High ROI Data Science

High ROI Data Science, authored by Vin Vashishta, targets founders and top tech companies, delivering insights on AI strategy, product development, and technical deep dives. It addresses the implementation challenges and opportunities of AI, fostering a data-centric culture in businesses, career advancement in data science, and the transformative potential of generative AI.

AI Strategy and Implementation Data Science Career Growth Generative AI Applications AI and Automation in Business Data-Driven Decision Making Organizational Adaptation to AI AI in Retail and Customer Service Marketing Analytics Leadership and Management Trends AI Disruption and Competitiveness

The hottest Substack posts of High ROI Data Science

And their main takeaways

How Super Apps & Platforms Are Turning AI On Its Head

79 implied HN points • 30 Oct 24
🕹 Technology AI Platforms Apps Business Models Consumer behavior
  1. Super apps in Asia grow by offering many services to a smaller customer base, unlike Big Tech that focuses on single services for many users. This helps them cater better to local needs.
  2. The advantages of super apps include faster product development, lower costs for data collection, and a unique competitive edge through exclusive data. They can quickly adapt to market changes too.
  3. Wrtn, a South Korean startup, shows how a super app can combine multiple AI services into one platform. This model offers better value to users and keeps them engaged with ads instead of multiple expensive subscriptions.

How An Informed Minority Took Over The World’s Biggest Firms

119 implied HN points • 29 Oct 24
💼 Business Strategy Management Economics Consulting Information Systems
  1. Information asymmetry is when one group knows more than another. This can create unfair advantages in social systems and businesses.
  2. The Werewolf Game illustrates how a small, informed group can control the majority. This game teaches us about strategy and deception in group dynamics.
  3. To protect ourselves from manipulation, we need to build mental firewalls. Knowing about information asymmetry helps us fight back against unfair advantages.

Everyone Loves The Idea Of AI, But Not The Reality

615 implied HN points • 06 Oct 24
🕹 Technology AI Data Business Modeling Training Platforms
  1. Many businesses love the idea of AI but find it hard to put into practice. It often looks easy on paper, but the reality is very different when trying to make it work.
  2. Data is really important for AI to work well. Companies need good data to build effective AI products, and often, they realize this too late after facing challenges.
  3. AI projects often fail because businesses don’t fully understand what they need to achieve. Companies should focus on solving real problems rather than just using the latest technology.

Stealing AI: New Cyber Threats In The GenAI Era

79 implied HN points • 24 Oct 24
🕹 Technology Cybersecurity Data Privacy
  1. Human errors and social engineering are significant risks in cybersecurity, even with strong defenses. Phishing attacks are becoming more sophisticated and can catch businesses off guard.
  2. Businesses need a holistic approach to data and AI security instead of treating them as separate issues. Better collaboration across technical teams is crucial for effective risk management.
  3. Emerging threats like data poisoning in AI systems require constant vigilance. Preventative measures and strong recovery plans are essential to protect data integrity and ensure business continuity.

Will AI Take Your Job? Deconstructing Job Descriptions To Understand What Roles Are Most Vulnerable

297 implied HN points • 10 Oct 24
💼 Business Automation Employment AI Impact Market Trends
  1. Job descriptions might not fully show what a role truly involves, which can lead to misunderstandings about automation risks. Some essential skills of great workers aren't even mentioned.
  2. As AI improves, many tasks in roles like AI Product Manager and Java Developer could be automated. Workers need to consider upskilling if a large part of their job can be done by AI.
  3. Data scientists may face reduced demand as companies prefer to buy AI solutions instead of building them. They might need to shift focus to more customer-facing roles to stay relevant.
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8 Best Practices for AI Strategy Development By An AI—Strategist

158 implied HN points • 13 Oct 24
🕹 Technology AI Strategy Data science Innovation Digital Transformation Business Intelligence
  1. AI is changing how we think about technology, moving beyond just improving what we have to creating entirely new ways to interact with it. This means businesses need to look for big, new opportunities, not just small tweaks.
  2. Having a strong data strategy is key for successful AI projects. This involves treating data as an important asset, gathering context, and making sure it's easy to access for training AI models.
  3. It's important to develop real, functional AI products that deliver clear value. Companies should focus on creating products that solve specific customer problems rather than just showing off cool technology.

It's A New Year, But Businesses Are Still Baffled By The Same Old Problems With Data And AI

317 implied HN points • 15 Jan 24
🕹 Technology AI Data Business Digital Transformation
  1. CEOs face challenges with limited skills and expertise in implementing AI initiatives.
  2. Businesses struggle with data complexity and ethical concerns when it comes to utilizing AI.
  3. Companies need to align AI opportunities with business goals, estimate costs upfront, and prioritize continuous reskilling for successful AI implementation.

Who’s Hiring And Paying The Best For Data Roles?

257 implied HN points • 04 Feb 24
💼 Business Jobs Salaries Career Paths
  1. In times of economic uncertainty, it's crucial to work for companies that offer top compensation, interesting projects, and stability to excel in your career.
  2. Data analysts and mid-level data leaders are facing challenges with salary declines and shifting demands, necessitating reskilling into safer roles like data engineering or AI product management.
  3. Data engineers are still sought after, but the market is becoming more competitive, requiring advanced skills like handling streaming data. AI product managers are in high demand, with lucrative compensation up to $300K.

What Are Companies Doing With Generative AI? A Tale Of 3 Strategies.

297 implied HN points • 12 Jan 24
🕹 Technology AI Retail Data Strategy E-commerce
  1. Companies are using Generative AI tools to decrease training times and improve customer service in retail.
  2. Some companies are implementing Generative AI without a clear business problem statement, leading to undefined outcomes.
  3. Retailers like Walmart are strategically using Generative AI to change customer workflows, improve online shopping experiences, and increase revenue.

Long-Chain Marketing: How Data Engineering & Data Management Create Value For The Business

297 implied HN points • 10 Jan 24
💼 Business Data Engineering Data Management Value Creation Marketing
  1. Understanding the long-chain in marketing is crucial for connecting business outcomes with data and metrics.
  2. Data engineering and knowledge management are essential for transforming data into valuable assets that can be monetized by the business.
  3. Long-chain marketing involves seeing marketing efforts as part of a longer sequence of actions that lead to business outcomes, rather than standalone events.

What Does It Take To Create Longevity And Stay Relevant In The New Work World?

178 implied HN points • 23 Jan 24
💼 Business Career development Market Trends Skill development Productivity
  1. Success in the new work world requires being forward-looking and prescriptive, not just reacting to trends.
  2. Manufacturing luck involves positioning early in emerging trends to have more opportunities and be better prepared.
  3. To stay relevant, focus on upskilling in areas that align with future trends and combine vision, follow-through, and productivity.

What To Do When You're Stuck At A Business That Doesn't Care About Data Science

357 implied HN points • 27 Feb 23
💼 Business Data science Leadership Technical Debt Strategic Planning
  1. Many data scientists in companies that don't prioritize data science end up doing basic reporting and analytics.
  2. Technical management in such companies often lack the understanding and incentives to support data initiatives.
  3. Navigating a lack of data culture and strategy in a company requires significant effort but can lead to valuable career opportunities.