How to perform Unstructured Data to Structured Data Conversion? | 9 Easy Steps

The majority of firms in recent times depend heavily on data-driven decision-making. Businesses gather a lot of data to analyze their consumers and goods in-depth, enabling them to plan their growth, product, and marketing plans going forward. However, organizations produce enormous amounts of unstructured data in this Big Data era which makes it necessary to perform Unstructured Data to Structured Data Conversion.

Thanks to extensive data analysis and business intelligence developments, companies may use insightful customer data to make data-driven decisions. However, it is now much easier for businesses to seamlessly glean insights from unstructured data. You will gain a thorough understanding of Unstructured Data & Structured Data. Read more about how to convert Unstructured Data to Structured Data.

What is Unstructured Data?

Data that doesn’t have a predetermined schema or data model is referred to as Unstructured Data. It contrasts with structured data, often arranged in rows and columns and used in conventional relational database systems (RDBMS). More recent technologies, such as NoSQL databases, Data Lakes, and Data Warehouses, can be used to manage Unstructured Data.

Unstructured data is all the media files, documents, and emails saved in external databases. Most of the data generated daily is unstructured; failing to collect results in a massive loss of potential. Unstructured Data can offer crucial additional context, boosting the accuracy of analytics and business decisions. At the same time, it is pivotal to perform Unstructured Data to Structured Data Conversion and leverage the enormous information available in Unstructured Data. To learn more about Unstructured Data, click here.

Advantages of Unstructured Data

Every day, 95 percent of the data produced is categorized as Unstructured Data. Emails, social media posts, photos, etc., offer helpful information for Big Data Research. One can extract this data from a NoSQL Unstructured Database to enhance the context and use the analytics function.

• Customer Experience: Businesses can enhance the customer experience by utilizing the insights gained from Unstructured Data. Monitoring Live Chats, Emails, Customer Support requests, and Social Media posts in real-time may be required when analyzing Unstructured data.
• Identify Market Gaps: Analyzing Unstructured Data might assist a business in locating fresh and unexplored market prospects. This is possible by keeping an eye on the Social Media comments and postings of their rivals and contrasting them with their metrics.
• Customer-Related Feedback: Businesses can read through many emails and open-ended client surveys using Artificial Intelligence (AI) technologies. They can monitor uninvited comments left on blogs, surveys, and other internet platforms.

What is Structured Data?

Structured data is data that mostly fits into rows and columns of a Database. It is sometimes referred to as the “conventional form of data,” and is closely related to relational databases. Relational databases are frequently used by businesses to store data and streamline data flow for software development and data analytics. Companies utilize Structured Query Language(SQL) to interact with it for reading, writing, and updating.

Advantages of Structured Data

Because of its inherent nature, structured data makes it understandable to users of any expertise level. The clearly defined schema makes storing and retrieving data simple, enabling reliable analytics operations. Below mentioned are some of the advantages of Structured Data:

• Stable Environment: Organizations have been utilizing Structured Data for a considerable time. As a result, you already have a wealth of established tools and models to process this data and produce insightful results.
• Progressive Insights: Many different types of professions can use Structured Data to help them make better decisions because there are excellent Data Analytics Tools available. These help businesses create a data culture where teams can gain insights without constantly depending on data scientists or analytics.

What is the difference between Unstructured Data & Structured Data?

Unstructured Data is a collection of numerous types of data retained in their original formats, in contrast to Structured Data, which is very distinct and maintained in a precise manner. Structured Data frequently contains quantitative data, also known as countable data. Unstructured data, in contrast, is referred to as qualitative data.

Structured data may be easily analyzed using techniques like classification, regression, and data clustering because of their distinct natures.

However, unstructured data includes subjective information that can’t be managed conventionally. Several machine learning and deep learning techniques are used to create insights and automate business operations depending on the organization’s needs. To gain an extra advantage, it is essential to perform Unstructured Data to Structured Data Conversion.

What is the need for Unstructured Data to Structured Data Conversion?

Unstructured Data, if properly utilized, can generate a pool of significant insights that can aid businesses in making Data-driven Decisions. Most modern firms view Unstructured Data as an Untapped Resource. This means that firms must find effective ways to gather and use Unstructured Data to make crucial business choices and thrive even in the face of intense competition.

While it may seem like a laborious task to filter large amounts of data, there are various advantages. You can classify links between disparate data sources and identify certain patterns by studying massive unstructured data sets. Additionally, performing Unstructured Data to Structured Data Conversion & analysis thereafter makes it possible to identify market and industry trends.

To comprehend Unstructured Data, Unstructured Data to Structured Data Conversion is necessary. Businesses employ cutting-edge technologies like Natural Language Processing (NLP) and Artificial Intelligence (AI) to perform Unstructured Data to Structured Data Conversion. This spares businesses from performing tedious chores like manually sorting through the data. Companies now find it much simpler to swiftly and reliably examine Unstructured Data because of new rising Machine Learning technologies. 

Cloud data warehouses and ELT enable you to store unstructured data and carry out fast and large-scale data transformations. By doing this way, it takes off your manual work in carefully selecting and applying transformations on your data before loading it to the destination to save on storage costs. Thus, ELT is the preferred method for preparing data for analysis.

Despite all its applications, structured data is still the defacto industry standard today. Here are some other reasons for unstructured to structured data conversion: 

• Accuracy and reliability: Structured data has clear rules and definitions, which makes it more consistent and trustworthy. You can use it to make confident and informed decisions based on reliable data.
• Analysis and reporting: Structured data is simpler to access and analyze than unstructured data, which can be messy and complex. You can use various tools and techniques to extract insights and create reports from structured data.
• Integration and exchange: Structured data can be easily integrated and shared with other applications and systems. You can also perform cross-analysis to discover patterns and trends from different data sources.
• Efficiency and productivity: Structured data improves searchability, which helps you find the data you need faster and easier. This saves you time and effort and streamlines your workflows.

Steps to perform Unstructured Data to Structured Data Conversion?

Here are the steps to transforming unstructured data to structured data:

• Step 1: Cleaning the Unstructured Data
• Step 2: Check to see if it should be kept or deleted
• Step 3: Choose the technology for data collection and storage based on company requirements
• Step 4: Entity Extraction
• Step 5: Create a pattern
• Step 6: Analyze the Data
• Step 7: Understand what will be done with the analysis’ results
• Step 8: Store the Data
• Step 9: Implement project measurement

Given the importance of Unstructured Data in enterprise data architecture today, it is crucial for organizations to understand what is and is not feasible when accessing both Structured and Unstructured data. In this section, you’ll learn how to convert Unstructured Data to Structured Data in Excel.

It could be hazardous if unstructured data fills up too much storage space for your company. It is a good idea to eliminate irrelevant information to prevent further misunderstanding and focus solely on the organized material that can be useful. Additionally, the data backup and recovery service, which should be helpful in times of emergency, needs to be updated and maintained.

Here is a list of procedures put together to aid in processing the Unstructured Data to Structured Data Conversion:

Step 1: Cleaning the Unstructured Data

Adhere to the stringent rule of cleaning the data daily into a usable relational database format. To avoid damaging the entire data collection, make sure you acquire data from dependable sources and stay away from any random sources. Making connections between data sources and extracting entities is another data cleansing method that will construct an organized database for analysis.

Step 2: Check to see if it should be kept or deleted

You’ll eventually realize that it’s not vital to always hold onto information that might become useless. Since collecting data for a goal is expensive, it should only be done when it is critical.

Step 3: Choose the technology for data collection and storage based on company requirements

Although the unstructured data will come from various sources, the analysis’s results must be put into a technology stack in order for them to be immediately usable. The volume, scalability, velocity, and variety of requirements are the sole determinants of the features that are crucial for choosing data retrieval and storage. After carefully comparing a potential technological stack to the project’s final needs, the data architecture should be put up.

Step 4: Entity Extraction

You can handle Unstructured Data by identifying individuals, companies, places, etc., from it. You can match the relational table syntax by using this approach to extract the appropriate data from the messy, raw data. Parts-of-Speech tagging can be used with semantic analysis and natural language processing to retrieve entities that are frequently used, such as “person,” “place,” and “business,” as well as their internal relationships. In order to comprehend the data patterns and the text flow better, you can achieve this by creating a term frequency matrix.

Step 5: Create a pattern

You should create a reference manual for yourself that includes one or more of the following:

• Classification: This procedure enables you to demonstrate the connection between the information source and retrieved data. To spot trends and maintain consistency with the process, it’s crucial to keep a record. You can classify the passage of text by categorizing the data per the context in which it is being used. Knowing the bigger context and the relevant domain can make processing unstructured data easier because multiple words can be used to refer to the same thing.

• Sentence chunking: If, while scanning, you come across words that belong to the noun category, the data can be organized according to the kind of relationships those words have with other words.

Step 6: Analyze the Data

Now that all the raw data has been organized, it’s time to analyze it and make sound business judgments. Knowing your objective for analyzing your unstructured data is crucial. Organizations will be able to provide better products, services, and customer experiences while aligning with their business goals by mining unstructured data for actionable insights. To learn more about Data Mining of Unstructured Data, click here.

Step 7: Understand what will be done with the analysis’ results

The analysis could be useless if the final result is unclear. Understanding what kind of result is needed—whether it’s a trend, effect, cause, amount, or anything else—is crucial. For the final outcomes to be used more effectively for commercial, market, or other organization-related gains, a clear road map should be developed.

Step 8: Store the Data

Information should be carefully preserved in its original format until it is really considered valuable and necessary for a specific reason, keeping storage of meta-data or other information that might aid in the analysis, if not now then in the future.

Step 9: Implement project measurement

Whatever be the case, the outcome is what matters most. The outcomes must be delivered in the proper manner, extracting and delivering insights into structured data from unstructured data. An online data extraction tool and a data intelligence tool should be used to handle this so that the user may carry out the necessary actions in real time. The next phase would be to assess the impact using the necessary ROI based on revenue, process effectiveness, and company growth.

How do you analyze unstructured data?

Ironically, Unstructured Data must first be applied with some sort of basic structure to be successfully examined. In some unstructured databases, data extraction is required for analysis.

Essential information like client sentiment that is difficult to extract by statistical approaches is contained in unstructured data. Advanced textual analysis can be used to gather data from various unstructured sources, such as Twitter feeds, Social Media feeds, and emails, to reveal customer sentiment at the individual level. Traditional insights gain extra context, balance, and value from these insights, boosting their strategic importance.

The below-listed are a few ways used to study and analyze Unstructured Data:

• Meta Data: Data that offers information about other data is known as metadata. It is essential for organizing, preserving, and processing unstructured data. For instance, capturing a picture with a camera or smartphone comes with additional details like the date, time, filename, geolocation, and more. Each company can construct its metadata fields based on requirements to describe the nature of the unstructured data because there are no industry standards for metadata. As a result, metadata aids companies in streamlining data analysis and search.
• NLP: The machine learning technique known as “natural language processing” (NLP) enables users to examine unstructured data. Using grammatical and semantic linkages, NLP can determine the meaning of text data. The processing of natural languages by the human brain, including English, Chinese, Spanish, and others, is imitated by NLP.
• Analysis of images: Visuals are also a very crucial part included in unstructured data. Image analysis is breaking down photographs into pieces and retrieving essential data, for instance, identifying medical issues by examining MRI or x-ray images. Some of the tasks involved are finding shapes, eliminating noise, spotting edges, counting items, identifying picture elements, etc.
• Data Visualization: The graphical representation of data that facilitates a more straightforward understanding is known as data visualization. Data visualization strategies enable users to understand data easily. Data visualization makes every intricate structure in the data visible, making it easier for users to comprehend the information.

Use cases of converting unstructured data to structured data

1. Medical Histories: Structured electronic health records can be created from unstructured notes and observations, allowing for easier access and analysis of patient data.

2. Diagnostic Reports: Unstructured radiology or pathology reports can be transformed into structured formats, facilitating better integration with health information systems and improving patient care.

3. Contract Analysis: Key information can be extracted from scanned or handwritten contracts and stored in structured databases, which assist in contract management and compliance checks.

4. Case Precedents: Legal precedents from various sources can be gathered and organized, then stored in a structured repository, which streamlines legal research and case preparation.

5. Bank Statements: Paper-based bank statements can be digitized and stored in structured formats, simplifying financial analysis and auditing processes.

6. Annual Reports: Lengthy annual reports can be summarized and stored in structured data sets, which enable easy extraction of key financial metrics and insights.

7. Resume Parsing: Diverse resume formats can be standardized and stored in a structured format, which enhances the efficiency of talent acquisition processes and helps find the best candidates.

8. Employee Feedback: Open-ended employee feedback can be analyzed and stored in structured data, aiding sentiment analysis and organizational improvements.

9. Product Reviews: Unstructured customer reviews can be categorized and stored in structured data, which enables better sentiment analysis and product improvements.

10. Inventory Logs: Handwritten inventory logs can be organized and stored in structured databases, which optimize inventory management and forecasting.

11. Property Descriptions: Diverse property listings can be normalized and stored in a standardized structured format, facilitating easier property comparisons and searches.

12. Client Preferences: Client notes and preferences can be captured and stored in structured data, which enhances property matching and client satisfaction.

Conclusion

The objective of every organization today, regardless of the particulars of the industry, is to make sense of both structured and unstructured data for better and more effective decision-making.

Given the high importance of both of these categories of data, connecting variously structured and unstructured data stores and methodically gathering insight across them are necessary for good big data analytics in business. Businesses should employ various technologies that combine the advantages of structured and unstructured data to make the most sense of their data & perform Unstructured Data to Structured Data Conversion. It’s also crucial to understand how to transform unstructured data to structured data.

Integrating and analyzing data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo Data, a No-code Data Pipeline helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write the code repeatedly. Hevo with its strong integration with 150+ Data Sources, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy. With Hevo, you can easily get an idea on how to convert unstructured data to structured data in Python.

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