Course 1 Lessons
Value-focused Definition of Nebulous Data:
Data living on the web, in other environments, or free-floating waiting to be observed and captured.
Nebulous Dark Data
Nebulous data pertains to topics like the fifth dimension, the next and best inhabitable planet, the anatomy of the nearest alien life-form, or something as simple as what exactly is on the minds of a company’s next customer at this very moment. Although it is better to know than to learn, we will always be learning about nebulous data and rarely getting it exactly right. In engineering environments where dark data is prevalent nebulous data rarely gets tapped into, but there are scenarios in which it can provide value.
It’s difficult to find and difficult to understand, but it is possible to develop or stumble on nebulous data. For example, if a company wanted to improve its marketing programs and wanted to know what regions of the USA responded best to its highest performing campaigns, it would most likely look at sales, cost per account (CPA), or gross response rate (GRR). However, none of these tell us which market responds best. That data is more nebulous; there are no data points for it.
To find data points for it we’d need to understand organizational targets and goals. Generally, we’d say sales are the most important facet to understand and review, followed by number of repeat customers, number of customer referrals, buying patterns, and volume of sales. All these aspects should also be assessed by cost and profit per occurrence, such as CPA, and other metrics like cost per referral. By assessing these data points and figuring out how to apply and integrate them, the company could amass referential information that could help it build a scaling mechanism for referrals and a mechanism to evaluate value per account, value per customer or customer type, and more.
