Thinknow Synthetic:Advanced Synthetic Data Solutions

an advanced synthetic data generation solution designed for market analysis. It combines generative neural networks with statistical analysis techniques. This methodology allows us to capture complex patterns in the available data, ensuring representativeness and coherence. Thanks to this approach, ThinkNow Synthetic generates realistic scenarios with an optimal balance between precision and diversity.

Key Features

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Multi-origin Data Collection

Panel Data: Access our extensive, culturally rich panel database from DigaYGane.com, featuring millions of data points collected over a decade from Hispanic, Black, AANHPI, and LGBTQIA+ communities.
Synthetic Data: Generate synthetic responses combining generative neural networks with statistical analysis techniques, ensuring culturally relevant and representative insights.

Enhanced Accuracy and Realism

Flexible Model Selection: Our approach applies a range of models, from statistical techniques to generative neural network methods, to create synthetic data that accurately reflects both single tables and complex, interconnected tables.
Comprehensive Quality Measures: Synthetic data is evaluated against real data across multiple dimensions, ensuring it upholds cultural diversity and accurately represents the target demographic groups.
Real-Time Generation: This methodology enables fast, on-demand data creation, allowing clients to access data whenever needed, even for highly specific or new questions.

Bias Mitigation

ThinkNow Synthetic is trained on multicultural data, prioritizing inclusivity, and minimizing biases. By incorporating a variety of communities and adjusting models accordingly, we ensure that generated data authentically reflects diverse populations.

Benefits:

  • Cost-Effectiveness: Reduce the expenses associated with large-scale surveys by supplementing panel data with synthetic data.
  • Data Completeness: Fill in missing responses and expand the diversity and volume of data for holistic analysis.
  • Cultural Relevance: Benefit from synthetic data specifically tuned to multicultural audiences, enhancing cultural insights.
  • Speed: Generate synthetic responses quickly and accurately, as opposed to waiting on traditional data collection.
  • Solution for Unbalanced Data in Market Analysis: Expands the original sample without losing human response patterns, ensuring more balanced and representative data.

Technical Overview:

AI-Augmented Data Simulation:

  • Augmented Surveys: Our solution enhances data collection by predicting opinions, filling in data gaps, and simulating real-world cultural patterns.
  • Realistic Data Emulation: Mimics real-world distributions while protecting privacy, providing a robust foundation for diverse audience research.

Customizable Models:

ThinkNow Synthetic enables tailored solutions, allowing clients to submit past survey data to further customize synthetic generation for unique cultural insights.

When the client has enough historical data, it is possible to train specific models capable of making predictions; in this case, we can adjust our model to anticipate or predict what will happen in future scenarios.

Some of these adaptable models are designed to perform information classification tasks. A clear example is the Fraud Detection system, which estimates whether a transaction or data delivery is likely fraudulent.

Applications:

  • Market Research: Gain a deep understanding of market trends with accurate, diverse consumer data.
  • Social Science Research: Study behaviors and societal trends across culturally varied groups with comprehensive, accurate data.
  • Policy Making: Access public opinion data that accurately represents diverse communities, supporting inclusive policy decisions.

How it Works

Client Engagement

We work with clients to understand their unique needs for accurate, culturally inclusive data.

Data Integration

ThinkNow Synthetic combines proprietary panel data with ai-generated synthetic data.

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Delivery

Our solution provides faster, accurate, and cost-effective insights, ensuring data reliability and relevance.

Synthetic Data Generation

Our models create data that recognizes real-world patterns, reflecting cultural diversity.

FAQs

An AI-generated synthetic sample is a data set artificially created using algorithms or artificial intelligence models rather than being collected directly from the real world.

A traditional sample collected in online panels is based on real data obtained from people who voluntarily participate in surveys or market research. These samples reflect the genuine experience and opinion of participants, although they may present self-selection biases, limitations in the availability of participants, and variable response rates.

In contrast, a synthetic sample is generated using algorithms and statistical models that simulate data from available sources to approximate the characteristics or distributions of the population. Since it does not depend on direct responses from people, it can be more flexible and faster to obtain, but its quality depends largely on the accuracy of the models and the source data used to create them.

When comparing synthetic samples with traditional samples obtained through online panels, several important advantages can be highlighted:

Speed ​​and scalability: Generating synthetic samples can be much faster than recruiting, filtering, and surveying participants in online panels. The volume of synthetic data can be easily scaled without worrying about participants' availability or response rate.

Reduced costs: By not requiring incentives, management, or maintenance of large panels of people, the costs associated with creating the samples are significantly reduced. This also reduces the expense of collection platforms and follow-up processes, which is especially useful for research projects with limited resources.

Full control over the sample composition: Data sets can be designed with particular demographic or behavioral characteristics (for example, minority groups that are rare in conventional panels). This allows for greater precision in studies that require samples with certain combinations of variables that are difficult to collect in actual practice.

Each technique has a different purpose: synthetic data creates information, weighting adjusts the relevance of existing data, and predictive modeling anticipates the future based on patterns.

Synthetic data: This is data artificially created by AI instead of collected from reality. It follows patterns similar to real data and is used when there is not enough information available or when you want to avoid exposing sensitive data. It helps train systems, improve accuracy, and reduce costs.

Weighting: This mathematical adjustment gives more importance to specific data to balance a sample. It does not create or predict new information, it only corrects imbalances in surveys or studies, ensuring that the results are more representative.

Predictive modeling: Uses data (real or synthetic) to predict what might happen in the future. It works by training a model to identify patterns and make estimates, such as predicting whether a customer will buy a product based on their purchase history.

Quality data: The synthetic sample must be based on information relevant to the study's objective.

Key variables: Include demographic data (age, gender, income, education), geographic data (region, locality), and other factors such as interests, lifestyle, and consumer habits.

Specific information: Incorporate data specific to each study or area of ​​research.

Statistical reference: Use official data (censuses, national surveys, market reports) to calibrate the sample and ensure its representativeness.