Arkodeep G.’s Post

In the rapidly evolving digital landscape, data modeling is not a relic of the past but a vital component of the future. As data leaders, your ability to adapt and integrate modern data modeling practices will determine your organization’s success in the years to come. https://lnkd.in/dniK5AWK #Technologynews #AI #Technews #cioinfluence

Two top priority initiatives for businesses today: How do we leverage AI to drive business efficiency? How do we ensure a proper process for Data Governance? Introducing Atlan AI, the first ever copilot for Data Teams. Atlan AI is designed to be the copilot for every human of data - be it #dataanalysts , #datascientists , #dataengineers, or business users. Imagine data analysts simply asking for the data asset they're looking for, or inquiring about the meaning of a column name in a chat, just as they would with a colleague. Or just imagine data stewards saving countless hours of documentation for terms and tables, using their time to oversee data governance practices that will make their team more agile. Picture a data engineer receiving a simple note on the numerous transformations a data asset has gone through, from source to consumption, without having to go through complex SQL. Think true data democratization, with every person in business functions exploring data without having to learn SQL! Atlan AI is capable of making all this reality, and much more. Atlan is Forrester Wave Leader for Enterprise Data Catalogs for DataOps in 2022 and has had recognition by Gartner seven times in 2021. Most recently, in early 2023, we debuted as a leader in G2 in the categories of #datagovernance , #MachineLearning. #datacatalog , and #dataquality .

Data Analysts spend 80% of their time finding, cleaning, and organizing data on 20% analyzing it. Infoworks is using AI and automation to flip that so Data Analysts can rapidly create new data models and focus their time on analytics. #DataAnalytics  #DataEngineering  #Modernization #Data  #Automation  #chatgpt  #generativeai https://lnkd.in/gFDJX6Gd

The digital age has ushered in an abundance of unstructured data, challenging businesses to navigate this ocean of information for actionable insights. To address this, businesses must adopt sophisticated data extraction tools to gather and organize diverse data sources efficiently. These tools play a crucial role in transforming raw data into valuable insights, setting the stage for data-driven decision-making. However, the transformation of unstructured data into insights presents complexities due to its disorganized nature and exponential growth. Businesses need advanced technologies like AI, ML, NLP, text analytics, image recognition, and cloud computing to decipher and structure this data effectively. Following a systematic process of data collection, processing, analysis, and insight generation ensures maximum value extraction. Best practices include defining clear business goals, employing clean data practices, establishing multidisciplinary teams, and adhering to data privacy regulations. Common pitfalls to avoid include data slicing, overcomplicated tools, and bias in data interpretation. The future of data-driven business relies on embracing advancements in AI, ML, and integrating these technologies seamlessly into operations. In conclusion, embracing a data-driven future is imperative for businesses to thrive in today's dynamic landscape. By effectively harnessing unstructured data and transforming it into actionable insights, companies can unlock strategic opportunities and stay ahead of the curve. Read more here: https://lnkd.in/gj9NNWHG

📊 As a B2B enterprise data analytics and AI leader, you'll want to stay ahead of the curve! Here are the **Top Trends in #Data and #Analytics for 2024** that can transform uncertainty into opportunity: 1. **#AI Revolution**: The combination of **Large Language Models (#LLMs)** and **#ArtificialIntelligence (AI)** will profoundly impact the modern data stack. 🤖 2. **Improving #DataLiteracy**: Semantic models and knowledge graphs aren't just hot topics; they're essential for informed decision-making. 🧠 3. **Increasing #Security Challenges**: As data becomes more valuable, safeguarding it against threats is paramount. 🔒 4. **Emerging New #DataProducts**: Innovations like **data-as-a-service (#DaaS)** and **low-code analytics / #NoCode #DE #DS** will reshape the industry. 🚀 5. **Achieving More with Less Data**: Efficiency matters! Extract maximum value from your data assets. 💡 6. **Evolving #DataTeam Roles**: Adapt to changing roles and responsibilities in the dynamic data landscape. 🌟 Stay informed, embrace these trends, and lead the way! 🚀🔍📈 Read more https://lnkd.in/guK-BDUh to dive deeper into these exciting developments! 📚👇 ---------------------------------------------- *Follow me to join me in my journey as I lead the AI R&D Lab Kavi Global, the Leading #Data #Analytics & AI Firm in the #USA, and keep up with the latest #DataAnalytics & #AI News & #TechTrends2024

"#artificialintelligence and #datascience became front-page news in 2023. The rise of #generativeai , of course, drove this dramatic surge in visibility." During the past several months, MIT Sloan Management Review conducted three surveys of data and tech execs leading to a list of five “developing issues should be on every leader’s radar screen. ➡ #genai sparkles but needs to deliver value. .. GenAI has captured a massive amount of business and consumer attention .. but .. results suggest that although excitement about the technology is very high, value has largely not yet been delivered.” Only 5-6% of companies across the two surveys have had any production or deployment at scale. ➡ “Data science is shifting from artisanal to industrial. Companies feel the need to accelerate the production of data science models .. investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. ➡ Two versions of data products will dominate. .. organizations view data products in two different ways. Just under half (48%) of respondents said that they include analytics and AI capabilities in the concept of data products. Some 30% view analytics and AI as separate from data products and presumably reserve that term for reusable data assets alone. Just 16% say they don’t think of analytics and AI in a product context at all. ➡ Data scientists will become less sexy. Data scientists, who have been called “unicorns” and the holders of the “sexiest job of the 21st century” ..have seen their star power recede. A number of changes in data science are producing alternative approaches to managing important pieces of the work. One such change is the proliferation of related roles that can address pieces of the data science problem. This expanding set of professionals includes data engineers to wrangle data, machine learning engineers to scale and integrate the models, translators and connectors to work with business stakeholders, and data product managers to oversee the entire initiative. ➡ Data, analytics, and AI leaders are becoming less independent. .. increasing numbers of organizations were cutting back on the proliferation of technology and data “chiefs,” “ due to short tenures and confusion about the responsibilities”. “functions performed by data and analytics executives” are being “subsumed within a broader set of tech, data, & digital transformation functions managed by a “supertech leader” who usually reports to the CEO. .. These leaders will need to be highly business-oriented, able to debate strategy.. , and able to translate it into systems and insights that make that strategy a reality. Link to the article: https://lnkd.in/d6XQYChK #digital #techgovernance #innovation Dr. Martha Boeckenfeld Dr. Debashis Dutta Nafis Alam Tibo Louis-Lucas Dominick Namis Matt Gray Chris Donnelly

"#artificialintelligence and #datascience became front-page news in 2023. The rise of #generativeai , of course, drove this dramatic surge in visibility." During the past several months, MIT Sloan Management Review conducted three surveys of data and tech execs leading to a list of five “developing issues should be on every leader’s radar screen. ➡ #genai sparkles but needs to deliver value. .. GenAI has captured a massive amount of business and consumer attention .. but .. results suggest that although excitement about the technology is very high, value has largely not yet been delivered.” Only 5-6% of companies across the two surveys have had any production or deployment at scale. ➡ “Data science is shifting from artisanal to industrial. Companies feel the need to accelerate the production of data science models .. investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. ➡ Two versions of data products will dominate. .. organizations view data products in two different ways. Just under half (48%) of respondents said that they include analytics and AI capabilities in the concept of data products. Some 30% view analytics and AI as separate from data products and presumably reserve that term for reusable data assets alone. Just 16% say they don’t think of analytics and AI in a product context at all. ➡ Data scientists will become less sexy. Data scientists, who have been called “unicorns” and the holders of the “sexiest job of the 21st century” ..have seen their star power recede. A number of changes in data science are producing alternative approaches to managing important pieces of the work. One such change is the proliferation of related roles that can address pieces of the data science problem. This expanding set of professionals includes data engineers to wrangle data, machine learning engineers to scale and integrate the models, translators and connectors to work with business stakeholders, and data product managers to oversee the entire initiative. ➡ Data, analytics, and AI leaders are becoming less independent. .. increasing numbers of organizations were cutting back on the proliferation of technology and data “chiefs,” “ due to short tenures and confusion about the responsibilities”. “functions performed by data and analytics executives” are being “subsumed within a broader set of tech, data, & digital transformation functions managed by a “supertech leader” who usually reports to the CEO. .. These leaders will need to be highly business-oriented, able to debate strategy.. , and able to translate it into systems and insights that make that strategy a reality. Link to the article: https://lnkd.in/d6XQYChK #digital #techgovernance #innovation Dr. Martha Boeckenfeld Dr. Debashis Dutta Nafis Alam Tibo Louis-Lucas Dominick Namis Matt Gray Chris Donnelly

"#artificialintelligence and #datascience became front-page news in 2023. The rise of #generativeai , of course, drove this dramatic surge in visibility." During the past several months, MIT Sloan Management Review conducted three surveys of data and tech execs leading to a list of five “developing issues should be on every leader’s radar screen. ➡ #genai sparkles but needs to deliver value. .. GenAI has captured a massive amount of business and consumer attention .. but .. results suggest that although excitement about the technology is very high, value has largely not yet been delivered.” Only 5-6% of companies across the two surveys have had any production or deployment at scale. ➡ “Data science is shifting from artisanal to industrial. Companies feel the need to accelerate the production of data science models .. investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. ➡ Two versions of data products will dominate. .. organizations view data products in two different ways. Just under half (48%) of respondents said that they include analytics and AI capabilities in the concept of data products. Some 30% view analytics and AI as separate from data products and presumably reserve that term for reusable data assets alone. Just 16% say they don’t think of analytics and AI in a product context at all. ➡ Data scientists will become less sexy. Data scientists, who have been called “unicorns” and the holders of the “sexiest job of the 21st century” ..have seen their star power recede. A number of changes in data science are producing alternative approaches to managing important pieces of the work. One such change is the proliferation of related roles that can address pieces of the data science problem. This expanding set of professionals includes data engineers to wrangle data, machine learning engineers to scale and integrate the models, translators and connectors to work with business stakeholders, and data product managers to oversee the entire initiative. ➡ Data, analytics, and AI leaders are becoming less independent. .. increasing numbers of organizations were cutting back on the proliferation of technology and data “chiefs,” “ due to short tenures and confusion about the responsibilities”. “functions performed by data and analytics executives” are being “subsumed within a broader set of tech, data, & digital transformation functions managed by a “supertech leader” who usually reports to the CEO. .. These leaders will need to be highly business-oriented, able to debate strategy.. , and able to translate it into systems and insights that make that strategy a reality. Link to the article: https://lnkd.in/d6XQYChK #digital #techgovernance #innovation Dr. Martha Boeckenfeld Dr. Debashis Dutta Nafis Alam Tibo Louis-Lucas Dominick Namis Matt Gray Chris Donnelly

Why are ontologies important in data management and AI? 6 key reasons. I think ontologies are very important for the future of data management and AI, but why? 6 things on my mind, feel free to add what's missing: 1. They provide a formal model of concepts and relationships that enable shared understanding. By defining classes, properties, and restrictions, ontologies create a common vocabulary and semantics around a domain. This facilitates interoperability and integration across systems and organizations. 2. They enable automated reasoning and inference. The formal logic-based representations of ontologies allow logical inferences to be made, deriving new knowledge from asserted facts. This kind of automated reasoning allows systems to check consistency, analyze the implications of data, and make recommendations. 3. They structure and organize knowledge for reuse. Ontologies provide an abstract framework for categorizing and relating entities to support explainability and reuse across applications. This semantic structure enables knowledge to be modularized instead of rebuilt from scratch for every use case. 4. They support machine learning transparency and accuracy. Providing context around training data characteristics, relationships, constraints etc. ontologies can improve ML model transparency, fairness, and accuracy. They also support the validation and monitoring of model performance over time. 5. They help ground AI systems and balance the potential for hallucination. Large language models "hallucinate" false information if not properly grounded. Ontologies provide a formal factual framework to map each of our realities and ensure language models align to truth and facts. 6. And last but not least, they help align data engineers and data scientists. Data engineers focus on building data pipelines while data scientists focus more on unlocking potential...this can leave gaps in formal data modeling. Ontologies can provide a unified semantic model spanning the full data lifecycle from integration to analytics and machine learning. This bridges the gap by enabling data engineers to incorporate more meaning and structure upfront, while still supporting flexibility for data scientists downstream. In short, ontologies move from "half messy half organized" data to formally defining a shared map of an organization's reality around data. This additional meaning, which can sit on top of the current data warehouse, data lakes, or traditional database, enables more intelligent systems and more meaningful data integration across platforms and organizations. We need ontologies to create more lean, efficient, and resilient systems. It's not like you are going to tidy up your room with some magic overnight, so let's get to work!

https://lnkd.in/geCDtk5U In recent interview at the Gartner Data and Analytics Summit in London Nicola Askham and Tendü Yogurtçu, PhD discussed the value and challenges of Data and AI governance. Data governance has evolved from a regulatory compliance requirement to a strategic enabler of innovation and value creation. In the era of AI revolution, AI applications depend on high-quality, relevant, and contextualized data. Here are five steps that can help your organization drive value from AI applications through data governance: 1.     Assess the maturity of your data governance and data quality capabilities and identify areas for improvement. 2.     Align your data governance objectives and metrics with your business goals and desired outcomes from AI initiatives. 3.     Implement a data catalog or a data intelligence platform to discover, document, and govern your data assets and metadata across your organization. 4.     Enrich your internal data with trusted third-party data sources to add context and completeness to your data and enhance your AI models. 5.     Adopt a data mesh or data fabric approach that suits your organizational structure and data architecture and enables data democratization and collaboration. #datagovernance #ai #aiapplications #value #genai