Data Analysis

Logo for the Data Insight Service by the company synthetic images.
Data Insight
Our Data Insight service provides a systematic evaluation of your data, focusing on quality improvement and insightful analysis. This helps identify data gaps, errors, characteristics and patterns of your existing data.
Screenshot of Service Data Insight Analysis report document.

Comprehensive Data Insight Process

Detailed Assessment and Optimization Recommendations for Enhanced Data Quality:

The main objective of the analysis is to uncover unknown themes, trends or patterns in the data.
Based on this, errors are to be eliminated, and the data cleansed with regard to the present use case. For this purpose, the real image data provided is checked for 3 main aspects:

Quality check of annotations

We conduct a thorough review of your annotations to assess their accuracy and consistency.
Based on the findings, we provide specific recommendations for improving annotation quality, ensuring your data is well-prepared for training robust models.

Analysis on image level

Our analysis identifies potential blind spots of the individual data points at the image, feature or class level, highlighting areas where single data points might cause errors.
As a result, there will be concrete proposals for action to deal with the individual data points.

Analysis on data set level

We evaluate your dataset as a whole, identifying patterns and potential weaknesses that could affect model performance.
Based on the findings, we present practical suggestions for augmenting or generating synthetic data to address gaps, enhancing the representativeness and effectiveness of your dataset.
The extracted information is visualized in e.g. a defect dictionary, histograms or other visualizations like interactive applications. The presentations of the extracted information are accompanied by specific key findings and recommendations for processing and using the data in relation to the given application and use case.

Leading us to the next Step

Now that we know what data we have, how good it is, but also what data we are missing and what the machine needs to be successfully learned, we move on to dataset creation and the associated generation of synthetic data.
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