Data Gathering in 2024: Overview, Challenges & Methods

While our previous article focuses on AI data collection, this article explores gathering data for other use cases and purposes.

Organizations and researchers need to gather large volumes of data to fuel their projects. Due to this growing need for data, the interest in data gathering has grown over the past few years (Figure 2).

Figure 2: Rising interest in data gathering in the past few years

To help data-driven businesses streamline the process of gathering data, this article explores:

• What data gathering means
• Why businesses need to gather data
• How they can gather data for different use cases
• What are some challenges of collecting data and how a service provider can help overcome them.

What does data gathering mean?

Data gathering is the process of gathering data from different sources, such as human-generated content, websites, online surveys, customer feedback, social media posts, and ready-made datasets. The collected data is used for different processes, such as developing AI-powered solutions, conducting research, or other data-hungry tasks.

Top reasons/use cases for collecting data

Organizations gather data for various reasons. This section highlights some of the top reasons why businesses should gather data.

1. Train AI/ML solutions

Training AI/ML models involves gathering diverse data types based on learning modalities to create accurate models. This is one of the most popular use cases of data gathering as the adoption of AI grows. 

Suitable methods of gathering data

The gathering method depends on the type of AI application. For example, a self-driving car model needs vast quantities of images and videos of roads, pedestrians, and other vehicles. Collecting such data could involve using cameras mounted on vehicles or working with a crowdsourcing data collection service provider. In contrast, natural language processing applications may require textual data collected from diverse sources such as books, websites, and social media platforms.

2. Deploy AI/ML solutions

During AI model deployment, data is needed to test and validate the model’s performance. This requires the collection of fresh and unseen data to ensure the model generalizes properly to new situations. 

Suitable methods of gathering

A recommendation engine may require data on user preferences and behaviors, which can be collected through in-house data collection from customer data. However, deploying a facial recognition system necessitates gathering real-world images to verify the system’s accuracy. Using prepackaged image datasets can be helpful for this purpose.

3. Improve AI/ML solutions

Improving AI/ML models is necessary as performance degrades over time due to changing data and circumstances. This requires fresh data to be collected to retrain the model and improve its performance.

Suitable methods of gathering

An AI-powered quality control system on a production line may require updated data when the product changes. This type of data can be collected internally from a PDM platform. 

On the other hand, if a machine translation model needs fresh text data to accommodate changes in language use, a more suitable method would be working with a data collection service provider.

4. Improve online marketing operations

Data needs to be gathered to improve online marketing processes.

• Research: Conducting online surveys to assess product user-friendliness, which can be done in-house or outsourced to third-party service providers. Click here to learn more about research data collection. 
• Sentiment Analysis: Evaluating customer attitudes through sentiment analysis data on social media feedback to identify keywords describing a brand.
• Personalization: Collecting user behavior data to create personalized content and recommendations, enhancing customer engagement and satisfaction.
• Market Analysis: Gathering market data to identify trends, competitor strategies, and emerging opportunities, informing decision-making and future marketing campaigns.

Suitable methods of gathering

Different data gathering methods can optimize various aspects of online marketing. 

• Web analytics tools can gather data on user behavior.
• Surveys or interviews can be used for qualitative or quantitative research to understand user experiences
• Sentiment analysis, which involves collecting user feedback from social media, can help understand customer attitudes toward a brand or product. A data collection service that also offers sentiment analysis solutions can be a good fit for this use case.

5. Search engine optimization

SEO leverages data gathering for creating accurate, up-to-date product descriptions in various languages.

Suitable methods of gathering

Keyword research is a crucial data gathering method that involves identifying popular search terms and incorporating them into the website’s content. Analyzing competitor websites, backlinks, and metadata can help refine SEO strategies, while user behavior data, such as bounce rates and session duration, can provide insights for website optimization. 

If dedicating a team is not possible for collecting data and implementing SEO-friendly practices, you can outsource the data collection process.

Check out to learn more about these data collection uses.

Data gathering challenges and how working with a service provider can help

Collecting data for your data-hungry processes can be challenging. However, working with a data gathering service provider can help overcome these challenges. 

1. Data quality and consistency

Ensuring high data quality and consistency is a primary challenge for companies during gathering data, especially for large-scale data gathering projects. Inaccurate or incomplete data can have a negative impact on the project.

 Nowadays, most data gathered is raw and unstructured and can be challenging to manage (Figure 3). Purchasing sophisticated analytics tools for unstructured data can be expensive. If you dedicate a team for the process, your employees will be spending a significant amount of time performing repetitive and error-prone data correction and structuring tasks.

How a data service provider can help

Data gathering services have quality control and data processing standards in place and can offer a scalable service based on your needs and budget. By partnering with a reliable service provider, organizations can access their expertise in curating accurate and consistent data sets. These service providers employ robust data validation and verification processes to ensure high-quality data, contributing to your data-driven projects.

Figure 3. Top unstructured data management challenges in the US and UK

For more on data collection quality assurance, check out this quick read.

2. Data privacy and compliance

Adhering to data privacy regulations, such as GDPR and CCPA, is a significant challenge for companies collecting data. Striking a balance between data gathering and privacy concerns is critical. If you dedicate a team to data gathering, it can be challenging for them to follow all data protection policies and regulations. 

How a data service provider can help

You can transfer the burden of data privacy and compliance issues to a third party for a fee. Data gathering service providers are well-versed in regulatory requirements and can help businesses navigate data privacy and compliance concerns. By implementing data governance frameworks and employing techniques like anonymization and pseudonymization, these providers can help minimize privacy risks in gathering data.

3. Data volume and complexity

Handling the vast volume and complexity of data from diverse sources, like social media and IoT devices, is a daunting task for the development team.

How a data service provider can help 

A data collection service can utilize state-of-the-art big data tools or work with a crowdsourcing model to manage and process large volumes of complex data. By incorporating data management tools or working with a large community of data collectors and processors, they can efficiently handle unstructured data and provide ready-to-use datasets much faster.

Click here to learn more about the benefits of crowdsourced data.

4. Data bias and representation

Addressing data bias and ensuring diverse representation is crucial to avoid biased AI training, unfair predictions, and reduced performance. Companies must ensure that data sets are representative of different demographics, cultures, and geographies.

How a data service provider can help

Data gathering service providers can help companies mitigate data bias by carefully selecting data sources and employing unbiased sampling techniques. These providers have extensive experience in curating diverse and representative data sets, leading to more accurate and fair AI deployment, market research, and sentiment analysis.

Next steps

You can use our data-driven list of data collection/harvesting/gathering services to find the option that best suits your data needs. 

To compare and evaluate data collection vendors, you can also download our free guide:

For more in-depth knowledge, you can also download our free data collection whitepaper:

Further reading

• Top 6 AIData Collection Best Practices
• Top 4 AI Data Collection Challenges and Their Solutions
• Top 5 AI Data Collection Trends for Data-Driven Businesses

If you need help finding a vendor or have any questions, feel free to contact us: