Most Companies Are Severely Lacking Data Access, and 71% Believe Synthetic Data Can be the Solution

Most Companies Are Severely Lacking Data Access, and 71% Believe Synthetic Data Can be the Solution

Introduction:

Introducing the groundbreaking synthetic data survey conducted by MOSTLY AI, the leading provider of AI-powered synthetic data. With the goal of understanding the state of synthetic data in 2023, the survey sought to address the challenges faced by companies in adopting and scaling AI/ML. The results were eye-opening, revealing that only 15% of AI/ML models are currently in production. Lack of AI/ML talent and data access were cited as major obstacles. However, the survey also highlighted the potential of synthetic data, with 71% of respondents agreeing that it is the missing piece needed for AI/ML success. Learn more about the power of synthetic data and how it is revolutionizing the data science landscape.

Full Article: Most Companies Are Severely Lacking Data Access, and 71% Believe Synthetic Data Can be the Solution

Synthetic Data Survey Reveals the State of AI/ML and Data Access in 2023

MOSTLY AI, in collaboration with KDnuggets, recently conducted a groundbreaking synthetic data survey within the data science AI/ML community. The goal was to gain insight into the state of synthetic data in 2023 and understand the challenges faced by companies in adopting and scaling AI/ML.

Challenges in AI/ML Adoption

The survey results highlight some key obstacles hindering the success of AI/ML projects. Of the respondents, 35% identified a lack of AI/ML talent as a major challenge, while 28% cited a lack of data access. It was also revealed that approximately 61% of participants experience delays of several months in accessing quality data. Interestingly, 71% of respondents believe that synthetic data can fill this data gap and help AI/ML projects succeed.

The Role of Synthetic Data in 2023

The current state of synthetic data is heavily influenced by the hype surrounding generative AI and the advancements in AI-powered technologies. As demonstrated by the widespread interest in ChatGPT, there is a growing excitement among professionals in leveraging AI in their work. AI-powered synthetic data generators offer a viable solution by providing privacy-safe alternatives that can serve as a replacement for original data. This allows for shorter time-to-data, easier data access, and automation of data science tasks.

Adoption and Understanding of Synthetic Data

Tobi Hann, CEO of MOSTLY AI, believes that synthetic data platforms are revolutionizing data-centric AI/ML across various industries. Sectors such as banking, insurance, and healthcare have already experienced significant adoption due to the handling of sensitive and business-critical data. However, data access and privacy concerns remain barriers to wider adoption.

Reasons for AI/ML Projects Failure

While AI-powered tools are increasingly embraced, the deployment of large-scale AI/ML models still poses challenges. The survey revealed that 35% of respondents attributed the failure of AI/ML projects to a lack of AI/ML talent, while 28% pointed to the issue of data access. The solution lies in leveraging AI-generated synthetic data, which can address both talent and data access challenges.

Data Access Bottleneck

One of the most surprising findings of the survey was that only 18% of respondents reported no issues with data access. For a considerable portion of participants, accessing quality data takes weeks (20%) or even months (61%). This lack of data access restricts the progress of data-centric projects. To keep up with the AI race, companies must prioritize meaningful data democratization efforts that enable AI/ML talent to grow and develop expertise.

The Missing Piece: Synthetic Data

Respondents overwhelmingly agreed that synthetic data is the missing puzzle piece for AI/ML project success, with 71% expressing this view. Gartner estimates that by 2030, synthetic data will surpass real data in AI models, indicating a shift toward the future of AI. Consequently, 72% of participants plan to adopt an AI-powered synthetic data generator in the coming years, with nearly 40% planning to do so within the next three months.

Education and Misconceptions

The survey also revealed a widespread lack of understanding among AI/ML experts regarding the nuances of synthetic data. Sixty-nine percent of respondents were unaware of the difference between rule-based and AI-generated synthetic data. Synthetic data companies have a responsibility to educate data consumers and facilitate hands-on experience to promote the effective use of synthetic data.

Anonymization Techniques and Synthetic Data Potential

In terms of data anonymization, 49% of respondents stated that they use data masking, while 20% opt to remove personally identifiable information (PII). However, these approaches are insufficient from both privacy and data utility perspectives. Only 31% of participants reported using privacy-enhancing technologies like AI-generated synthetic data. This indicates the need for greater adoption and awareness of privacy-enhancing techniques.

Looking Ahead

MOSTLY AI will continue to monitor and analyze synthetic data trends, conducting another survey next year to track the progress and changes in the field. To stay updated on the latest news, research, and regulations related to synthetic data, sign up for the monthly Synthetic Data Newsletter. If you’re ready to accelerate your data-centric projects, explore the potential of AI-generated synthetic data.

Summary: Most Companies Are Severely Lacking Data Access, and 71% Believe Synthetic Data Can be the Solution

MOSTLY AI, in collaboration with KDnuggets, conducted a survey on the state of synthetic data in the AI/ML community. The survey aimed to understand the challenges companies face in adopting and scaling AI/ML, the understanding of AI-generated synthetic data, and the data access challenges in 2023. The results revealed that only 15% of AI/ML models are in production, with 35% attributing the failure of projects to a lack of AI/ML talent and 28% to a lack of data access. Synthetic data was seen as the missing piece needed for AI/ML projects to succeed. The survey highlighted the need to educate the data community about the benefits and use cases of synthetic data. MOSTLY AI’s CEO, Tobi Hann, believes that synthetic data platforms are revolutionizing data-centric AI/ML across industries. Access to quality data remains a bottleneck for organizations, but synthetic data generators offer a solution by providing representative synthetic data for AI/ML tasks. The survey also emphasized the importance of data democratization to enable the growth of AI/ML talent and the rise of citizen data scientists. The future looks promising for synthetic data, with 72% of respondents planning to use AI-powered synthetic data generators in the next few years. However, misconceptions about synthetic data are prevalent, highlighting the need for education and easy-to-use platforms. Synthetic data has the potential to revolutionize data anonymization techniques, with privacy-enhancing technologies accounting for 31% of anonymization methods used by respondents. MOSTLY AI will continue to monitor synthetic data trends and invites interested parties to sign up for their newsletter or try their free-forever account to experience the benefits of synthetic data generation.

Frequently Asked Questions:

Q1: What is Data Science?
A1: Data Science is a multidisciplinary field that involves extraction, analysis, interpretation, and representation of data to gain insights and solve complex problems. It combines elements of mathematics, statistics, computer science, domain expertise, and communication skills to make data-driven decisions and predictions.

Q2: What are the key skills required for a Data Scientist?
A2: Data Scientists should possess a strong foundation in mathematics and statistics, along with programming skills (R or Python), data visualization, and machine learning. Additionally, they should have domain knowledge, critical thinking abilities, and effective communication skills to explain their findings to non-technical stakeholders.

Q3: How is Big Data related to Data Science?
A3: Big Data refers to the vast amount of structured and unstructured data collected from various sources. Data Science plays a crucial role in handling and analyzing this Big Data. It utilizes techniques like data mining, machine learning, and predictive modeling to extract valuable insights and patterns from large datasets, enabling organizations to make informed decisions.

Q4: What industries benefit from Data Science?
A4: Almost every industry benefits from Data Science, including finance, healthcare, retail, marketing, e-commerce, and telecommunications. Data Science can help companies optimize their processes, improve customer experience, develop personalized marketing strategies, detect fraudulent activities, and make data-driven decisions, leading to increased profitability and competitive advantage.

Q5: What are the ethical considerations in Data Science?
A5: Ethical considerations in Data Science involve privacy, security, and biases. With the abundance of data being collected, it is crucial to ensure that personal information is protected and used responsibly. Data scientists should also be mindful of any biases present within the data that could lead to discriminatory outcomes. Transparency and accountability are key to maintaining ethical practices in the field of Data Science.