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Handling unstructured text manually is time-consuming (and, as a result, costly) because it is prone to errors and biases due to human error. In addition, it is not scalability friendly. Having tools that can help you cut through data clutter and uncover what is valuable will become more critical as your company and data develop. Where do you begin, and what should you do first? Unstructured data management consists of four processes that must be completed:
Now, what is unstructured data management, and what are its benefits? Continue reading to find out more.
What is Unstructured Data Management?
Unstructured data is a dataset that has not been organized in a prescribed way. Textual unstructured data, such as open-ended survey replies and social media chats, is the most common kind of unstructured data. Still, non-textual data, such as photographs, video, and audio, may also be found. Because of the rising usage of digital apps and services, unstructured information is expanding alarmingly.
Unstructured data management is the act of collecting, saving, arranging, and analyzing data that doesn’t have any specified structure.
How to Manage Unstructured Data?
It is well recognized that unstructured data (also known as qualitative data) is a vital corporate asset. Since client views are unstructured, they are challenging to manage and comprehend. Here is how to manage unstructured data.
1. Organize and Ensure All Contents are Searchable
For managing unstructured data in the organization, you’ll need a large amount of storage. As a result of its ease of use and ability to facilitate distant collaboration, public cloud-based storage is the logical choice. It is also expandable and cost-effective since you can upgrade to a higher tier if you want more space. Investing in on-premise storage gear, such as servers or external hard drives, is an alternative to storing data on the cloud. Because of security concerns or data protection rules, some firms choose to keep their vital data on-site. As much as you may value the total control that on-premise storage affords you, the expenses of IT support, maintenance, and security infrastructure outweigh the advantages.
You may also choose hybrid data storage, a combination of on-premises and cloud-based data storage options. Consider how simple it will be to search for and filter your data while storing it. This will allow you to swiftly browse datasets using keywords and identify what you are looking for. To summarise material and make it searchable, it is necessary to include metadata in documents and files.
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2. Unstructured Data should be Cleaned
When managing unstructured data sets, remember that they frequently contain spelling errors, HTML tags, punctuation marks, hashtags, special characters, and so forth. Preprocessing data, also known as ‘data cleaning,’ is required to improve the quality of your datasets. Before beginning any text analysis, it is necessary to finish this stage. Preprocessing data entails a variety of strategies, such as deleting extraneous information and slicing data into more understandable chunks of material, among others.
3. Make Use of Artificial Intelligence Tools to Analyze Unstructured Data
It is necessary to examine unstructured data once it has been collected, categorized, and cleansed. To interpret and handle large amounts of text data, text analysis techniques integrate machine learning with Natural Language Processing (NLP). These tools function by automatically categorizing text by subject, sentiment, intent, and other factors, as well as extracting certain information such as keywords or named entities. In the case of managing Twitter mentions, for example, you might use a sentiment analyzer to monitor sentiments in real-time and rapidly identify problems that need your attention.
4. Figure Out How to Present Your Data Visually
Among the unstructured data management solutions is the visual presentation of data. To manage unstructured data, compelling data visualizations are required. You can turn tedious spreadsheets into clear and helpful information that you can share with your colleagues using charts, reports, and interactive dashboards. CHI Software Development Team can help you to analyze your data using machine learning and build bespoke data visualizations so you can get deeper into the information.
Conclusion
This article covers how to manage unstructured data. Every organization has its own set of requirements and peculiarities, necessitating the development of customized solutions to deal with unstructured data. Based on our previous experience at CHI Software Development Team, we can inform you that we can help you manage your unstructured data; try us today for a change.
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