Innovations in Synthetic Data for AI and Market Research

🚀 Generating High-Quality Synthetic Data — While Preserving Feature Relationships In today’s data-driven world, organizations urgently need realistic data for testing, development, and AI training—but privacy concerns and regulations like HIPAA and FERPA often make using real data impossible. That's where structured synthetic data comes in. Harpreet Singh and I developed a synthetic data generation pipeline that not only mimics the distribution of real data—but also preserves the relationships between features, something many approaches overlook. 🧠 Here's a look at what sets this approach apart: ✅ Preprocessing - Imputes missing values (median/mode/“Unknown”) - Encodes categoricals smartly: binary, one-hot, or frequency-based - Fixes skewed features using Box-Cox - Standardizes numerical data - Stores all parameters for full reversibility 🔍 Clustering with HDBSCAN Real data often comes from diverse subgroups (e.g., customer segments or patient cohorts). Using HDBSCAN, we automatically detect natural clusters without predefining their number. This ensures minority patterns aren’t averaged out. 📊 Per-Cluster Modeling Using Copulas Each cluster is modeled independently to capture local behavior. - First, we fit the best marginal distribution for each feature (normal, log-normal, gamma, etc.) - Then, using copulas (Gaussian, Student-T, Clayton), we preserve the inter-feature dependencies—ensuring we don’t just get realistic individual values, but also realistic combinations This step is crucial. It avoids scenarios like low-income customers buying large numbers of luxury items—something that happens when relationships aren't preserved. 🎯 Generation and Postprocessing - Samples are drawn from the fitted copula - Inverse CDF restores each feature’s shape - Reverse standardization and decoding returns everything to the original format - Categorical encodings are fully recovered (binary, one-hot, frequency) 🧪 Validation The pipeline doesn't stop at generation—it rigorously validates: - Kolmogorov-Smirnov and chi-square tests for distributions - Correlation matrix comparison (Pearson, Spearman) - Frobenius norms for dependency structure accuracy - Cluster proportion alignment ⚠️ Limitations: All variables are treated as continuous during dependency modeling—so while relationships are preserved broadly, some nuanced categorical interactions may be less precise. ✅ Use Cases: - Safe test data for dev teams - Realistic ML training data - Simulating rare edge cases - Privacy-preserving analysis in finance, health, and retail 📚 Full breakdown with code is here: 👉 https://lnkd.in/gS5a3Sk7 Let us know what you think—or if you'd like help implementing something similar for your team. If you find it useful, don't shy away from liking or reposting it. #SyntheticData #Privacy #AI #MachineLearning #DataScience #Copulas #HDBSCAN #DataEngineering

One of the hardest parts of fine-tuning models? Getting high-quality data without breaching compliance. This Synthetic Data Generator Pipeline ia built to solve exactly that, and it is open-sources for you to use! You can now generate task-specific, high-quality synthetic datasets without using a single piece of real data, and still fine-tune performant models. Here’s what makes it different: → LLM-driven config generation Start with a simple prompt describing your task. The pipeline auto-generates YAMLs with structured I/O schemas, filters for diversity, and LLM-based evaluation criteria. → Streaming synthetic data generation The system emits JSON-formatted examples, prompt, response, metadata at scale. Each example includes row-level quality scores. You get transparency at both data and job level. → SFT + RFT with evaluator feedback We use models like DeepSeek R1 as judges. Low-quality clusters are automatically identified and regenerated. Each iteration teaches the model what “good” looks like. → Closed-loop optimization The pipeline fine-tunes itself, adjusting decoding params, enriching prompt structures, or expanding label schemas based on what’s missing. → Zero reliance on sensitive data No PII. No customer data. This is purpose-built for enterprise, healthcare, finance, and anyone who’s building responsibly. And it works: 📊 On an internal benchmark: - SFT with real, curated data: 79% accuracy - RFT with synthetic-only data: 73% accuracy That’s huge, especially when your hands are tied on data access. If you’re building copilots, vertical agents, or domain-specific models and want to skip the data wrangling phase, this is for you. Built by Fireworks AI 🔗 Try it out: https://lnkd.in/dXXDdyuM

I’m excited to share that I have co-authored a new World Economic Forum report: 📘 Synthetic Data: The New Data Frontier 👉 Read the report: https://lnkd.in/ei2uhgSK Synthetic data is no longer experimental; it has become a cornerstone of innovation across various sectors. It can: ✔️ Fill data gaps and strengthen AI models ✔️ Protect privacy while enabling research and collaboration ✔️ Support fairness by simulating underrepresented groups But with its promise comes risks: bias reinforcement, model collapse, privacy leaks, and misuse. This Primer, developed by the Global Future Council on Data Frontiers, outlines definitions, use cases, risks, and recommendations for leaders across the public, private, and civil society sectors. We aim to help decision-makers harness synthetic data responsibly, striking a balance between innovation and trust, equity, and accountability. I look forward to the discussions this will spark on how we can responsibly build the future of data. It was great collaboration with a group of thinkers and leaders from all over the world. Lauren Woodman, Arun Sundararajan, Francesca Rossi, Justine Gauthier, Rachel Adams, Kathy Baxter, Alberto-Giovanni Busetto, Nighat Dad, Sandhya Devanathan, Sara Hooker, Maui Hudson, Kathrin Kind, Jae Lee, Angela Oduor Lungati, Vukosi Marivate, Graciela Marquez Colin, Fawad A. Qureshi, Crystal Rugege, Anna Tumadóttir, Linghan Zhang, Casey Price, Karla Yee Amezaga, Henry Ajder, Khaled El Emam, Stefaan Verhulst, Elio Atenógenes Cathy Li, Audrey Duet, Maria Basso, Daniel Dobrygowski, Leandro Loss, Martina Szabo, Lea Weibel #SyntheticData #AI #DataGovernance #WEF #DataFrontiers #ResponsibleAI #DigitalTrust #DataEquity #DataInnovation Snowflake

Exciting developments in generative retrieval! Researchers from Amazon have unveiled innovative strategies for creating synthetic data to train domain-specific generative retrieval models. This approach tackles the challenge of limited in-domain query annotations, offering a scalable solution for various domains. Key highlights: 1. Two-stage training framework:   - Stage 1: Supervised fine-tuning to decode document identifiers from queries   - Stage 2: Preference learning to refine document ranking 2. Synthetic data generation techniques:   - Multi-granular query generation (chunk-level and sentence-level)   - Constraints-based queries incorporating domain-specific metadata   - Context2ID data for document content memorization 3. Preference learning enhancements:   - Use of Regularized Preference Optimization (RPO)   - Strategic selection of hard negative candidates 4. Impressive results across diverse datasets:   - MultiHop-RAG, AllSides, AGNews, and Natural Questions   - Competitive performance against off-the-shelf retrievers The researchers' approach leverages large language models (LLMs) like Mixtral 8x7b for query generation, outperforming specialized models like docT5query. Their method also generalizes well to different types of document identifiers, including semantic and atomic identifiers. This work opens up new possibilities for building effective domain-specific retrieval systems without extensive manual annotation. It's a significant step forward in making generative retrieval more accessible and adaptable to various domains.

When it comes to AI-powered market research, it's time to challenge the conventional wisdom. Replicating human survey results is often seen as the gold standard, but what if that's not enough? Traditional surveys, and even naive AI models, tend to overstate consumer intentions, missing the mark on real-world actions. Through an experiment with Ask Rally's language models, we found that a basic model replicated survey biases (78% of simulated responses favored an eco-friendly car), yet switching to a more advanced model cut this figure to 37%, much closer to actual market behavior. The takeaway? The true advantage lies not in mirroring traditional methods but in choosing and calibrating AI models that bridge the intention-action gap. This approach not only aligns synthetic research with reality but could redefine how we predict consumer behavior altogether.

Your competitor just trained an AI model on ten million customer transactions. You have access to 300,000. And half of those are locked behind GDPR restrictions you can't legally bypass. This isn't a hypothetical. This is the reality facing every CTO I've spoken with in the past six months. The AI race isn't being won by the smartest algorithms anymore. It's being won by whoever solves the data access problem first. Here's what the market is telling us. 75% of businesses will use generative AI to create synthetic customer data by 2026, exploding from under 5% in 2023 Gartner. That's not gradual adoption. That's a complete market flip in under three years. Studies show synthetic data cuts collection costs by 40% while improving model accuracy by 10% EC Innovations. But the real number that should wake you up: 60% of data leaders will face critical failures managing synthetic data by 2027, risking governance, accuracy and compliance Gartner. We're rushing toward a solution that most organizations aren't equipped to handle. I've spent 15 years watching technology waves crash over enterprises. This one feels different. The gap between early movers and laggards isn't measured in quarters anymore. It's measured in weeks. Manufacturing companies using synthetic sensor data for predictive maintenance are seeing 25% productivity gains and 70% fewer breakdowns Technostacks. Their competitors are still arguing about data sharing agreements. The math is brutal and simple. Real patient data takes 18 months to get approved for AI training. Synthetic patient data gets approved in three weeks. Same privacy protection. Better bias control. Synthetic data can reduce AI model biases by up to 15% EC Innovations because you can engineer fairness in rather than discover discrimination after deployment. Forrester now lists synthetic data as a top emerging technology for 2025, with regulators actively encouraging adoption in financial services, insurance, healthcare and the public sector Forrester. Read that again. Regulators are encouraging this. The same regulators who spent the last decade building walls around data are now opening gates to synthetic alternatives. Every enterprise leader faces the same choice right now. Wait for enough real-world data to accumulate naturally, falling further behind each month. Or learn to generate the future you need to train for. The winners won't be determined by who has the most data. They'll be determined by who figures out how to manufacture the right data fastest. That transformation is already underway. The only question is whether you're engineering it or getting engineered out of relevance.

𝗡𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱! Medical imaging is packed with hidden clinical biomarkers, but privacy hurdles and data scarcity often keep this treasure trove locked away from AI innovation. Frustrating, right? That’s exactly what inspired me and Abdullah Hosseini to ask: Can we generate synthetic medical images that not only look real, but also preserve the critical biomarkers clinicians rely on? So, we dove in. Using cutting-edge diffusion models fused with Swin-transformer networks, we generated synthetic images across three modalities—radiology (chest X-rays), ophthalmology (OCT), and histopathology (breast cancer slides). The big question: 𝗗𝗼 𝘁𝗵𝗲𝘀𝗲 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗶𝗺𝗮𝗴𝗲𝘀 𝗸𝗲𝗲𝗽 𝘁𝗵𝗲 𝘀𝘂𝗯𝘁𝗹𝗲, 𝗱𝗶𝘀𝗲𝗮𝘀𝗲-𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻𝘁𝗮𝗰𝘁? • Our diffusion models faithfully preserved key biomarkers—like lung markings in X-rays and retinal abnormalities in OCT—across all datasets. • Classifiers trained only on synthetic data performed nearly as well as those trained on real images, with F1 and AUC scores hitting 0.8–0.99. • No statistically significant difference in diagnostic performance—meaning synthetic data could stand in for real data in many AI tasks, while protecting patient privacy. This work shows synthetic data isn’t just a lookalike—it’s a powerful, privacy-preserving tool for research, clinical AI, and education. Imagine sharing and scaling medical data without the headaches of privacy risk or limited access! Read the full paper: https://lnkd.in/eW6TM9H2 Get the code & datasets: https://lnkd.in/ek4wSkg3 #AI #Innovation #SyntheticData #DiffusionModels #MedicalImaging #HealthcareInnovation #DigitalHealth #Frontiers #WeillCornell #HealthTech #HealthcareAI #PrivacyPreservingAI #GenerativeAI #Biomarkers #MachineLearning #Qatar #MENA #MiddleEast #NorthAfrica #MENAIRegion #MENAInnovation #UAE #UnitedArabEmirates #SaudiArabia #KSA #Egypt AI Innovation Lab Weill Cornell Medicine Weill Cornell Medicine - Qatar Cornell Tech Cornell University

Synthetic patient data created with GPT-4o mirrored real neurosurgery cases so well that it reproduced key clinical findings and supported machine learning, without using any real patient info. 1️⃣ Real neurosurgery datasets are hard to access, share, and combine, limiting research and AI development. 2️⃣ This study tested if realistic fake data could stand in for real patients to identify risk factors and train prediction models. 3️⃣ GPT-4o generated lifelike patient data using just a description of how the real data behave, without access to actual patient charts. 4️⃣ The synthetic data preserved known risk factors for loss of functional ability after surgery and longer ICU stays. 5️⃣ It also supported building a model that predicted functional decline with strong accuracy when tested on real patients. 6️⃣ Key clinical metrics, like surgical complexity, complications, and preoperative status, were faithfully reproduced. 7️⃣ Compared to older tools, GPT-4o made more accurate data and was easier to use, needing only a plain-language prompt. 8️⃣ No real patients were re-identified or mimicked, preserving privacy even at scale. 9️⃣ This opens the door to sharing high-quality neurosurgery data across institutions, no consent hurdles, no ethics delays. 🔟 With better tools, synthetic data could finally unlock broader collaboration and reproducibility in neurosurgery research. ✍🏻 Austin A. Barr, Eddie Guo, Brij Karmur, Emre Sezgin. Synthetic neurosurgical data generation with generative adversarial networks and large language models: an investigation on fidelity, utility, and privacy. Neurosurg Focus. 2025. DOI: 10.3171/2025.4.FOCUS25225

Over the last 15 years, I have watched 100+ data companies emerge, scale and sometimes disappear. The next big wave in B2B data is going to be a transformational pivot and not an incremental improvement. Amit Vasudev and I have had a few recent discussions on this topic. Here’s how we see the evolution happen and what comes next : 1. Data 1.0 : Legacy Data Vendors   Most existing data vendors fall into this category. Data is captured in a snapshot of time and rapidly decays. Venbdors often license data from each other or run the scraping infrastructure themselves. Data is delivered as rows in spreadsheets or via APIs Problem: Data is static, lacks continuity and context. Vendors increasingly face continuous downward pricing pressures and regulatory risks Moat: Technical difficulty and Risk tolerance 2. Data 2.0 : Data Orchestration Rise of Clay and the DataOS category marks a significant shift in the data landscape Orchestration platforms often do not own the data, instead they own the user and the workflows By abstracting 50+ vendors behind a single interface and currency, we have seen a permanent shift in market power away from individual data providers towards orchestration vendors This layer is also making data programmable through the integration of signals, workflows, enrichment and AI inferencing    Problem: Complexity and Rapid commoditization. A red ocean with margins stacked on upstream vendors that have zero platform loyalty Moat : Short term stickiness via data contracts, single currency and UX. Not sure about moat in the long run. 3. Data 3.0 Personalized Data Layer This is where the real shift is happening The future of data is not new fields, but better decisions driven by contextual reasoning. The next wave of data vendors will : - Deliver personalized data via API or MCP server - Operate as continuous real time data streams - Rely on AI inferred synthetic data signals - Generate data through AI research, micro surveys, social mining, targeted crawls and LLMs Data 3.0 will increasingly be "generated" and not collected Problem : This model is harder to build. Synthetic data introduces inaccuracy and uncertainty if not done well Moat : Large engaged audience. Research data flywheel with proprietary data insights. Synthetic data algorithms and heuristics How Zeer is contributing to Data 3.0: - Discovering continuous real time demand inside accounts - Uncovering true (and variable) ICP and buyer personas in target accounts - Building infrastructure to deploy AI research agents and targeted micro surveys to produce personalized data at scale Closing: Data 3.0 is already here and it is a transformational shift from before As the market power realigns, many legacy data vendors will struggle to adapt Next generation of billion dollar data companies will be built by those who collect, generate and deliver personalized insights and not rows of records WDYT? Please share your thoughts below or reach out directly.

Stop guessing what your persona wants based on a static PDF from 2022. Most marketers treat personas like a creative writing exercise. They give them a name like "Marketing Mary," assign her a hobby like "enjoys hiking," and then wonder why their conversion rates are tanking. The reality? Mary doesn't exist. And your guesses about her behavior are usually wrong. We’ve started moving away from static personas toward Synthetic Audience Modeling. Instead of a flat description, we’re building AI agents trained on actual historical data, call transcripts, and customer sentiment to act as a "Synthetic Audience." We don’t just "think" about how they’ll react to a new creative. We run the creative through these models. We role-play the entire user acquisition funnel before we spend a single dollar on media. The results are brutal and honest. The synthetic model doesn't care about your feelings or your "beautiful" brand colors. It tells you exactly where the friction is, why the hook failed, and what actually triggers a click. Optimization isn't about being a "visionary" anymore. It’s about building better models to simulate reality so you don't have to pay for expensive mistakes in your live campaigns. If you’re still relying on "gut feeling" for your UA strategy, you’re basically just gambling with your client’s budget. Stop guessing. Start modeling. #GrowthHacking #UserAcquisition #AI #MarketingStrategy #SyntheticData #NoBS