Data classification (data management)


In the field of data management, data classification as a part of the Information Lifecycle Management process can be defined as a tool for categorization of data to enable/help organizations to effectively answer the following questions:
When implemented it provides a bridge between IT professionals and process or application owners. IT staff are informed about the data value and management understands better which part of the data centre needs to be invested in to keep operations running effectively. This can be of particular importance in risk management, legal discovery, and compliance with government regulations. Data classification is typically a manual process; however, there are many tools from different vendors that can help gather information about the data.
Data classification needs to take into account the following:
Note that this classification structure is written from a Data Management perspective and therefore has a focus for text and text convertible binary data sources. Images, videos, and audio files are highly structured formats built for industry standard API's and do not readily fit within the classification scheme outlined below.
First step is to evaluate and divide the various applications and data into their respective category as follows:
Types of data classification - note that this designation is entirely orthogonal to the application centric designation outlined above. Regardless of structure inherited from application, data may be of the types below
1. Geographical
2. Chronological
3. Qualitative
4. Quantitative
It should also be evaluated across three dimensions:
  1. Identifiability: how easily can this data be used to identify an individual?
  2. Sensitivity: how much damage could be done if this data reached the wrong hands?
  3. Scarcity: how readily available is this data?

    Basic criteria for semi-structured or poly-structured data classification

Note that any of these criteria may also apply to Tabular or Relational data as "Basic Criteria". These criteria are application specific, rather than inherent aspects of the form in which the data is presented..

Basic criteria for relational or Tabular data classification

These criteria are usually initiated by application requirements such as:
Note that any of these criteria may also apply to semi/poly structured data as "Basic Criteria". These criteria are application specific, rather than inherent aspects of the form in which the data is presented.

Benefits of data classification

Benefits of effective implementation of appropriate data classification can significantly improve ILM process and save data centre storage resources. If implemented systemically it can generate improvements in data centre performance and utilization. Data classification can also reduce costs and administration overhead. "Good enough" data classification can produce these results:
There are three different approaches to data classification within a business environment, each of these techniques – paper-based classification, automated classification and user-driven classification – has its own benefits and pitfalls.

Paper-Based Classification Policy

A corporate data classification policy will set out how employees are required to treat the different types of data they handle, aligned with the organisation’s overall data security policy and strategy. A well-written policy will enable users to make fast and intuitive decisions about the value of a piece of information, and what the appropriate handling rules are for example who can access the data and should a rights management template be invoked. The challenge, without any supporting technology, is ensuring that everyone is aware of the policy and implements it correctly.

Automated Classification Policy

This technique bypasses the users’ involvement, enforcing a classification policy to be consistently applied across all touchpoints, without the need for major communication and education programmes.
Classifications are applied by solutions that use software algorithms based on keywords or phrases in the content to analyse and classify it. This approach comes into its own where certain types of data are created with no user involvement – for example reports generated by ERP systems or where the data includes specific personal information which is easily identified such as credit card details.
However, automated solutions do not understand context and are therefore susceptible to inaccuracies, giving false positive results that can frustrate users and impede business processes, as well as false negative errors that expose organisations to sensitive data loss.

User-Driven Classification Policy

The data classification process can be completely automated, but it is most effective when the user is placed in the driving seat.
The user-driven classification technique makes employees themselves responsible for deciding which label is appropriate, and attaching it using a software tool at the point of creating, editing, sending or saving. The advantage of involving the user in the process is that their insight into the context, business value and sensitivity of a piece of data enables them to make informed and accurate decisions about which label to apply. User-driven classification is an additional security layer often used to complement automated classification.
Involving users in classification also leads to other organisational benefits including increased security awareness, an improved culture and the ability to monitor user behaviour which aids reporting and provides the ability to demonstrate compliance. Furthermore, managers can use this behavioural data to identify a possible insider threat, and address any concerns by providing additional guidance to users as appropriate, for example through additional training or by tightening up policy.