The dashboard gives a high level visual overview of the data that has been imported into your project. You can choose to see how your data breaks down via a specific import, or holistically with all your data. This can help you make informed decisions for future imports prior to creating searches and slices for export.
Project Dashboard Cards
Valuable aspects of the overview include breakdowns of your data within cards such as:
Imported documents are categorized as follows:
- Fully Searchable – The metadata was successfully extracted from these files and their content was OCR'd to create searchable text.
- Metadata Only – The metadata was successfully extracted from these files, but they are of a type that would not generate searchable OCR'd text, so only the metadata is searchable.
- Unsupported – These file types are not supported by Nextpoint's Data Mining app, so neither the metadata nor any text in these files will be searchable.
- Unknown – These file types are unknown/unreadable to Nextpoint's Data Mining app and could not be processed. Neither their metadata nor any text in these files will be searchable. This may also include files that have readability issues (e.g. a corrupt eml file or an encrypted spreadsheet)
Individual documents may contain multiple languages. Each document is categorized based on the "dominant" language – or most prevalent language – detected in the text.
Importing errors are categorized as follows:
- Unsupported Type – This file type is not supported by the Data Mining app and cannot be processed.
- Unsupported Size – The file size is too large to process.
- DeNIST – Computer system files and NSRL not generally user generated and therefore often not relevant to most litigation.
- Error – An error occurred while processing this file and/or extracting its metadata.
- Other – File specific issues that make them unreadable (e.g. corruption, encryption, empty files...)
The data timeline shows the frequency of documents through your data's date range. It will include custodian frequency along the timeline (if applicable).
Or view one of the other support resources in the Data Mining series: