How to Use Data Scraping to Mine Structured Data From the Unstructured Data?

Analysis of Unstructured Data and Data Scraping

How to Use Data Scraping to Mine Structured Data From the Unstructured Data?

The rise in information digitization, combined with various transactions has resulted in data overflow.

The constant increase in the velocity of digital information has resulted in doubling the global data in an extremely short time.

According to Gartner, about 80% of the data in an organization is unstructured, which includes data from the emails, customer calls, and social media feeds.

This doesn’t include the data logged by different user devices. Whereas this might be alarming to make a suitable analysis about organized data, this is even tougher to make the right sense of the unstructured data.

Analysis of Unstructured Data and Data Scraping

Analysis of Unstructured Data and Data Scraping

Analyzing Unstructured or Semi-Structured Data to Improve Business

The organizations need to analyze unstructured and semi-structured data sets for extracting insights from the structured data for taking better business decisions.

These decisions consist of shaping customer sentiments, finding customer requirements and recognizing the offerings, which will associate more with customer requirements.

When filtering the big data amount may seem like a boring work, it certainly has its benefits.

Through analyzing huge unstructured data sets, you can classify connections from the distinct data resources and get particular patterns. This analysis allows the finding of business and market trends.

Conversion of Unstructured Data into Structured Data

There are many steps to study unstructured data and perform data scraping to mine structured data insights:

Data Source Analysis

Before you start, you have to analyze the data sources which are important for data analysis.

You can have unstructured data resources available in various forms like text documents, web pages, audio files, video files, chats, customer emails and more.

Also, you need to analyze the unstructured data resources, which are completely applicable.

1. Identify what to do with the analysis results

If your end results are not clearer, your analysis is of no use. It’s important to understand what kind of results are needed, is it an effect, trend, reason, quantity or anything else needed.

You need to have a clear road-map about what needs to be done with the concluding results to utilize them in a better way for your business, organization or market-related gains.

2. Choose the expertise for data storage according to business requirements

Although unstructured data comes from different resources, the results of the analysis need to be injected with the technology stack in order that the results can be easily used.

Features which are essential for choosing the data storage and retrieval completely rely on the scalability, velocity, volume, and different requirements.

A future technology stack needs to get well evaluated against the final requirements and after that the project’s data architecture is set-up.

Some examples of the business requirements and the collection of the expertise are given here:

  • Real-time Pricing: It’s very important for the eCommerce companies to provide actual-time pricing.

This needs tracking and monitoring of the activities of real-time competitors and providing offerings as per immediate results of the analytics software.

Those price technologies consist of competitor pricing monitoring software.

  • Better Availability: Better availability is very important for unstructured data and details from the social media platforms.

This technology platform needs to ensure that no data loss is there in the real- time.

This is a superior idea of holding the information intake like the data redundancy plans.

  • Multilevel Support: Another significant aspect is the ability to isolate data from different user groups.

Efficient web scraping solutions need to support multi-level positions.

The data isolation is important, according to the sensitivities associated with the customer information and feedbacks shared with the real insights for meeting confidentiality requirements.

3. Storing information in the data warehouse

Information needs to be stored well in the natural format until it becomes really beneficial and needed for the precise objective, maintaining metadata storage or other details, which might help during the analysis now or later.

4. Data formulation for better storage

While maintaining your original data, if you need to allow data usage, the finest option is cleaning the copies.

The better option is to clear the symbols and white spaces while transforming the text.

The duplicated results need to be removed and the required data should be saved in the data sets.

5. Understanding the data patterns in a better way

Using processing of natural language and semantic analysis, you can fetch entities that are very common like location, person, company and other internal associations.

By doing so, you can create a time-frequency matrix for better understanding different data patterns.

6. Data extraction and text data scraping

When the database is created, the data need to get classified and appropriately segmented through data scraping.

Different data intelligence tools are used to search similar customer behavior while targeted for the particular campaigns or classifications.

The customer’s outlook can be determined using the sentiment analysis and web scraping, for reviews and feedbacks that help in understanding product recommendations and market trends better and provide guidance for the new services or product launch.

You can use Social Media Intelligence Solutions for extracting the events or posts which prospects and customers are sharing using forums, social media and other platforms for improving your services and products.

7. Better project measurement and implementation

What matters the most is the end results. It’s important that the clients get results in a requisite format with data extraction and providing well-structured data insights from the unstructured data.

This needs to be handled using web scraping software as well as data intelligence tools in order that the users can take the necessary actions in real time.

The decisive step might be to calculate the effects with the necessary ROI through revenue, business improvements, and process effectiveness.

Conclusion

You can derive the real value only when unstructured, semi-structured, and structured data analysis is united for 360-degrees outlook.

To identify how to grow the business outcomes using web scraping and data scraping solutions, contact us or ask for a free consultation and our representative will contact you soon.

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