119: Data Science Trends for 2018

Episode Summary

Executive Summary: The episode surveys 2018 data science trends, centering on AI’s expanding role in business, the rise of blockchain, growing emphasis on security, the mainstreaming of deep learning, big data infrastructure, and self-serve analytics. The hosts argue that AI and blockchain will reshape operations, decision-making, and trust, while data science becomes more democratized through tools, teams, and executive buy-in.

Main Topics: AI as a business foundation: The discussion frames AI as the core enabler of modern businesses, used for optimization, decision-making, and specialized applications like NLP, computer vision, robotics, and automated data systems. Blockchain and decentralization: Blockchain is presented as a disruptive technology beyond Bitcoin, with potential in voting, media distribution, and secure decentralized systems, especially when combined with AI. Security and cyber resilience: The hosts highlight major 2017 breaches as evidence that data security will be a dominant 2018 priority, with blockchain and AI expected to strengthen protection and detection. Deep learning goes mainstream: Deep learning is gaining adoption because of open-source tooling, more practical applications, and democratized access, even though traditional models still dominate many company use cases. Big data platforms and cloud adoption: Hadoop, Spark, Scala, and cloud infrastructure are discussed as essential responses to growing data volumes and the need for scalable, faster analytics pipelines. Self-serve analytics and data democratization: The episode argues that organizations increasingly want non-data-scientists to access and use data directly through automated analytics tools and dashboards. Data science teams and executive education: Rather than relying on lone experts, companies are expected to build collaborative data science teams and educate executives so strategy and decisions are better informed by data.

Key Arguments: AI adoption is already underway in many firms, mostly through narrow applications that improve operations, reduce costs, and support executive decision-making. Blockchain is more than cryptocurrency; its decentralized, tamper-resistant architecture makes it suitable for secure voting, media licensing, and other trusted transactions. Security will remain a major trend because expanding data exposure and increasingly sophisticated attacks make breaches more likely and more costly. Deep learning is becoming accessible due to open-source ecosystems and easier implementation, even though simpler models remain more common for many business tasks. Big data technologies are growing because the volume and complexity of data require distributed systems that can store, process, and analyze information efficiently. Cloud infrastructure is preferred because it scales faster, lowers hardware costs, and reduces maintenance burdens compared with on-premises systems. Self-serve analytics will expand as organizations realize data science cannot remain centralized in a few specialists, but human oversight will still be necessary. Companies will move from hiring isolated data science heroes to building teams and educating executives, creating a broader data culture and better strategic alignment.

Data Points: Companies already integrating AI: around 60% - Adlan cites Gartner-style data indicating many companies have already integrated AI into business processes. Companies starting AI adoption: around 30% to 40% - Firms are in the planning stage but have not yet fully implemented AI. Organizations building enterprise AI strategies: 59% - Gartner statistic shared near the end of the discussion about AI strategy maturity. Organizations that have already made the plunge into AI strategy: 41% - Complementary Gartner statistic indicating a substantial minority are already implementing AI strategy. Deep learning course enrollments: 65,000 students - Used as evidence of rapid market interest in deep learning since the course launch. Deep learning course timeframe: 9 months - Enrollment figure is contextualized by the period since the course was released. Monthly course sign-ups: about 7,000 per month - Derived from 65,000 enrollments over nine months to illustrate demand. WannaCry cyberattack period: May 2017 - Referenced as a major security breach driving increased concern about cybersecurity. Equifax breach disclosure date: 7th of September 2017 - The date Equifax announced its massive data breach. Equifax affected customers: 143 million - Number of people impacted by the Equifax data breach. Equifax total customers: about 800 million - Describes the scale of the credit agency mentioned in the security discussion. US population referenced: 324 million - Used to underscore the scale of the Equifax breach relative to the US population.

Pivotal Quotes: "AI is the new electricity" — Kirill (attributed to Andrew Ng): Introduces the idea that AI will become a foundational utility for businesses. "Blockchain will play a significant role in that because since everything is decentralized and since everything is cryptid and since everything is well organized into flows that are in such a way that you cannot go back in the flow and modify anything, well, that makes it a super solid, safe, and fast system." — Adlan: Explains why blockchain is seen as transformative for secure, efficient systems. "We will always need some people to have a complementary job on that because the machines cannot do everything." — Adlan: On self-serve analytics and automation, emphasizing the continued need for human oversight.

Implications: Listeners should expect faster AI adoption, stronger demand for security and blockchain skills, and more democratized analytics. For careers, this means learning both tools and business context, while organizations must invest in data strategy, teams, and executive literacy.