Three Key Factors to Keep in Mind When Getting Data Ready for Generative AI

Three Key Factors to Keep in Mind When Getting Data Ready for Generative AI

Introduction:

Introduction:

The growing interest in generative artificial intelligence (AI) tools like ChatGPT is fueling a surge in business investment in AI and machine learning (ML) technologies. Experts project that spending in this area will reach $154 billion this year, a significant increase from last year. However, building AI tools from scratch can be prohibitively expensive for most businesses. As a result, many organizations are focusing on fine-tuning existing models to align with their specific needs. When preparing for an AI/ML initiative, businesses must consider three critical factors: data accessibility, data quality, and data quantity. By addressing these factors and implementing modern data governance standards, businesses can set themselves up for success in AI/ML projects.

Full Article: Three Key Factors to Keep in Mind When Getting Data Ready for Generative AI

Rapid Growth of Business Investment in AI and Machine Learning Predicted for this Year

Excitement around breakthrough generative artificial intelligence (AI) tools like ChatGPT has contributed to industry analysts projecting rapid growth in business investment in AI and machine learning (ML) technologies. According to IDC, spending in this area is expected to reach $154 billion this year, which is nearly 27% more than last year’s investment in AI/ML-related hardware, software, and services. However, the organizations behind these AI tools have deep pockets, access to vast datasets, and well-established data management practices, making it extremely expensive for most businesses to train large language models from scratch.

Fine-Tuning Existing Models: A Cost-Effective Approach

Given the prohibitive costs of training a new model, many businesses are looking for ways to finetune existing base models to align them with their specific preferences and narrative. For generative AI and language models, this process involves evaluating training data in specific formats, iteratively aligning the data with the desired narrative, and feeding clean source data into the language model.

Three Critical Factors in Data Assessment for AI/ML Initiatives

When preparing for an AI/ML initiative, companies should consider three crucial factors to save time and streamline the data assessment process:

1. Data Accessibility: Companies often face challenges with data accessibility as it is scattered across multiple systems or stored in incompatible formats. This is particularly common after mergers and acquisitions when data is stored in different clouds and managed using different architectures. Aggregating and standardizing the data into a single format becomes difficult, hindering effective data utilization for ML scaling.

2. Data Quality: High-quality curated data is essential for domain-specific generative AI. Pulling data from systems not designed for analytics can lead to garbage in, garbage out scenarios. Project leaders may need to combine data from various sources and monitor it over time to prevent data drift or model drift, where the data used to train the AI/ML tool no longer reflects the model’s purpose. Curating and maintaining high-quality data is crucial for accurate and reliable AI/ML outcomes.

3. Data Quantity: Businesses often augment their internal data with data from external sources. However, integrating data from third-party sources can pose challenges in terms of quality and frequency. Data may have time gaps or come in different formats. It needs to be transformed into a standard format and observed regularly to remain fresh, usable, and relevant to the AI/ML initiative. Regulatory implications and jurisdictional rules regarding data storage should also be considered.

Working Toward a Successful AI/ML Data Project

To ensure a successful AI/ML data project, Gartner predicts that companies need to adhere to modern data governance standards. It is crucial to define objectives, gain organizational buy-in, set clear goals, and create consensus on the value of the program. Assessing data quality and suitability for AI/ML projects is also essential. Project leaders should ensure the data has core quality attributes required for any analytics project and is complete, accurate, and timely. Leveraging resources such as skilled data engineers or data engineering tools can help businesses accelerate their AI/ML project and focus on delivering valuable insights.

The Importance of Human Oversight in Generative AI

Generative AI projects, although exciting, require intensive human oversight to leverage models effectively and derive value from them. While the technology has advanced, reliance on human involvement is crucial to ensure accuracy and reliable outcomes.

About the Author

Will Freiberg is a technology executive and entrepreneurial leader with expertise in sales, product, business development, customer success, and strategic initiatives. He currently serves as the CEO of Crux, a cloud-based data integration, transformation, and operations platform. Will’s experience includes leadership positions at D2iQ, where he played a vital role in defining the cloud-native container industry.

Summary: Three Key Factors to Keep in Mind When Getting Data Ready for Generative AI

The excitement surrounding generative artificial intelligence (AI) tools has led to rapid growth in business investment in AI and machine learning (ML) technologies. IDC predicts that spending on AI and ML will reach $154 billion this year, a 27% increase from last year. However, training large language models from scratch can be costly and time-consuming. Therefore, many businesses are looking to finetune existing models to align them with their data and objectives. When preparing for an AI/ML initiative, companies should consider three critical factors: data accessibility, data quality, and data quantity. Additionally, businesses should define clear objectives, ensure data quality, and consider partnering with experts to streamline their AI/ML projects.

Frequently Asked Questions:

Questions and Answers About Data Science

Question 1: What is data science?
Answer: Data science is a multidisciplinary field that combines computer science, statistics, and domain knowledge to extract valuable insights and knowledge from structured and unstructured data. It involves cleaning, processing, and analyzing large amounts of data to uncover patterns, make predictions, and drive decision-making in various industries.

Question 2: What skills are required to become a data scientist?
Answer: Aspiring data scientists should have a strong foundation in programming languages such as Python or R, as well as a good understanding of statistics and mathematics. They should also possess excellent problem-solving and analytical skills, as well as the ability to communicate effectively and present findings to both technical and non-technical audiences.

Question 3: How is data science different from data analytics?
Answer: While data science and data analytics are closely related, there are some differences between the two. Data analytics typically focuses on analyzing past or historical data to understand trends and patterns, whereas data science goes beyond that to also involve predictive modeling and utilizing advanced algorithms to make future predictions and derive actionable insights.

Question 4: In which industries is data science being used extensively?
Answer: Data science is being widely adopted in various industries including finance, healthcare, retail, marketing, and technology. In finance, for example, data science is used for fraud detection and risk assessment. In healthcare, it helps in disease prediction and personalized medicine. Retail and marketing industries leverage data science for customer segmentation, targeted advertising, and demand forecasting.

Question 5: How does data science contribute to business success?
Answer: Data science plays a crucial role in driving business success by enabling organizations to make data-driven decisions. It helps identify market trends, customer preferences, and potential risks or opportunities. By leveraging data science techniques, businesses can gain a competitive edge, optimize processes, improve customer experience, and increase revenue. Additionally, data science also aids in automating tasks, optimizing supply chains, and reducing costs.