Defensible disposal of unstructured content is a key outcome of sound information governance programs. A sound approach to records management as part of the organization’s information governance strategy is rife with challenges.
Some of the challenges are explosive content volumes, difficulty with accurately determining what content is a business record comparing to transient or non-business related content, eroding IT budgets due to mounting storage costs, and the need to incorporate content from legacy systems or merger and acquisition activity.
Managing the retention and disposition of information reduces litigation risk, it reduces discovery and storage costs, and it ensures organizations maintain regulatory compliance. In order for content to be understood and determined why it must be retained, for how long it must be retained, and when it can be dispositioned, it needs to be classified.
However, users see the process of sorting records from transient content as intrusive, complex, and counterproductive. On top of this, the popularity of mobile devices and social media applications has effectively fragmented the content authoring and has eliminated any chance of building consistent classification tools into end-user applications.
If classification is not being carried out, there are serious implications when asked by regulators or auditors to provide reports to defend the organization’s records and retention management program.
Records managers also struggle with enforcing policies that rely on manual, human-based classification. Accuracy and consistency in applying classification is often inadequate when left up to users, the costs in terms of productivity loss are high, and these issues, in turn, result in increased business and legal risk as well as the potential for the entire records management program to quickly become unsustainable in terms of its ability to scale.
A solution to overcome this challenge is automatic classification. It eliminates the need for users to manually identify records and apply necessary classifications. By taking the burden of classification off the end-user, records managers can improve consistency of classification and better enforce rules and policies.
Auto-Classification makes it possible for records managers to easily demonstrate a defensible approach to classification based on statistically relevant sampling and quality control. Consequently, this minimizes the risk of regulatory fines and eDiscovery sanctions.
In short, it provides a non-intrusive solution that eliminates the need for business users to sort and classify a growing volume of low-touch content, such as email and social media, while offering records managers and the organization as a whole the ability to establish a highly defensible, completely transparent records management program as part of their broader information governance strategy.
Benefits of Automatic Classification for Information Governance
Apply records management classifications as part of a consistent, programmatic component of a sound information governance program to:
Reduce
- Litigation risk
- Storage costs
- eDiscovery costs
Improve
- Compliance
- Security
- Responsiveness
- User productivity and satisfaction
Address
- The fundamental difficulties in applying classifications to high volume, low touch content such as legacy content, email and social media content.
- Records manager and compliance officer concerns about defensibility and transparency.
Features
- Automated Classification: automate the classification of content in line with existing records management classifications.
- Advanced Techniques: classification process based on a hybrid approach that combines machine learning, rules, and content analytics.
- Flexible Classification: ability to define classification rules using keywords or metadata.
- Policy-Driven Configuration: ability to configure and optimize the classification process with an easy "step-by-step" tuning guide.
- Advanced Optimization Tools: reports make it easy to examine classification results, identify potential accuracy issues, and then fix those issues by leveraging the provided "optimization" hints.
- Sophisticated Relevancy and Accuracy Assurance: automatic sampling and bench marking with a complete set of metrics to assess the quality of the classification process.
- Quality Assurance : advanced reports on a statistically relevant sample to review and code documents that have been automatically classified to manually assess the quality of the classification results when desired.
