Data Entropy And The Role Of Large Program Implementations In Addressing Data Disorder

Clutter is a highly pervasive phenomenon. Homeowners are very familiar with this occurrence as their acquisitions grow to fill available space. Closets, garages, basements, and many areas not in obvious sight become dumping grounds for things that do not have immediate utility or a logical place in the house. Now think of a scenario where the volume, velocity, and variety of goods entering the house goes up by several orders of magnitude in a very short period of time. The house will simply start to over flow with articles strewn wherever they can fit, with little thought given to order, use, and structure. Enterprises face a similar situation with data as volumes have grown dramatically over the last two to three years. Organizational reluctance to retire or purge data creates over owing repositories, dark corners, and storage spaces full of outdated, unseen, and difficult to access information – i.e., data clutter. Temporary fixes only add layers to the problem, creating additional waste, maintenance challenges, damage, inefficiency, and improvement impediments. All these factors drive data entropy, which for purposes of this paper is defined as the tendency for data in an enterprise to become increasingly disorderly. Large programs are often data centric and surface data clutter issues. This paper explores the concept of data entropy in today’s world of rapidly expanding data types and volumes entering an organization at exponentially higher speeds, and how large program implementations can be used as catalysts to address data clutter and modernize the data supply chain to streamline data management.