a "magic stick" and requires significant pre-work to create meaningful connections between data. Despite its advantages in managing complex data relationships, Graph DB is a data store after all, and has some shortcomings. If you have experience using Graph DB , I would love to hear about your experience and what use cases you've use/ed it for. Cheers
Network Analysis of ICIJ data shows how to stop Russian oligarchs in their tracks
I thought this was really cool and wanted to share. Complex systems of secrecy: the offshore networks of oligarchs: and to really rein in Putin’s allies’ wealth, governments should target their financial enablers, a new study suggests, read more here: Analysis of ICIJ data shows how to stop Russian oligarchs in their tracks - ICIJ To really rein in Putin’s allies’ wealth, governments should target their financial enablers, a new study suggests.Explore the Latest Articles in the Community Library 📚
Our ever-growing Community Library is filled with articles, blogs, and useful resources. Check out the latest articles below, whether you’re just starting out with Quantexa or a seasoned pro, there’s something here to help everyone learn and flourish. Service Operations 📖Quantexa Monitoring Series 📖Quantexa Platform Monitoring 📖Quantexa Platform Monitoring - Key Metrics and Log Entries 📖Quantexa Platform Monitoring - Moving Beyond the Minimum 📖Quantexa Application Monitoring - Introduction 📖Quantexa Application Monitoring - Getting Started with Toolchains Service Build and Transition 📖2.7 Quantexa Upgrade Guide 📖How to make the most out of Intellij IDEA 📖Running a Data Discovery Process 📖When and How to Use DQA Statistics Functionality 📖Project Management Best Practices: An Upgrade Journey 📖Docker for Quantexa Implementations 📖Spark Cluster - Resource Management Service Design 📖Setting Up Infrastructure and Underlying Platforms 📖Setting Up Infrastructure and Underlying Platforms: Cloud Edition 📖Setting Up Infrastructure and Underlying Platforms: On-Premise Edition 📖Using QPython for Analytics and Data Science Teams 📖Quantexa Platform Security Design for Solution ArchitectsData Quality is STILL a fundamentally unsolved issue!
Having met a couple of customers in the banking sector this week in South East Asia, a real pain that have been constantly shared was data quality. That's due to incomplete information, inconsistency, lack of standardized entries and so on. Issues that we all probably know of. Not that's a surprise of any sort, but the fact that traditional ways are still used to tackle the ever existing and growing issue is what puzzles me. In an ever evolving world where we are witnessing advancements in AI and other areas at unprecedent pace, and seeing organizations still struggle with foundational challenge should not be the case. It goes without saying the importance of proper data foundation. Anything less than that would lead to improper analytics, entity resolution, decisioning, etc. I'll probably need to (and will) write a blog about this in the coming weeks. I'm keen to hear from anyone about their approach/vision in tackling data quality issues, and what are some of your most pronounced challenges? Cheers
📣Upcoming Webinar: The Biggest Challenges in Data Quality: How Far Can AI Go to Solve Them? 📣
In this webinar, Dan Onions, Global Head of Data Management at Quantexa, and Martin Maisey, Head of Data Management EMEA, will delve into the pressing question on every data professional's mind: "How can AI help me?" Unlock the full potential of your data strategy: As AI technologies, particularly LLMs, become increasingly integral to data management strategies, ensuring the quality and reliability of these systems' outputs is paramount. Our experts will explore the critical role of foundational data quality in harnessing AI effectively and responsibly, and address key challenges, such as achieving consistency and accuracy in AI-generated outputs and aligning them with regulatory standards already on the horizon. Attendees will gain insights into practical applications of AI in the real world, understanding how to make AI outputs on data trustworthy across the entire organization. Register Your Place Here: The Biggest Challenges in Data Quality: How Far Can AI Go to Solve Them? (quantexa.com)
BLOG: Helping CSPs Achieve Relevant Insight that Maximize Data Value
Check out our latest CSP blog: Helping Communication Service Providers (CSP) Achieve Relevant Insights that Maximize Data Value Understand how Quantexa and Google Cloud help you to become more data-driven and customer-centric with Decision Intelligence Read the blog here: Helping Communication Service Providers Maximize Data Value Learn how Communication Service Providers can become more data-driven and customer centric through unifying data from previously siloed and scattered points. #data #decisionintelligence #Qalliancespowered
🎥 Webinar: Data Understanding Best Practice
Head of Business Analysts, , Senior Principal Data Engineer, , and Principal Data Engineer, , present on delivery best practices for Data Understanding. Data is fundamental to the Quantexa Platform. To maximize its potential, it is crucial to have a comprehensive understanding of data when designing and configuring your Quantexa deployment. Learn the best practices for achieving optimal data understanding and enhancing the effectiveness of your Quantexa solution. Further Reading: https://community.quantexa.com/kb/categories/113-data-source-onboarding https://community.quantexa.com/kb/categories/110-data-source-onboarding-document-design https://community.quantexa.com/kb/categories/111-data-source-onboarding-entity-design https://community.quantexa.com/kb/categories/112-data-source-onboarding-data-quality
