Data Classification - Revisited

In an earlier post (see Simple data classification for Business Continuity) I described a simple means of beginning to classify the value of data for Business Continuity. I’d like to expand on that topic and perhaps approach the subject from a slightly different perspective.

The value of business data

When speaking of the value of business data, the one universal constant is “it depends”. All data is “important” to the business owner – otherwise it wouldn’t have been created in the first place, right?

But looking at the question from the terms of the “business”, the true value to the organization of any particular piece of data lies in how that data is accessed, not in any innate value placed on it by the data’s creator. In fact, the importance of data varies significantly among industries, even by application and perhaps time of day within any particular firm.

Just as the true value of data will vary in nearly every case, the process of assigning a value to the data will be different from enterprise to enterprise. Take for example, the case of a large web based retailer. In this environment, the cost of an hours’ outage might be estimated as:

Estimated Cost of Outage $'s per hour		First	Second	Third	Fourth	Fifth
(hard dollars)	Loss of Sales	X	X	X	X	X
(soft dollars)	Customer Satisfaction	Z	Z+10%	Z+15%	Z+20%	Z+25%

In this example, the retailer has determined that the cost of lost sales remains constant while the soft dollar loss relating to customer satisfaction (and future customer visits) gradually increases with the duration of the outage.
Although this is a simplistic case, it does illustrate a starting point that can be used and built upon in support of different industries and or clients. Take, for example, an enterprise in the banking or services industry. A chart such as follows might be used to quantify the cost of an outage:

Estimated Cost of Outage $'s per hour		First	Second	Third	Fourth	Fifth
(hard dollars)	Loss of Fees	X	X	X	X	X
	Loss of Float	Y	Y	Y	Y	Y
(soft dollars)	Customer Satisfaction	Z	Z+10%	Z+15%	Z+20%	Z+25%

In either case, once you have the anticipated costs assigned to components of both the “hard” and “soft” dollar categories, the value of the data to the business is represented by the sum of the individual columns
Performing this type of exercise is an important step in gaining management concurrence and understanding of the true business value of the various data components. It is also the basis of generating sustainable Service Level Agreements (SLA) as well as Recovery Time and Recovery Point Objectives (RTO and RPO).