Human Generated Data in 2024: Benefits, Challenges & Methods
The creation of data has rapidly increased after the Covid-19 pandemic (See Figure 1). Whether it is unstructured or structured, business leaders and tech developers need to use this data for different applications. The usage of machine-generated data1 is also increasing as digital solutions such as generative AI become more popular.
However, human generated data remains important to businesses and tech developers since it offers many benefits that machine-generated data can still not offer.
If you are planning to leverage human-generated data in your data-driven business or project, continue reading. In this article, we explore the following:
- What is human-generated data?
- What are its benefits and challenges?
- How to access human-generated data for your business or digital project?
Figure 1. The global volume of data created, captured, copied, and consumed from 2010 to 2020, with forecasts for 2025
What is human-generated data?
Human-generated data is data that is created by people through human action, as opposed to machine learning or other artificial means. This can include anything from text data to social media posts to pictures and videos. Even though machine generated data and technologies like generative AI become more popular. human-generated data remains an important source of information for businesses and tech developers.
Top 4 benefits of human-generated data
As technology improves, human-generated data will become an even more critical asset for businesses. This section highlights some benefits of human-generated data.
1. Caters to exclusive requirements
There are some projects or applications in which only human-generated data can be used. For instance, if a facial recognition or an automatic speech recognition system needs to analyze live human data, it can not be trained with machine-generated data. This can lead to inaccuracies and erroneous results.
2. Fills the gaps of generative AI
Generative AI sounds exciting, but it can not replace humans yet. For instance, not long ago, Google created the project Muze to generate fashion designs, which turned out to be unrealistic and unwearable.
However, tremendous progress is being made in the generative AI field; for instance, newer solutions like DALL-E 22 are claimed to create realistic images for text. Even though such solutions seem promising for improving workflows and reducing manual tasks, they are not autonomous. Deep or machine-learning models for generative AI require human-generated data and input to be developed and used.
3. Fuels behavioral analysis
Behavioral analysis is an effective way of collecting qualitative data that is used for various business applications. Companies can use it to gain valuable insights into their customers, products, services, and operations. This allows them to make informed decisions that drive growth and profitability.
Behavioral analysis can not be conducted without human-generated data. For instance, if a retail store is observing the behavior of the customer as they enter a store, to identify movement patterns, it needs to observe the customers in action. Such data can not be generated with human intervention.
Additionally, human-generated data can be used for predictive analytics tasks such as forecasting sales or predicting customer churn rates.
4. Makes the business more customer-focused
By leveraging human-generated data, companies can gain a better understanding of their customers. This knowledge can then be used to create innovative solutions that improve the customer experience, optimize business processes and develop new strategies for growth. Brands can create targeted marketing campaigns aimed at specific audience segments. All in all, human-generated data is an invaluable asset for any business looking to stay competitive in the ever-changing digital landscape.
Top 4 challenges of Human-generated data
It is not all rainbows and butterflies. Data created by humans can have some issues as well. This section will highlight some of them.
Data generated by humans takes more time as compared to by machines. This is mainly because people make errors, get tired, and take more time to do things than machines. For instance, AI-powered writing tools such as Jasper can produce content up to 5 times faster (claimed by the company) than humans.
Human-generated data can be expensive since collecting, analyzing and interpreting it requires recruitment of contributors, expensive equipment, dedicated locations, and servers to be stored, etc. These costs rise with the size of the dataset.
For instance, to gather human-generated audio files, microphones and soundproof rooms will be required in addition to the participants.
Data generated by humans is highly accurate, but the level of accuracy starts to fall as the dataset becomes larger and the data collection process becomes more repetitive. Manual data collection can become error-prone since modern datasets are required to be large and diverse. Gathering such data involves repetitive tasks, which lead to mistakes and errors. Such errors can lead to inaccuracies in the dataset, reducing the overall quality of the dataset, and can require excessive data processing. Check out this quick read to learn more about how to improve the quality of a dataset.
4. Sample bias
Human-generated data can also include sample bias. For example, the data might be collected from only certain areas or demographics, which may not accurately represent the population as a whole.
Top 3 ways/methods of accessing human-generated data
Crowdsourcing is an effective way to avoid the previously mentioned challenges, specifically the time-consumption and cost-related ones. Through crowdsourcing, a large group of people generate data and share it through an online platform (Which the company needs to develop or purchase). This way, a large amount of data can be generated in a shorter period of time. The crowd uses their own equipment to generate the data, eliminating the extra costs of purchasing equipment or hiring contributors.
This method is suitable for projects that have budget and time constraints and require diverse human-generated content. For projects of secretive nature, such as govt projects, this method would not be suitable. If you do not wish to go through the hassle of the development and management of a crowdsourcing platform, you can work with a crowdsourcing service. Some service providers also offer data protection for projects of secretive nature, so it is important to consider that while selecting a vendor.
2. In-house data collection
Human data can also be generated in-house if the company is willing to spare the personnel, time, and budget. In this method, a team is dedicated to the process, which recruits the contributors, purchases the necessary equipment, and processes the data after collection. This method can allow the company to generate highly personalized datasets in a private setting.
This method is best suited for projects of confidential nature. Since the data does not leave the company servers, it stays confidential. For instance, to train machine learning models for a government project, the data must be collected in-house. This method is unsuitable for collecting large-scale datasets created by humans since it can take the budget and timeline of the project to unreasonable heights.
You can check our data-driven list of data collection/harvesting services to find the best option that suits your business/project needs.
3. Pre-packaged/public datasets
There are also prepackaged datasets available which are generated by humans and can be accessed for free or purchased for a price. Third-party firms generate and sell such prepackaged datasets for different applications, such as machine learning development, and update them regularly. Public datasets are generated by the general public to promote the growth and development of AI solutions. For instance, a public, free-to-download dataset can be made available to support the development of the facial recognition industry.
Public datasets can sometimes have quality issues since the data is generated by the general public and does not go through rigorous quality checks. Prepackaged datasets have better quality than public datasets but lack uniqueness. You can not use them for projects that have unique data requirements.
Such datasets are good for projects which have a limited budget and time and do not require high levels of quality and personalization.
- Crowdsourced AI Data Collection Benefits & Best Practices
- Data Collection Automation: Pros, Cons, & 3 Methods
If you need help finding a vendor or have any questions, feel free to contact us:
- “Machine-generated data.” Wikipedia. Retrieved Dec 06, 2022.
- Diaz, Jesus. (Aug 31, 2022). This 30-second fashion show demonstrates the true creative power of DALL-E 2. Retrieved: Dec 06, 2022.
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