Having an unstructured data set means that the data does not have a defined structure. It’s one of three fundamental data types, as well as semi-structured and structured formats.
Unstructured data examples include chat transcripts, call log contracts, sensors, and call logs. Data, since these datasets do not have a structure conforming to a predefined data model.
Data that is unstructured must be standardized and organized into rows and columns so that machines can read it, i.e., ready to be analyzed and interpreted. This makes managing data that is structured easy. In this article, we will discuss some of the issues involved with unstructured data management. Stay tuned!
Challenges in Unstructured Data Management
#1 Long Waiting Period For Detecting New And Changed Data
Scanning a scaled-out NAS for updates can cause a lengthy and lengthy waiting. Daily changes on large amounts of hundreds of millions or billions of tiny files and parsing entire storage file systems require speedy and effective technology.
The komprise data discovery quickly lists the new deleted, altered and deleted files in storage without the lengthy and tedious processing of a full storage filesystem. There are more companies that need to not perform the full tree scan of their files each when an archive, backup, or synchronization process is completed.
#2 Complex Data Management
Traditional approaches typically depend on several data protection systems to overcome any limitations of each solution. Businesses typically have several options for data protection to meet different requirements, which can lead to the expense of different software/hardware and storage resources. Komprise provides a scalable and storage-agnostic solution for advanced database management systems that can scale horizontally into hundreds of servers a single computer can control.
#3 Reduced Data Usability
Data usability is another area of concern when using unstructured data. The companies must convert the unstructured data into machine-readable format before processing it, and the data also requires indexing and schema for it to function. Additional data processing demands increase the time to insight which could result in delays in making decisions.
For example, receipts scanned can’t be scanned directly and have to be processed via some OCR tool to collect relevant information. In the same way, posts on social media need to be scraped and transformed into a structured form so that you can analyze sentiment.
Today, tools for data extraction can automate the process of data extraction, processing and loading, basically every step. They can scrape and process data with no structure on a large scale.
#4 Accessing Siloed Data
In the modern workplace, digitalized employees are demanding more information from employers. Privacy laws like CPRA and the GDPR have focused on protecting employee data and enhancing employee access to their data.
Furthermore, employee requests to access their personal data are on the rise. The issue is how to offer seamless access to the sensitive data stored in silos of data across different sources, like chats, email, and audio logs.
The first step to solving this issue is to identify sources of employee data. The next step would be to combine different information stored in multiple systems to create an integrated repository. Then, employers should establish a strong ID validation and masking system to avoid data leaks.
The ethical management of the personal information of employees, making it available upon request, and sharing new privacy laws with employees create a culture that is a sense of security within an organization.
#5 Having long service level agreements and a recovery time objective
Massive volumes of data and the complexity of diverse backup processes make retrieval, data protection and business continuity lengthy and complicated. SLAs are hard to meet in the long run, and RTOs are only sometimes fulfilled. Komprise’s sophisticated capabilities allow the ability to backup and recover storage and shared file systems ranging from 100 TB up to multiple petabytes and automatically control the amount of storage utilized through the automatic consolidation of the number of versions.
