Steps of Digitization Process

Digitization is itself the process of converting text, pictures, or sound into a digital form. What you get is digital data once the process is complete. Such type of data can be used in further useful things like machine learning, data analysis, business intelligence, or knowledge discovery. This digitization actually makes any set of records immortal. That’s why its market size is projected to inflate to 26.9 CAGR by 2031, as per a report.

These records can be edited, used, re-used, refined, analyzed, shared, edited, and transformed into useful information. Being over the internet, you can call or recall it over and over without facing any time or location constraints. It actually creates a paperless world.

There are a number of steps involved in the data digitization cycle.

Data Digitization-How to Do?

Let’s get through “how do you do digitization”.

Step 1. Data Preparation

Before you go ahead, it’s essential to plan and prepare adequately. This initial step involves defining objectives, setting a budget, preparing scanned copies for digitization, removing discrete data or unwanted papers, and establishing a timeline. Also, it requires legal and ethical considerations, and meta data planning. Key considerations during this phase require image enhancement, removing clips, other pins, etc., for making data completely paperless.

Step 2: Selection and Prioritization

Remember, not all data requires digitization, and it’s essential to prioritize what should be digitized first. This step involves the following:

• Data Evaluation: Assess the value, significance, and potential use of the data. Historical documents, scientific records, and rare books might take precedence over less critical materials.
• Risk Assessment: Identify risks associated with data deterioration or loss, such as physical damage or environmental factors.
• Access and User Needs: Consider the needs of users and stakeholders. Prioritize data that will have the most significant impact on their goals.
• Resource Allocation: Allocate resources to the selected data based on priority and importance.
• Hosting Resourcing: It involves selecting the team, tools, and other critical resources that are required for this scraping project, such as cloud servers, scanners, and other equipment.

Step 3. Pilot Program and Testing

This is associated with creating tailor-made scripts, which can best fit the data in the scanned files. It ensures the workflow runs smoothly.

Step 4. Physical Preparation

Once you’ve identified the data to digitize, strategize it for the digitization process for physically preparation:

• Cleaning and Repair: Ensure that physical materials are clean and in good condition. Repair torn pages, fix loose bindings, or stabilize fragile items.
• Inventory: Create a detailed inventory of the items to be digitized, including their current condition.
• Storage: Store materials in an appropriate environment with controlled temperature and humidity to prevent further degradation.

Step 5. Scanning and Capturing Data

Scanning is a fundamental step in data digitization, and it involves converting physical documents into digital images or text. The process includes:

• Equipment Selection: Choose the appropriate scanning equipment, such as flatbed scanners, document scanners, or specialized equipment for fragile or oversized items.
• Resolution and Quality: Determine the required resolution for scanning to ensure high-quality digital images. This choice depends on the intended use of the digital data.
• File Format: Select suitable file formats for storing digitized data. Common formats include PDF, TIFF, and JPEG, depending on the content and purpose.
• Metadata Capture: Capture metadata during the scanning process to document key information about each item, such as title, date, author, and any relevant contextual details.
• Quality Control: Implement quality control measures to ensure accurate and consistent digitization results. This includes checking for missing or distorted data and adjusting settings as needed.

Methods involved in the extraction

There are several methods involved in this digitization processing. People often hire the provider of data extraction services from India because it’s an inexpensive alternative. It hardly costs INR 3,500 per assignment, which is really affordable.  

• Manual Extraction: This scraping solution is the best fit for those who have low volumes of data. On the flip side, the large volume of scanned copies can prove labor-intensive work in the step involved in digitization, which is inexpensive in Asian countries, especially in India.
• OCR Conversion: It is really helpful in scanning and extracting low to high-volume of records from scanned copies or editable databases.
• Intelligence Character Recognition: Also called ICR, this method is highly effective for processing high-volume of invoices or handwritten documents. These can also have printed characters from image files.
• Voice Recognition: This method of extraction automatically converts speech or voice into text. Smart devices like Siri or Echo are here in our lives, making this process easier and more spontaneous by devices.
• Optical Mark Reading (OMR): This is an ideal survey data extraction or capturing method, which helps in extracting tick-marked information on forms, questionnaires, or survey campaigns.
• Intelligent Document Recognition: This is all about interpreting and indexing different documents, such as invoices, letters, contact lists, metadata, and other elements of a database or document.

Step 6. Data Entry and OCR

Conversion is the typical practice of converting scanned images (PDFs) into textual form. IT requires OCR conversion, which involves scripting. It’s a way of digitalizing data and information through these processes.

• Scripting: This is the process carried out at a grass root level, which involves scripting. The programmers can be customized it in accordance with the requirements thereafter.
• Scanning & Recognition: Once the code is evolved, the running program scans and recognizes the files. These scanned versions are then converted into digitized datasets.  This program actually directs the system to check characters in the inked form. The machine understands the fed program and then, extracts data in the colored or tinted text, which is then scanned and extracted via recognition.  This processing may involve but can be carried out anywhere, irrespective of any company, individual, or brand.
• Transfer: Upon scanning the tinted text that the machine understands from the document, the transfer process is carried out. Scanned and recognized content is sent to a particular server location, where it remains safe and intact. From there, the cleaning process begins.

Step 7. Data Entry & OCR

In cases where the digitization process involves text documents, Optical Character Recognition (OCR) comes into play:

• Data Entry: If the data is not in a machine-readable format, deploy data entry experts to manually transcribe it into a digital text file. This step requires human intervention and meticulous attention to detail.
• OCR Processing: Utilize OCR software to convert scanned images of text into machine-readable text. OCR conversion ensures analyzing the scanned images and recognizing characters, enabling text searching and editing.

Step 8. Data Cleansing

This is an outstanding practice of removing typos, duplicates, oddities, outliers, inconsistencies, missing values, discrepancies, or irrelevant records from a similar data entry. This step of data digitization is the crucial one.

• Proofreading and Editing: After OCR conversion, review the text for errors and inconsistencies to utilize its benefits. Manually correct any inaccuracies or formatting issues.
• Data Normalization: When you have a number of abbreviations and want to complete entries, it is called normalization.
• Typos: Typos are actually typing errors, which can be removed via manual cleansing, or any software.
• Data Appending: Here in this method, you can get off redundancies due to incomplete records like incomplete addresses (without zip codes). Basically, appending ensures completing the missing links in the datasets. 
• Data Standardization: This method is all about optimizing records to improve their understanding and comprehensibility.

This is how a number of procedures together make extraction possible, which enriches the business directory with a ton of data-driven solutions. These solutions are actually feasible because of being backed by facts associated with the niche or domain.

Step 9. Metadata Creation and Management

Metadata is essential for organizing and retrieving digitized data effectively. This step involves:

• Metadata Standards: Adhere to established metadata standards (e.g., Dublin Core, MODS, METS) to ensure consistency and interoperability.
• Cataloging: Create metadata records for each digitized item, including descriptive, administrative, and structural metadata.
• Database or Repository: Establish a database or digital repository to store and manage both the digitized data and associated metadata.
• Access Control: Implement access controls and permissions to protect sensitive or restricted data.

Step 10. Quality Assurance

Quality assurance is an ongoing process throughout the digitization project, which works on tips and tricks for error-free data:

• Data Verification: This digitization services involves the thorough examination of the pooled data at an affordable cost (INR3 per form). In other countries, it can push you to pay out more. It may have any obsolete or private data, which the data experts can filter out or undo. Only useful and valid entries are put in the database. This is valid for phone verification or social account examination.
• Validation: Validate the accuracy and completeness of the digitized data by comparing it to the original materials.
• Data Integrity: Implement data integrity checks to detect and correct any corruption or loss of data.
• User Testing: Involve users and stakeholders in testing the digitized data to ensure it meets their needs and expectations.
• Feedback Loop: Establish a feedback mechanism for continuous improvement and addressing issues that arise during the digitization process.

Step 11. Storage and Preservation

Preserving digitized data is as critical as the digitization process itself:

• Storage Solutions: Choose appropriate storage solutions, whether on-premises or cloud-based, to ensure data safety, availability, and long-term preservation.
• Backup and Redundancy: Implement backup and redundancy strategies to protect against data loss due to hardware failures or disasters.
• Digital Preservation: Consider digital preservation best practices, including regular data migration, format migration, and metadata maintenance, to ensure data remains accessible over time.

Step 12. Access and Retrieval

The primary goal of digitization is to make data more accessible:

• User Interfaces: Develop user-friendly interfaces or platforms for accessing and searching digitized data.
• Search and Discovery: Implement robust search and discovery functionalities to help users find the information they need quickly.
• Access Policies: Define access policies and permissions to control who can access the data and under what conditions.

Step 13. Continuous Improvement

Digitization is an ongoing process that requires continuous improvement and maintenance:

• Monitoring: Continuously monitor the digital collection for issues, including data corruption, broken links, and outdated formats.
• Updates: Keep software and hardware up to date to ensure compatibility and security.
• Feedback and Evaluation: Collect feedback from users and stakeholders to identify areas for improvement and enhancement.

All of these processes together let the company focus on the steps of the digitization process to have digitized data to fuel digitalization and automation.