Understanding the different types of data: synthetic, open sourced, and created

Understanding the different types of data: synthetic, open sourced, and created

When it comes to data for your project, there are several types to choose from. Understanding the differences between synthetic, open sourced, and created data is crucial in selecting the right one for your needs.

Synthetic data refers to artificially generated data that mimics real-world data. It is created using algorithms and statistical models, providing a controlled and customizable dataset. Synthetic data is often used in scenarios where real data is unavailable or sensitive. It can be useful for testing and validation purposes, as it allows you to create various scenarios and analyze different outcomes.

Open sourced data is freely available data that is collected and shared by individuals or organizations. It can be accessed by anyone and is often used for research, analysis, and machine learning. Open sourced data provides a vast amount of information on a wide range of topics, making it a valuable resource for many projects. However, it is important to ensure the reliability and accuracy of the data when using open sourced datasets.

Created data refers to data that is collected specifically for a particular project or purpose. This could involve surveys, interviews, experiments, or observations. Created data allows you to gather information that is tailored to your needs and specifications. It can be time-consuming and costly to collect, but it provides the advantage of being highly relevant and specific to your project.

Factors to consider when choosing the right data for your project

When deciding on the type of data to use for your project, there are several factors to consider:

  • Availability and accessibility: Consider the availability and accessibility of the data. Is the data easily accessible to you? Is it readily available or do you need to request permission or pay for access? Assessing the availability and accessibility of the data will help determine if it aligns with your project's timeline and budget.
  • Relevance and quality: Ensure that the data is relevant to your project and of high quality. Assess the accuracy, reliability, and completeness of the data before making a decision. Poor-quality data can negatively impact the results and conclusions of your project.
  • Ethical considerations: Consider any ethical concerns associated with the data. If you are using open sourced data, ensure that it has been collected and shared ethically and legally. If you are collecting your own data, ensure that you follow ethical guidelines and obtain appropriate consent from participants.
  • Scope and scale: Consider the scope and scale of your project. Does the data cover the specific variables or aspects you need for your analysis? Does it provide enough data points and variability? Assess if the data aligns with the requirements of your project.
  • Credibility and validation: Evaluate the credibility of the data source. If you are using open sourced data, consider the reputation and expertise of the organization or individual providing the data. If you are creating your own data, ensure that you employ rigorous methods and validate the data through peer review or expert consultation.

It is important to carefully consider these factors before choosing the type of data for your project. Each type of data has its own advantages and limitations, and selecting the right one will ultimately depend on your project's specific requirements and objectives.

Synthetic Data

What is synthetic data and how is it generated?

Synthetic data refers to artificially generated data that mimics real-world data. It is created using algorithms and statistical models to provide a controlled and customizable dataset. These algorithms and models generate data that closely resembles the characteristics and patterns of the original data. Synthetic data can be used in scenarios where real data is unavailable or sensitive, making it a valuable resource for testing and validation purposes.

The generation of synthetic data involves various techniques such as data augmentation, data synthesis, and generative models. Data augmentation involves adding noise, perturbing values, or making modifications to existing data to create new samples. Data synthesis involves combining existing data to create new samples that exhibit similar characteristics. Generative models, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), learn the underlying data distribution and generate new data points that follow the learned distribution.

Advantages and disadvantages of using synthetic data

Using synthetic data offers several advantages and disadvantages that should be taken into consideration when choosing the right data for a project.


  • Data privacy: Synthetic data can be used as a privacy-preserving alternative to using real data. It allows organizations to generate data that maintains the privacy and confidentiality of individuals or sensitive information.
  • Data customization: Synthetic data allows for the creation of customized datasets that can be tailored to specific use cases or scenarios. This flexibility enables organizations to test various hypotheses or simulate different scenarios.
  • Data diversity: Synthetic data can provide a wider range of data samples compared to real-world data. This diversity allows for a more comprehensive analysis and testing of different scenarios.
  • Cost-effective: Generating synthetic data can be more cost-effective than collecting or acquiring real data. It eliminates the need for data collection, cleaning, and storage, which can be time-consuming and expensive.
  • Data augmentation: Synthetic data can be used to augment existing datasets, increasing the size and variability of the data. This augmentation can improve the performance and robustness of models trained on limited real data.


  • Model bias: Synthetic data may introduce biases that reflect the assumptions and limitations of the data generation process. It is crucial to ensure that the synthetic data accurately represents the real-world data to prevent biased results.
  • Limited real-world representation: Although synthetic data aims to mimic real-world data, it may not capture the full complexity and diversity of the original data. This limitation could impact the generalizability and applicability of models trained on synthetic data.
  • Data quality: The quality of synthetic data heavily relies on the accuracy and robustness of the algorithms used for its generation. Errors or inaccuracies in the generation process can lead to incorrect or misleading results.
  • Data trustworthiness: There may be concerns about the trustworthiness and reliability of synthetic data. Organizations and stakeholders may question the validity and authenticity of the generated data, raising issues of transparency and credibility.
  • Limited domain-specific knowledge: Synthetic data generation requires expertise in data modeling and algorithm design. In domains with complex structures or specialized knowledge, accurately capturing the intricacies of the data may be challenging.

Open Sourced Data

Explanation of open sourced data and its availability

Open sourced data refers to data that is freely available to the public. It is typically released under an open license, allowing anyone to access, use, and share the data without restrictions. Open sourced data can come from various sources, including government agencies, research institutions, and non-profit organizations. This data can be in the form of raw datasets, reports, images, or any other type of digital content.

The availability of open sourced data has significantly increased in recent years. With the advancement of technology and the growing emphasis on transparency and data sharing, more organizations are recognizing the value of making their data open to the public. Government agencies, in particular, are playing a crucial role in releasing large amounts of open sourced data, providing valuable insights for businesses, researchers, and the general public.

Benefits and limitations of using open sourced data

Using open sourced data offers several benefits, but it also has its limitations. Here are some key points to consider:


  • Access to diverse data: Open sourced data provides access to a wide range of datasets from various domains and industries. This diversity allows for a comprehensive analysis and the exploration of different perspectives and insights.
  • Cost-effective: Open sourced data is available for free or at a minimal cost, reducing the financial burden of data acquisition. This accessibility makes it particularly beneficial for small businesses, researchers, and individuals with limited budgets.
  • Promotes innovation and collaboration: Open sourced data encourages innovation and collaboration by allowing individuals and organizations to build upon existing data. It enables the development of new tools, applications, and research that can benefit society as a whole.
  • Transparency and accountability: Open sourced data promotes transparency and accountability, particularly when it comes to government or public sector data. It allows citizens to access information about public services, budgets, and decision-making processes, fostering trust and participation in governance.
  • Data-driven decision-making: By leveraging open sourced data, organizations can make informed decisions based on objective and reliable information. This data can be used for market research, trend analysis, risk assessment, and evidence-based policymaking.


  • Data accuracy and quality: Open sourced data may vary in terms of accuracy and quality. It is crucial to carefully evaluate the source and credibility of the data before using it for analysis or decision-making.
  • Data availability and relevance: While there is a vast amount of open sourced data available, it may not always align with specific research or business needs. Finding relevant and up-to-date data can sometimes be a challenge, requiring thorough search and evaluation.
  • Data privacy and security: Open sourced data may contain sensitive or personal information that needs to be handled with care. It is essential to comply with data protection regulations and ensure the privacy and security of individuals and organizations involved.
  • Data bias and representativeness: Open sourced data can be subject to biases and representativeness issues. It may not fully represent the diversity of a population or capture specific subgroups, potentially leading to skewed or misleading results.
  • Data integration and compatibility: Integrating and analyzing multiple open sourced datasets can be complex due to differences in data formats, structures, and compatibility. Data cleaning, preprocessing, and integration efforts may be required to ensure seamless analysis.

Created Data

What is created data and how is it generated?

Created data refers to data that is generated by a business or organization for specific purposes. This type of data is unique and tailored to the specific needs and goals of the organization. It can be collected through various methods such as surveys, experiments, simulations, or observations.

When generating their own data, organizations have control over the data collection process and can design experiments or surveys to gather the specific information they require. This allows them to obtain data that is directly relevant to their business or research objectives.

Custom datasets
Custom datasets | Img. source Keymakr.com

Pros and cons of generating your own data


  • Customization: Creating your own data allows you to collect information that is tailored to your specific needs. This ensures that the data is highly relevant and provides insights that are directly applicable to your business or research goals.
  • Data quality control: By generating your own data, you have control over the data collection process, ensuring that it is accurate and reliable. You can implement quality control measures to minimize errors and biases, ensuring the integrity of the data.
  • Flexibility: Generating your own data gives you the flexibility to collect data on specific variables or aspects that are important to your organization. You can design experiments or surveys that focus on areas that are of particular interest, allowing you to gain a deeper understanding of those specific aspects.
  • Confidentiality: Creating your own data provides you with the advantage of keeping sensitive information confidential. This can be particularly important for organizations dealing with proprietary data or sensitive customer information.


  • Time and cost: Generating your own data can be time-consuming and expensive. It requires planning, implementing data collection methods, managing participants or respondents, and analyzing the data. Additionally, there may be costs associated with data collection tools or software.
  • Limited sample size: Depending on the resources and reach of your organization, generating your own data may result in a relatively small sample size. This could limit the generalizability of your findings and reduce the statistical power of your analysis.
  • Biases and limitations: While you have control over the data collection process, it is important to be aware of potential biases and limitations. Biases can arise from factors such as participant selection or response biases. Additionally, certain populations or segments may be difficult to reach or may choose not to participate, which can introduce sample biases.
  • Data validity and reliability: The validity and reliability of the data generated by your organization are contingent on the rigor and quality of the data collection methods. It is essential to follow best practices and employ reliable measurement tools to ensure the accuracy and consistency of the data.

Choosing the Right Data

When it comes to data, businesses have various options to consider, including synthetic data, open sourced data, and created data. Each type has its own advantages and considerations. Therefore, it is important to carefully evaluate the options and choose the most suitable data for your project. Here are some factors to consider when deciding between synthetic, open sourced, and created data, as well as some best practices for selecting the most appropriate data for your needs.

Factors to consider when deciding between synthetic, open sourced, and created data

  • Data Purpose and Relevance: The first factor to consider is the purpose of your project and the relevance of the data to your objectives. Synthetic data is artificially generated and can be customized to fit specific requirements. Open sourced data, on the other hand, is publicly available and can provide a broader scope of information. Created data refers to data that you generate specifically for your project. Consider which type of data aligns most closely with your project goals and requirements.
  • Data Quality: The quality of the data is crucial for accurate analysis and decision-making. Synthetic data can be controlled and tailored to meet specific quality standards. Open sourced data varies in quality, as it is sourced from various platforms and may require cleaning and validation. Created data gives you the ability to ensure high-quality data by carefully designing and implementing the data collection process.
  • Data Privacy and Security: Data privacy and security are essential considerations in today's digital landscape. Synthetic data can be generated without exposing real customer or organizational data, reducing privacy concerns. Open sourced data may have privacy implications if it contains personally identifiable information. Created data allows you to maintain full control over data privacy and security.
  • Data Cost: Cost is another crucial factor to consider. Synthetic data can be more cost-effective since it does not require extensive data collection efforts. Open sourced data is generally free or inexpensive, but it may require additional resources for processing and cleaning. Created data can be costlier as it involves designing and implementing data collection methods and managing participants or respondents.

Best practices for selecting the most suitable data for your project

  • Define Project Objectives: Clearly define your project objectives and the specific data requirements to achieve those goals. This will help you determine which type of data is most suitable and relevant.
  • Evaluate Data Quality: Assess the quality and accuracy of the data. Consider factors such as data source credibility, completeness, consistency, and reliability.
  • Consider Data Privacy and Security: Evaluate the privacy implications of the data you are considering. Ensure compliance with data protection regulations and assess any potential risks to sensitive information.
  • Assess Data Accessibility: Determine the accessibility of the data you need. Consider factors such as availability, ease of access, and any restrictions or limitations on its usage.
  • Weigh Data Cost and Resources: Consider the cost and resources required for each type of data. Evaluate the trade-offs between data quality, relevance, and cost to determine the most cost-effective option for your project.
  • Consider Data Scalability: Evaluate the scalability of the data for your project. Consider whether the data can be easily expanded or adapted as your project evolves.

Ultimately, choosing the right data type depends on the specific needs and goals of your project. Synthetic data offers customization and control, open sourced data provides a broader scope of information, and created data allows for tailored data collection. By carefully considering factors such as data purpose, quality, privacy, cost, and resources, you can make an informed decision and select the most suitable data for your project.

Key Players in Data Filming and Collection

  • Keymakr data creation: Renowned for its expertise in data annotation, Keymakr also excels in data filming and collection, providing customized datasets that cater to specific project requirements. Their approach ensures that unique scenarios, lighting conditions, and environments are captured, ensuring a rich and diverse dataset.
  • One of the core advantages here is the ability to provide a production process in any location. If needed, the process could be co-located to the client to ensure clear management, escaping messengers and chatting.
  • Samasource: With a focus on ethical AI, Samasource offers data collection and sourcing services tailored to the unique needs of computer vision projects. performance and reliability of AI models.
  • iMerit: iMerit specializes in data solutions, including data collection and enrichment services. They provide access to a global workforce capable of capturing and generating diverse datasets, ensuring high-quality inputs for computer vision algorithms.
  • Hive: Hive offers a comprehensive suite of AI solutions, including data labeling and collection. Their data collection services are designed to capture unique and specific scenarios, ensuring that computer vision models are exposed to a wide range of data.

Data plays a crucial role in today's business landscape. Choosing the right data type, whether it be synthetic, open sourced, or created data, requires careful consideration of factors such as data purpose, quality, privacy, cost, and resources. By following best practices and taking these factors into account, businesses can make informed decisions and select the most appropriate data for their projects.

Let the right data be with you ;-)