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Generate large volumes of data without real, sensitive, or personally identifiable information. Ensure accuracy and reliability with automated quality assurance. Protect sensitive information and comply with data protection regulations. Tailor data generation to simulate diverse scenarios. Access valuable insights and information about synthetic data. Use synthetic data for data modeling,

LLM finally evolving to IVC&LLM We have been extensively working with L LM and the inclusion of analytics or tabular input and of course documents wow a standard neural net LLM was designed to process documents. It takes a bit more to put tabular data in context or a narrative. it seems to me that we need much more than a large language model. In fact, a business needs and information value chain large language model, frankly, because without the information in context, it is a very limited operational environment. And actually a LLM hallucinating is really, very poor data quality and business cannot afford that. On a positive note, this has tremendous potential from a operational, and processes perspective however, you have to follow some of the same practices from a data quality perspective, that we have been using already in data governance and data quality for years. It’s in evolution not a revolution. We need to focus on the synthesis, of business intelligence and artificial intelligence more comprehensively Ira Warren Whiteside Information Sherpa

Synthetic Data is useless if it's not accurate. 🔴 For organizations leveraging synthetic data here is a deep dive into the strategies that can enhance the quality and accuracy of synthetic data. 🔍 Quality Assurance & Metrics Quality starts with rigorous data quality checks and regular validation processes. Increasing data sources can enrich the context and diminish biases, improving both the fidelity and utility of synthetic datasets. Automated tools and visual inspections help identify and rectify errors in early stages, ensuring that inconsistencies are not propagated into the synthetic outputs. 📊 Investment in Robust Validation Techniques Validation is critical. Techniques such as the Kolmogorov-Smirnov and Total Variation Distance Test assess the statistical similarity between original and synthetic datasets, ensuring high fidelity. On the utility front, metrics like Prediction Score and Feature Importance Score indicate how well the synthetic data performs in real-world scenarios, predicting outcomes and identifying key variables effectively. 🛡️ Privacy Considerations Metrics like Exact Match Score and Linkability, help identify PII anonymization. A balanced approach between utility and privacy is essential to prevent potential data breaches while maintaining functional value. 🔄 Regular Reviews and Model Audits Continuous improvement through regular dataset reviews and comprehensive model audit processes helps in adapting to new challenges and changing data environments. This ongoing revision not only ensures sustained data accuracy but also helps in fine-tuning the synthetic data generation over time. 🌐 Advanced Training Techniques Increasing the volume of training examples significantly boosts the model's ability to encapsulate complex data relationships accurately. Techniques like SMOTE for handling imbalances or employing advanced anonymization for sensitive fields can refine data quality further. 👁️ Looking Ahead: The Trade-Offs and Decisions The delicate balance among fidelity, utility, and privacy highlights the inherent trade-offs in synthetic data generation. Organizations must prioritize according to specific needs and use cases. There’s no one-size-fits-all answer, but a thoughtful approach tailored to the unique demands of each project will drive the best outcomes. ------ Follow our newsletter for regular updates on Synthetic Data: https://lnkd.in/dZUJBB6e Request a demo: https://lnkd.in/dBiDwz-P Betterdata helps you access data 10x quicker: contact@betterdata.ai #DataScience #SyntheticData #AI #MachineLearning #DataQuality #PrivacySecurity

Receiving analysis and insights on data to guide decision-making is a dream for many companies. Having suggestions on actions to take in an almost proactive manner, with well-founded and clearly explained justifications, is highly desirable. Is this possible? Yes. Is it simple? Not quite. Besides the techniques I have discussed in previous articles—fine-tuning, talking to your data, RAG, etc.—there are additional details necessary to make this a reality. One of these is called CoT - Chain of Thoughts. Chain-of-Thought (CoT) is a reasoning methodology used in artificial intelligence, particularly in the training of large language models, to improve their ability to solve complex problems. In the CoT approach, a model is trained to generate a series of intermediate reasoning steps instead of jumping directly to a final answer. This process mimics how humans often solve problems by breaking them down into smaller, more manageable components. That means, every time you ask which product you must buy more or what would be the setup for a new production behind the scenes the LLM model is running a check list or following the way an analyst would solve this problem. Another thing very interesting with CoT is when you use a database with some methods to solve an specific task, this way the LLM can use this orientation every time it face the problem. Use your imagination.

https://lnkd.in/gfqc3yXB 1. Privacy and AI governance will become a top business priority 2. Enterprises will be keen to adapt to purpose-built LLMs rather than general-purpose LLMs 3. The power of orchestration will span the entire enterprise 4. The digital-first experience will evolve into the secure digital-first experience 5. The digital-first experience will evolve into the secure digital-first experience Blog – Top Technology Trends for 2024 - https://lnkd.in/gtYSAP5F CFO / Technology - https://lnkd.in/gW7FfmHQ Paul Young CPA CGA Senior Data and AI Thought Leader - Close, Consolidate, and Reporting Cycle Paul_Young_CGA@outlook.com #CFO #ChiefFinancialOfficer #financialanalyst #treasuryanalyst #Accountant #controller #analyst #internalauditor #businessanalyst #Treasurer #financemanager #managerofFPA #managerfinancialplanningandanalysis #budgetanalyst #costinganalyst #regulatoryanalyst #equityanalyst #operationalmanager #HRmanagement

Integrity and compliance are key to our sustainable success. We have developed a Smart Data approach to build quality data repositories for better data life-cycle management. It improves decision-making processes and allows us to spot problems before they occur 📊 Internally, Akkodis business consultants can work with your team by laying out multi-step channels for ensuring data is properly handled and employees are educated on the methodology of data science and operating within a highly transparent ethical framework. #Akkodis #SmartIndustry #SmarterFuture #Privacy #DataProtection

Integrity and compliance are key to our sustainable success. We have developed a Smart Data approach to build quality data repositories for better data life-cycle management. It improves decision-making processes and allows us to spot problems before they occur 📊 Internally, Akkodis business consultants can work with your team by laying out multi-step channels for ensuring data is properly handled and employees are educated on the methodology of data science and operating within a highly transparent ethical framework. #Akkodis #SmartIndustry #SmarterFuture #Privacy #DataProtection

Integrity and compliance are key to our sustainable success. We have developed a Smart Data approach to build quality data repositories for better data life-cycle management. It improves decision-making processes and allows us to spot problems before they occur 📊 Internally, Akkodis business consultants can work with your team by laying out multi-step channels for ensuring data is properly handled and employees are educated on the methodology of data science and operating within a highly transparent ethical framework. #Akkodis #SmartIndustry #SmarterFuture #Privacy #DataProtection

Integrity and compliance are key to our sustainable success. We have developed a Smart Data approach to build quality data repositories for better data life-cycle management. It improves decision-making processes and allows us to spot problems before they occur 📊 Internally, Akkodis business consultants can work with your team by laying out multi-step channels for ensuring data is properly handled and employees are educated on the methodology of data science and operating within a highly transparent ethical framework. #Akkodis #SmartIndustry #SmarterFuture #Privacy #DataProtection

Integrity and compliance are key to our sustainable success. We have developed a Smart Data approach to build quality data repositories for better data life-cycle management. It improves decision-making processes and allows us to spot problems before they occur 📊 Internally, Akkodis business consultants can work with your team by laying out multi-step channels for ensuring data is properly handled and employees are educated on the methodology of data science and operating within a highly transparent ethical framework. #Akkodis #SmartIndustry #SmarterFuture #Privacy #DataProtection