Know what language data is missing before collecting more.
DataKnobs organizes dialects, demographics, linguistic patterns, and usage contexts into a dataset intelligence layer for AI teams to analyze representation, pinpoint blind spots, and allocate limited collection resources effectively.
Your dataset includes a sufficient amount of Hindi, but lacks representation of Bhojpuri, Maithili, Rural Marathi, older speakers, and low-literacy usage
The difficulty lies not in just gathering more data, but in discerning the data that is relevant.
In Indian language AI, a large amount of data may obscure significant gaps in coverage. A dataset may appear extensive, but could lack representation of various dialects, regions, speaker groups, education levels, and real-world usage scenarios that are essential for improving model quality.
How do we know what data to collect?
Sort opportunities for collection ranking based on expected increase in representation, improvement in model quality, and cost-effectiveness.
How do we know what is missing?
Analyze the dataset in relation to population, usage, geography, dialect, and evaluation coverage goals.
How do we measure representation?
Measure the variance between the actual world, live traffic, assessment data, and training data.
How do we prioritize scarce budgets?
Suggest the following collection strategy that prioritizes maximizing coverage per dollar, rather than just focusing on the number of records per dollar.
Determine if the dataset accurately reflects the intended population.
The DataKnobs Dataset Intelligence platform allows for visibility into coverage across various language, dialect, geography, age, education, literacy, and usage contexts.
Population Reality
Dataset Reality
See missing language coverage as a map, not a spreadsheet.
Dataset Intelligence analyzes language and usage regions to create a comprehensible information geometry, emphasizing areas with high coverage, low coverage, and gaps that impact evaluation and model effectiveness.
Top Missing Regions
Coverage currently stands at 0.4%, falling short of the target of 8%. The language gap with the highest
Coverage is currently at 1.8%, falling short of the target of 9% with weak representation in
Coverage is at 0.3% with a target of 4%, with gaps in evaluation and underrepresentation in training
Age 65+ coverage is weak despite high downstream usability risk.
Language Coverage
Demographic Coverage
Usage Context
Prioritize scarce collection budgets by expected impact, not volume.
DataKnobs suggests collecting specific data based on representation gain, model-quality lift, evaluation coverage, and collection cost rather than requesting additional data.
If you can collect only one thing, collect Bhojpuri conversations.
Bhojpuri exhibits the widest representation gap, limited evaluation coverage, significant population impact, and a promising potential for improved model quality in the future.
Recommended Collection Plan
Distribute funds to the areas that enhance representation and evaluation standards the most.
From raw datasets to collection decisions.
DataKnobs Dataset Intelligence transforms dataset profiling into a practical process for AI teams, language-data teams, evaluation teams, and executives.
1. Map reality
Determine the target audience based on language, dialect, region, demographics, and usage context.
2. Profile datasets
Evaluate the distribution of the actual dataset, production traffic, training data, and evaluation data.
3. Detect gaps
Discover absent and inadequately represented areas through the utilization of information geometry and coverage divergence.
4. Recommend action
Focus on collecting the data with the highest return on investment, building benchmarks, fine-tuning, or creating a human review
Many teams are aware of the amount of data they possess, but DataKnobs reveals the gaps in their information.
The Dataset Intelligence for Indian language AI pinpoints gaps in language and demographic coverage, identifies representativeness gaps in datasets, and highlights where scarce data collection budgets can have the most impact.
Create an interactive prototype featuring the following screens: coverage home, language map, blind spots, collection ROI, and benchmark builder.
Review the UX flow