What is Fragmentation in Data? How do you solve it?

Data fragmentation occurs when data is broken down into smaller pieces and stored in different locations. Data can be physically fragmented, spread across different physical devices or servers. It can also be logically fragmented, where a single file is broken into multiple parts and stored across a storage system. On top of that, there can be application-level fragmentation, where applications store the same data in their own formats.

The easiest and simplest way to handle this challenge is to implement a centralized platform — a single source of truth that can aggregate, process, and store data from multiple sources. But this solution is hard to implement given the abundance of applications enterprises deal with today. Application sprawl means one repository may not be sufficient to handle the diversity of data coming into the enterprise every day.

Enterprises can also draw up a comprehensive data management strategy to organize data infrastructure, optimize usage, consolidate data as much as possible, and ensure continuous vigilance to combat data growing in silos and furthering fragmentation.

How does fragmented data affect unstructured data?

Fragmentation has its costs and they multiply in several ways when the data is unstructured. Unstructured data such as images, videos, sound clips, and large blueprints can be duplicated across multiple silos, driving up storage costs. Unmanaged fragmentation can slow down data processing and affect business operations — in healthcare, for example, it can lead to misclassification errors in algorithms.

Fragmented unstructured data can also escape data security and governance strategies, increasing the risk of data breaches. It can complicate eDiscovery, increasing litigation costs.

All of this happens before the cost of productivity, where employees spend more time searching for information across apps and storage repositories.