Organizations urgently need a robust data strategy to unleash the full power of Artificial Intelligence (AI) because they have diverse data in various areas. This foundation involves a comprehensive approach.
Organizations need to make sure their data is secure and well-managed. They also need to integrate AI systems, maintain clean data practices, and ensure their employees are skilled with current analytics tools. This challenge is getting bigger, as the International Data Corporation predicts a 23% annual growth in data creation and replication from 2020 to 2025.
Good data is crucial for AI algorithms. It helps recognize patterns, identify issues, and create personalized experiences, making businesses more effective and competitive. However, a recent Ernst and Young report found that 81% of companies have their data scattered across different parts of their organization. This poses a challenge in integrating data from various sources for AI applications, limiting the full potential of these tools.
Organizations must recognize the importance of preparing data for AI. This involves regularly cleaning and preprocessing data to remove errors and ensure its quality for AI use. However, there is a need for additional education to empower individuals with the essential knowledge and skills for handling data.
CISCO Study on Analytics Tools and Readiness
Good data analytics tools and AI applications work well together, and business leaders know it. A study by CISCO found that 67% of global respondents liked how their analytics tools handle complex AI-related data sets. But, there's a problem: 74% said their tools aren't fully integrated with data sources and AI platforms. In fact, 31% of respondents said their tools are not integrated or only somewhat integrated at best.
As AI becomes a big deal in business, there are new concerns about the quality of data used to train AI models. Many companies are using data from outside sources, so they're now making efforts to check the quality of that data. About 76% of companies worldwide are doing advanced checks on external data to make sure it's reliable for training AI. Almost all of them (97%) keep track of where the data comes from, but not everyone is doing it effectively. Only 4 out of 10 companies have a structured system for tracking data sources, and it's not integrated with all AI projects. Meanwhile, 17% have some basic tracking but lack detailed information about the data's origins.
AI Data Management Training and Outsourcing Dilemma
Training AI systems to perform specific tasks with accuracy and reliability requires a large amount of data. Many companies hire gig workers from platforms like Upwork, Online Jobs, and Mechanical Turk to complete tasks that are difficult to automate, such as solving CAPTCHAs, labeling data, and annotating text. The gathered data is then employed to train AI models. However, these workers are often poorly compensated and pressured to complete tasks quickly.
Due to these challenges, some gig workers may be turning to tools like ChatGPT to increase their earnings. A team of researchers from the Swiss Federal Institute of Technology conducted a study to determine the extent of this trend. They hired 44 people on Amazon Mechanical Turk to summarize excerpts from medical research papers. Using an AI model they trained, the researchers analyzed responses for signs of ChatGPT output, such as repetitive word choices. They also examined workers' keystrokes to identify potential copying and pasting, indicating the use of external responses.
The study which was shared on arXiv estimated that approximately 33% to 46% of the workers had employed AI models like ChatGPT. The researchers anticipate this percentage to rise as AI systems like ChatGPT become more powerful and accessible.
Strategies to Eliminate Data Silos in Organizations
AI relies on data as its foundation. However, when data is stored in isolated pockets or silos within an organization, it can create problems for overall data analysis and collaboration between different AI projects. This can lead to duplication of efforts, inefficient resource use, and inconsistencies in decision-making based on data. Without centralized oversight, maintaining consistent data quality becomes challenging.
For AI to reach its full potential, it's critical to manage data in a centralized way. Fixing this issue is important for AI to function effectively, along with other tasks like cleaning data, ensuring quality, adhering to security and regulatory standards, and honing processing skills.
To eliminate data silos in an organization implementing AI, start by conducting a thorough audit of existing data sources to understand their structure and connections. Then, implement a centralized data management system that promotes integration, consistency, and accessibility across departments. Promoting a team culture with straightforward rules and continuous training can promote consistent data practices and boost the sharing and use of data between departments.