Chaos theory posits that randomness really isn’t so random. Under all that apparent disorder are patterns and logic that rely on small nuances or changes, and the outcome is far less chaotic than we initially believe.
When we look at how businesses are challenged to find information, optimize it, and use it, we can relate to chaos theory to data.
Governance applications are an ideal example. Searching unstructured data, classifying it, purging/deleting, or moving it based on business and compliance policies is usually an arduous manual task. But small nuances – metadata, or a nine-digit sequence representing a social security number within that content – makes an enormous difference in how it should be managed or stored.
The new crop of machine learning and analytics applications, both of which aim to increase the actionable knowledge we derive from information, also rely on understanding and processing small details in the data. For these systems to work, and deliver accurate outcomes, we need insight into the data’s value or advantage.
When it comes to information, data chaos is prevalent. It can stem from a mix of on-premises and cloud infrastructure or incompatible platforms. It can be a failure or breach that renders critical files unusable. It can be a rapid expansion, merger, or acquisition that creates silos, sprawl, or redundancy. It can simply be insufficient attention to capacity growth, such as stale, orphaned, and non-business data.
The goal of data management today is to gain control of business data to address critical concerns like continuity, cost, and compliance, without compromise. Data chaos is an obstacle to this goal.
Our goal is to give users control with great insight and knowledge into their information, break through data silos, and transform and simplify operations. That insight and knowledge comes from the nuances: the state, placement, importance, and availability of data.
When chaos is eradicated, the result is seen in economics, for one. Consolidating infrastructure, eliminating duplicate data, purging, and removing inactive data to lower-cost storage: all of this leads to significant cost reduction. It can also improve application experience and performance even as the environment scales. Additionally, it can reduce risk of information exposure that leads to legal and financial consequences.
Most importantly, the result is the potential for new and dramatically streamlined functions: collaboration, governance, business analytics, machine learning, cybersecurity, e-discovery, and much more.
We believe there is enormous opportunity for organizational change when we make order out of chaos in the data. That’s our theory, anyway. To hear more, schedule a consultation with us.