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💡 Opportunity: Data for AI & LLMs
🗂 Overview:
By now, we’ve probably all heard the quote “Data is the new oil”. This was said back in 2006 by Clive Humby. But one thing missing from that quote is that he also said “Like oil, data is valuable, but if unrefined it cannot really be used”.
Just as unrefined oil sits in barrels without purpose, raw data is meaningless stored in servers and databases.
I’ve previously explained how you can make money with databases. But today we’re going to take it up a notch and talk about how AI is turning previously unrefined data into goldmines and how you can too.
Now that AI has taken over, people are finding more and more ways to use data. This has come in some unique ways that would have likely never been useful before.
⭐️ Examples:
Reddit: We’ve recently seen huge data deals for AI learning. Google paid Reddit $60m to “make its content available for training the search engine giant's artificial intelligence models”.
Newscorp: Likewise, OpenAI partnered with News Corp in an even bigger deal ($250m) to improve their models and ensure higher journalistic standards.
Code Repositories: Microsoft owns Github and has used its enormous code repository to train its CoPilot AI feature. Likewise, Mistral AI just released a competitor Codestral (also backed by Microsoft).
Legal Documents: Companies like Luminance and others are using legal documents from around the world to train LLMs, helping to process and understand legal documents across various jurisdictions. There is also one AI trained on court decisions which can (with 79% accuracy) predict the outcome of a trial.
Medical Journals and Health Records: Expanding access to anonymized patient records and detailed medical research improves the accuracy of medical advice. Flatiron Health curates and analyses oncology-specific electronic health record data, providing valuable research and clinical insights.
Epidemic Prediction: BlueDot uses global news reports, animal and plant disease networks, and official proclamations to predict disease outbreaks. The AI notably flagged a cluster of unusual pneumonia cases in Wuhan, China, several days before the WHO released its statement on COVID-19.
👎 Problems:
Businesses often face several challenges when trying to use their data effectively and make the most out of it. Here are some common issues:
Data Quality: Poor data quality due to errors, duplicates, and incomplete information can significantly hinder analysis and decision-making processes.
Lack of Integration: Integrating disparate data systems and technologies can be technically challenging, requiring significant resources and expertise.
Data Overload: Companies frequently collect more data than they can analyze, leading to 'data swamp' conditions where extracting valuable insights becomes impractical.
Compliance and Privacy Concerns: Navigating the complex landscape of data privacy regulations (like GDPR or CCPA) requires robust compliance strategies that many businesses struggle to implement effectively.
Skill Gaps: There is often a lack of skilled personnel who can analyze and interpret complex datasets, which limits a company's ability to leverage data-driven insights.
Cost of Data Management: The infrastructure and tools required to store, process, and analyze large volumes of data can be costly, making it difficult for smaller businesses to compete.
Real-Time Data Processing: Many businesses require real-time or near-real-time data processing to make timely decisions, but setting up systems that can handle this is complex and resource-intensive.
Security Vulnerabilities: As data becomes a critical asset, the risk of data breaches increases. Protecting data against cyber threats requires continuous investment in security technologies and practices.
🧐 Opportunities:
Opportunity: Data Brokerage for AI
Problem: Developers and companies need high-quality, well-curated, and legally compliant datasets necessary for training and refining effective AI models.
Similar to how Apollo sells datasets of leads to businesses or how BuiltWith sells datasets of internet technology usage, you could sell datasets curated for specific purposes to AI developers and companies.
A lot of money can be made with data - this listing just went live on acquire.com. They made $2.5m (profit) in the last 12 months selling GPS and POI data to people with location-contextual products.
You could offer pre-made datasets that people can buy off the shelf and/or custom datasets that companies employ you to create for their specific needs.
Here are some examples of existing data brokerage companies:
Hugging Face is an AI community with a library of existing datasets. It’s a massive library and shows how in-demand good datasets are. But a lot of businesses need things that Hugging Face can’t offer:
However, there are several reasons why a company might opt for other sources or choose to engage a data brokerage instead:
Customization Needs: Datasets from Hugging Face, while diverse, may not meet the specific needs or granularity required by certain projects. Companies might need data tailored to specific problems, demographics, or industries that aren't adequately covered by available datasets.
Proprietary Data Requirements: Some companies require unique datasets that can provide them with a competitive edge. Data brokerages can offer proprietary data that isn't available publicly, which can be crucial for businesses operating in niche markets.
Data Privacy and Compliance: Companies must often adhere to strict data privacy laws like GDPR or HIPAA. Data from public sources or repositories might not always guarantee compliance with these regulations, whereas a data brokerage can provide datasets that are compliant with legal standards, reducing legal risk.
Data Quality and Reliability: While Hugging Face offers a quality selection, businesses might require data with guaranteed accuracy and reliability, vetted through rigorous processes. Data brokerages can provide enhanced quality assurance, ensuring the data is clean, well-documented, and ready for immediate use.
Integration and Support Services: Data brokerages often provide additional services such as data integration support, customized analytics, and ongoing data management, which are crucial for companies without the technical expertise to handle large datasets or integrate them into their existing systems.
Exclusive Access: Some brokerages have exclusive rights to certain datasets or have created niche datasets that are not available on public platforms, which can be invaluable for specific research and development efforts.
💡 You can access 3 more opportunities for free at the bottom of this email.
🛠 How to Build:
Market Research and Niche Identification:
Conduct research to identify specific industries and sectors that have a high demand for AI but struggle with data acquisition.
Analyze competition and current market offerings to find gaps in data types, quality, or specific use cases.
Data Sourcing and Partnership Development:
Forge relationships with data collectors, companies, and digital platforms to source raw data. Ensure diversity in data sources to enhance the variety and richness of the dataset offerings.
Data Processing Infrastructure:
Invest in or develop software tools for data cleaning, processing, and annotation. Consider cloud storage solutions for scalability and security.
Product Development:
Create a range of product offerings, including both pre-made and custom dataset solutions. Design user-friendly interfaces for clients to explore and purchase datasets or submit briefs for custom solutions.
Quality Assurance and Testing:
Implement rigorous testing phases to ensure data accuracy, reliability, and relevance. Continuously update datasets to keep them current and useful.
🚀 How to Grow:
Digital Marketing Campaigns:
Pay-Per-Click (PPC) Campaigns: Implement targeted PPC campaigns focusing on high-intent keywords related to AI data needs and data brokerage services.
LinkedIn Ads: Utilize LinkedIn advertising to target professionals and companies in specific industries that benefit from AI development, such as tech, healthcare, finance, and automotive sectors.
Social Media Content:
LinkedIn: On top of running targeted ads, publish regular posts about your services, customer stories, and the value of accurate data for AI.
Twitter: Share quick tips, industry news, and bite-sized insights into how quality data can transform AI projects. Use hashtags to increase visibility in relevant conversations.
YouTube: Create videos showcasing what you do, industry updates and new LLMs/tools/datasets you’ve created.
Email Marketing:
Newsletter: Develop a weekly newsletter that shares insights into data trends, case studies, new dataset releases, and exclusive offers.
Personalized Email Campaigns: Send tailored emails to leads based on their industry and previous interactions with your site, providing them with relevant information and solutions.
Webinars and Online Workshops:
Host webinars and workshops that address common data challenges in AI development and demonstrate how your datasets can solve these problems. Use these sessions to gather leads and interact directly with potential customers.
Search Engine Optimization (SEO):
Optimize your website’s content to rank higher for keywords related to AI datasets, data brokerage, and industry-specific data applications. This includes creating rich content such as blog posts, white papers, and infographics that can attract backlinks and improve domain authority.
Client Onboarding Experience:
Enhance the customer onboarding process with educational content and resources that help them understand how to best utilize the data they purchase. A strong onboarding experience can lead to higher customer satisfaction and retention.
Community Building:
Build a community around your brand by creating forums or social media groups where professionals can discuss AI, share data insights, and solve common problems together. This fosters loyalty and establishes your brand as a key player in the AI community.
Local Partnerships:
Establish connections with local universities, tech incubators, and research organizations. Sponsor hackathons and competitions where your datasets can be used, increasing brand visibility and engagement among emerging talents in the field.
Customer Feedback and Continuous Improvement:
Regularly collect customer feedback to improve your products and services. Showcase how this feedback has been implemented to show commitment to customer satisfaction and continual improvement.
💡 Bonus Opportunities
Bonus Opportunity #1: Business Specific LLMs
Problem: Businesses have a lot of data and they don’t know what to do with. They want to use AI but they don’t know how.
This idea involves tailoring AI solutions to leverage a company’s existing data. This concept goes beyond traditional AI applications by creating specialized AI models that understand and operate seamlessly within the unique context of each business.
Core Offering: Design and implement customized LLMs that can integrate directly into business operations. These LLMs would use the company’s own data to drive decision-making, automate processes, enhance customer interactions, and optimize operations.
Data Assessment and Strategy Consulting:
Begin with a comprehensive assessment of the client's data infrastructure, quality, and governance. Provide strategic advice on data collection, storage, and management to ensure that the data can effectively train AI models.
Custom AI Development:
Develop bespoke AI models tailored to the client’s specific needs. This could involve creating LLMs that understand industry-specific jargon and workflows, can automate routine tasks, generate reports, or provide predictive insights.
Integration and Deployment:
Handle the seamless integration of these AI models into existing business systems and workflows. Ensure that AI tools interact well with legacy systems and can scale with business growth.
Training and Support:
Provide training to employees on how to interact with and get the most out of the AI tools. Offer ongoing support and maintenance, including updates as business needs evolve or as new data becomes available.
Opportunity #2: Upskilling In AI
Problem: Employers want to upskill their employees to help incorpoarte AI into their business.
A lot of businesses have yearly training budgets they offer to each employee others have overall training budgets they use when necessary.
With AI becoming more and more useful for everyday businesses you could offer workshops and training programs to improve data and AI literacy within organizations, helping teams understand and use data more effectively for example, how to use this data to create useful AI tools/models for their firm.
You could offer the below services:
Customized Training Modules: Develop a curriculum that can be customized for different departments and roles within a company, ranging from basic data literacy for non-technical staff to advanced courses on AI model development for IT departments.
Hands-On Workshops: Organize interactive workshops where participants work with actual datasets from their company to solve real problems. This could include data visualization, predictive analytics, and the basics of machine learning models.
AI Development Bootcamps: Offer intensive bootcamps that guide technical staff through the process of creating and implementing AI models. Cover topics like data cleaning, model selection, training, validation, and deployment using industry-standard tools and languages like Python, R, TensorFlow, and PyTorch.
Continuous Learning and Support: Provide ongoing support and learning resources after the initial training. This could include access to a web portal with tutorials, forums, and updates on new data analysis techniques and AI developments.
Certification Programs: Allow participants to earn certifications in data and AI literacy, which can enhance their resumes and encourage a culture of continuous learning within the organization.
Opportunity #3: AI-Powered Marketing Analysis Tools
Problem: Digital marketers spend too much time making reports instead of doing actual work to bring the business more money.
This is something I’ve wanted to build for myself to use in some of the businesses I work with.
This could be done in a few ways but the way I wanted to use it is this:
Data Consolidation:
Source Integration: Connect Google Analytics and data from various marketing channels (Meta, Google Ads, Email, etc.) to a centralized source, such as a Google Sheet.
Data Synchronization: Ensure all data is accurately matched and synchronized across sources to maintain consistency and reliability.
Activity Log Documentation:
Universal Documentation: Establish a comprehensive document where all changes made on each platform are logged. This includes modifications like budget adjustments or bid strategy alterations, along with the date and rationale for each change.
Efficiency: This process integrates seamlessly into existing workflows, as these changes should already be tracked, thus requiring no additional day-to-day effort.
AI Integration and Reporting:
AI Training: Connect the consolidated data and activity log to an AI system, trained using your platform’s historical data, past reports, and advertiser platform documentation.
Dynamic Reporting: Enable the AI to generate insightful reports and charts, offering commentary on why certain events occurred based on logged activities. The system should support querying in natural language to facilitate interactive, on-demand report generation and trend analysis.
Phased Rollout:
Initial Setup: Start with individual AI systems for each channel to manage complexity and ensure precision.
Gradual Integration: Gradually integrate these systems into a comprehensive, all-encompassing tool that handles multiple channels simultaneously.
Advanced Features:
Forecasting: Utilize AI to aid in forecasting future trends based on historical data.
Spend Allocation: Assist in optimizing budget allocation across various channels to maximize ROI.
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