Data Transcription for Your Digital Transformation in 2024

In the post-pandemic¹ world, digital transformation (DX) is something that businesses are taking very seriously. Companies that still have not adopted digital solutions should act now if they wish to remain economically viable in the future (See Figure 1). While operating in a world that is becoming more digital, many businesses still² use paper-based processes and need to extract information and insights from them. 

An important element of digital transformation is updating your data in favor of your digital storage protocols. That is where data transcription comes into play. In order to implement any digital solution, such as a PIM tool, PDM software, ERP system, etc., all company data needs to be transcribed into digital data to make it machine-ready.

To help business leaders streamline their digital transformation journey, we have curated this article which explores: 

• What is data transcription?
• Why is it important? 
• Where is it used?
• And some methods of conducting data transcription. 

Figure 1. Survey results on the importance of digital transformation in the future. 

What is data transcription?

It is the process of converting data from one form to another. In most cases, data transcription involves transferring data from an analog source into a digital format. This can include converting paper documents into electronic documents, such as text or a spreadsheet, or audio recordings into text format. For example, data transcription could be used to convert handwritten paper notes into data that can be searched on a computer.

Why is it important?

Data transcription allows data to be more accessible and easier to use. Converting data into digital formats makes data more easily searchable, shareable and transferable between applications and systems, making data analysis much faster and simpler. Additionally, transcribing your data can reduce the costs associated with storing physical documents or recordings.

As businesses rush toward a digital future (see figure 2), they need to transcribe their data as one of the first steps.

Figure 2. Digital transformation horizon

Where is data transcription used?

This section highlights some applications of data transcription.

• Implementing PDM: Data transcription is required to extract analog product data from engineering and production departments and digitize it to make it import-ready for PDM software.
• Implementing PIM: Similarly, before implementing a PIM system in your business, analog product data is extracted from different departments of an organization, such as marketing, sales, e-commerce, etc. The data is then digitized to make it import-ready for a PIM system.
• Conducting qualitative research: While collecting data for a qualitative research project, data transcription is required to convert the recorded interviews into text. It is the initial step of the whole qualitative data analysis process.

What are the methods of data transcription?

Data transcription is done in 2 main ways; manual data transcription and automated data transcription through intelligent data processing (IDP).

1. Manual data transcription

In manual data transcription, professional transcribers extract data from all parts of the organization and transform it from analog form into digital format manually, without using any automated tools or software. Manual data transcription can be used for all types of data, including: 

• Audio recordings, 
• Handwritten notes, 
• Paper documents, etc. 

It is important to note that manual data transcription requires more time and effort than automated data transcription but may be more accurate, depending on the data source and scale of the company. Additionally, manual data transcription can also be used to clean up data before it is converted into a digital format.

1.1. Recommendations

Like every other manual task, manual data transcription is not suitable for large-scale businesses that have tons of data. A small to medium-scale business with a small amount of data running in the pipeline can easily dedicate a professional transcriptionist or a small team of professional transcribers to perform the task manually. However, the problem occurs when the size of the organization and data increases. This is because it makes the task highly repetitive and error-prone.

Sometimes even medium-sized businesses manage a large amount of data that can not be transcribed manually. For such transcription needs, AIMultiple recommends working with data transcription services. This will allow you to maintain the level of quality while keeping project deadlines in check.

2. Automated data transcription

Automated data transcription is the process of converting data from an analog source into a digital format using automated tools or software. Automated data transcription allows for faster data transfer and conversion, as well as greater accuracy than manual data transcription. The automated tools use intelligent data processing (IDP) to: 

• Classify the data
• Extract it 
• Transcribe it 
• And validate it.

The automated tools leverage machine learning, intelligent data processing (IDP), computer vision (CV), and OCR technology to transcribe data into digital format.

2.1. Recommendations

Automated data transcription is suitable for businesses with large amounts of data spread across multiple departments and business partners. This method is much faster and creates significantly fewer errors. Using an automated data transcription tool is also useful for verbatim transcription, in which word-to-word transcriptions are necessary, and sometimes human transcribers can not keep up.

For verbatim transcription, although automated tools can perform much faster than human transcribers, sometimes they fall short. That is mainly because automated data transcription tools can sometimes have difficulty in reading data such as noisy recordings, a blurry image or video file, difficult-to-understand handwriting, text that includes grammatical errors, or qualitative research data. Transcribing qualitative data, for instance, can be difficult for an automated tool since it requires knowledge of the context. In such cases, it is important to have a human-in-the-loop approach (See Figure 3).

Figure 3: A human-in-the-loop automated data transcription system

Further reading

• E-commerce Data Categorization: Importance & Best Practices
• Top 6 Data Collection Best Practices

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References

1. “How COVID-19 has pushed companies over the technology tipping point—and transformed business forever”. McKinsey Survey Oct 5, 2020. Retrieved: Dec 13, 2022.
2. “Fueling digital operations with analog data”. McKinsey Article. Apr 20, 2022. Retrieved: Dec 13, 2022.