Automatic Document Classification
Automate Document Classification
Leave Template Building and Other Guesswork Behind
Look at All of the Documents in the Set
Massive Labor Savings over Manual Review
What if you had the ability to automatically classify documents by data similarity for both electronic and scanned paper documents and your organization could see all of the document types that exist in your storage and other systems?
On an automated basis without much by way of human intervention, our technologies have enabled companies to turn unstructured documents into groups of structured document types such as: invoices, sales agreements, service agreements, etc. At the same time, these companies have been able to significantly enhance their storage taxonomies from cluster content – in one case virtually doubling the number of classes in the taxonomy assuring easier access to enterprise information.
We don’t get "confused" by file types. It doesn't matter whether a document originated from Word, PDF or a scanned paper copy. We group documents based upon their data similarity taking into consideration graphical, textual and zonal considerations. Unlike Boolean guesswork or reliance on random exemplars, you are assured that you deal with all of the documents…they either cluster or they are outliers to be dealt with separately.
For e-Discovery litigation holds or production, this means that you can be assured that you have looked at everything.
For document classification, this means that you can setup rules and retention policies based on the document type and automatically apply them to all documents of that type throughout your enterprise - all done from your normal Line-Of-Business (LOB) application (Documentum, OpenText, Sharepoint, Alfresco, OnBase, etc.)
But there is more…
- Deduplication procedures are not restricted to discrete file types. So often people have taken a significant document and converted from a word file, to a pdf, to a jpeg etc. – we identify duplicates and near duplicates across file types;
- Retention/reduction/remediation decisions about millions or billions of documents by simply looking at the first few and last few exemplar documents in each document type cluster. You know that the decision being made is no better and no worse that the examples that are reviewed. Hundreds or thousands of groups can be quickly and accurately reviewed that effect millions or billions of data similar documents universally;
- Comprehensive document retention, reduction and remediation policies can be created - from a position of awareness - rather than interviews with stakeholders who can't possibly remember everything that they have, much less what their predecessors passed on;
- You can always use traditional approaches to "sub-divide" a cluster for finer tuning. Our methodology takes deduplication and parent / child into account as well, making it an order of magnitude more comprehensive than any current method; and …
There is still more….
- Code automatically generated to create the clusters is preserved and can be subsequently applied to newly introduced information – enabling implementation of a mostly automated self-classification system for the new information;
- For example, use this approach to ease the pain of preservation and continued vigilance over information required to be found and tracked in a litigation hold; and
Finally, you have an approach where:
- Your data content itself informs your knowledge of your data environment – no guesses, real information and knowledge derived from your own content;
- You can approach resolution of many of the problems of big data – data volume, data variety and data velocity or extraordinary creation rate – with largely automated approaches that assure good decisions of what to retain, that assure continued enhancement of the taxonomy to support the storage and that assure accuracy and confidence in the timely retrieval of the right information for stakeholders;
- You can move to implementing a cost effective, sustainable, objective and automated program of data classification based upon iterative clustering and adjustments to storage taxonomies or requirements of a specialized repository, such as for litigation, based upon the content of the information itself;
And for eDiscovery repositories, there is no need to recode for newly introduced information – it either clusters with the “old” or creates new clusters and outliers.
Give us a call to discuss your needs. No doubt our solutions based approach can resolve most of them.
To View or Download a pdf of this blog, click here.