One of the basic tenants of a comprehensive data management strategy is an effective data archive.
A data archive is a store of data that is built for long time retention and as-needed search. For some industries (such as financial organizations), archiving is a legal requirement. For others, archiving is a strategy that serves as a protection against legal action.
Here Are 4 Requirements That You Don’t Want to Skip When Defining Your Collaboration Network’s Archive:
1. Capturing of Modified Messages
One feature that employees enjoy in digital collaboration tools is the ability to audit their own communications. Features such as edit or delete come in handy when an employee hits send too early with a typo or the wrong file attached.
But when it comes to risk management—revised content could be the difference between a breach or a legal violation. The ability to search and see content modifications is key to having a comprehensive, robust archive that protects a company’s best interest.
2. Extraction of Message and Relevant Context
A defining feature of digital collaboration is threaded conversations. This can be either one-to-one or in a group of individuals.
When searching an archive of messages—context is everything.
When searching for a specific employee or keyword, the ability to extract the surrounding conversational context is critical for accurate discovery, and evaluation, of relevant content.
3. Ability to Leverage That Context to Retain Specific Data
Because there is a serious cost to hoarding data without a reason—it is best practice to pair any archive with a retention policy. However, sometimes a legal hold scenario or the like requires the data preservation of a specific employee(s) or a project, for an indefinite period of time.
This is where the nuances of collaboration define their own needs. Given the importance of supplying the context surrounding a piece of content: it is key to preserve the context surrounding a communication in addition to the specific message in question.
4. Predictive Coding for Fast Access
A big component of what makes an archive effective is the ability to access and extract data from the stores. Unfortunately, that process is not always efficient. In fact, the most expensive part of eDiscovery is the review process of whether the surfaced documentation are either relevant or irrelevant.
One way to make the search and discovery process less manual is through predictive coding. These machine learning models leverage keyword search and filtering to reduce the amount of irrelevant materials that would require review—saving time and increasing efficiencies.
Archiving and Records Retention for Workplace by Facebook
Aware by Wiretap was built by collaboration leaders for collaboration leaders. The platform seamlessly integrates with leading tools to give organizations the ability to safely, securely collaborate.
Learn more about how Aware’s risk management suite can help your organization proactively build, manage and search an effective archive for your digital workplace.
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