223: Data Science Trends for 2019

Episode Summary

Executive Summary: Kirill Aram and Adlan de Pontoves review their 2018 tech predictions, score themselves on accuracy, and present eight key 2019 trends for data science and AI. The episode emphasizes career relevance, highlighting AI, IoT, cloud, NLP, explainable AI, RPA, data literacy, and data storytelling as the most important areas for professionals to follow.

Main Topics: Review of 2018 prediction accuracy: The hosts revisit eight predictions made for 2018 and assess which trends materialized, using this as a framework to justify their forecasting approach and career guidance. AI and hybrid intelligence systems as the biggest 2019 trend: They identify hybrid intelligence systems optimized by deep neuroevolution as the central AI breakthrough for 2019, arguing that combining multiple AI models is the next step beyond single-model improvement. Data science career direction and data literacy: The episode stresses that trends matter because professionals need to learn the right skills, support citizen data scientists, and help organizations become more data-driven. Cloud computing and movement to scalable infrastructure: The hosts argue that cloud adoption is now the norm, driven by efficiency, cost, and scalability, with AWS highlighted as the strongest AI cloud platform. Internet of Things, digital twins, and connected systems: They position IoT as a massive growth area that combines AI, blockchain, sensors, robotics, and embedded systems, with digital twins as a major enabling technology. Explainable AI, cybersecurity, and trust: The discussion links growing cybersecurity threats and governance concerns to a need for explainable AI, transparency, and better control over black-box systems. Automation, NLP, and data storytelling: RPA, NLP, and visualization/data storytelling are presented as fast-growing areas that will automate repetitive work while increasing the need for humans to communicate insights clearly.

Key Arguments: AI adoption is no longer speculative; businesses and governments are already investing heavily and expect operational and model-level change. Blockchain interest fell with the crypto downturn, but the hosts believe the broader technology still has long-term value beyond Bitcoin. Cybersecurity demand is rising because breaches and regulations like GDPR have made data protection unavoidable. Deep learning is becoming mainstream, and the key breakthrough is no longer just better models but better model architectures and hybrid systems. Big data tools like Hadoop were predicted too early, while cloud platforms automated much of the infrastructure that used to require manual big-data engineering. IoT is framed as the convergence point of AI, blockchain, sensors, robotics, and embedded systems, making it a high-opportunity but multidisciplinary field. Explainable AI will matter because organizations, executives, and governments need to understand how models make decisions. RPA will automate repetitive white-collar tasks and become a practical entry point for data/process automation. Data storytelling and visualization will become more valuable as analytics becomes more widespread and decision-makers need understandable narratives. NLP will continue expanding because assistants, chatbots, and customer experience systems depend on it.

Data Points: Webinar episode length: Almost 2 hours - The hosts note the episode is long because it was originally recorded as a webinar. Countries represented in audience: Many countries worldwide - Live audience joined from places including India, Sweden, USA, Chile, Australia, Colombia, Canada, Tunisia, Romania, Greece, Malaysia, Japan, China, Mexico, Singapore, Taiwan, and others. 2018 prediction accuracy: 6 out of 8 (75%) - The hosts review their eight 2018 predictions and state they got six correct. Organizations expecting AI-driven business model changes: 58% - Used to support the claim that AI is affecting business strategy. Organizations expecting new AI business value: 91% - Used to show broad confidence in AI value creation over the next five years. Organizations expecting indirect AI benefits: 81% - Cited as evidence that even passive organizations anticipate AI-related gains. Blockchain interest decline: 65% drop - Measured against December 2017 interest levels. Bitcoin interest decline: 80% drop - Used to explain the blockchain hype decline. Cybersecurity interest growth: 61% increase in 2018 - Google Trends-style interest increase cited for cybersecurity. Google research papers in 2018: 565 papers total; approximately 400 in ML/deep learning - Used to illustrate mainstream R&D investment in deep learning. AR/VR product and service spending: $27 billion in 2018 - Referenced as a 92% year-over-year increase. Organizations using or planning digital twins: 48% - Organizations implementing IoT that already use or plan to use digital twins. Digital twin market growth: 38% annually to $16 billion by 2023 - Deloitte forecast cited for digital twins. Public cloud services market: $206.2 billion in 2019 - Projected from $175.8 billion in the current year, a 17.3% increase. Cloud market growth rate: 17.3% - Projected year-over-year growth for public cloud services. IoT spending forecast: $1.2 trillion by 2022 - The largest projected growth area discussed in the episode. IoT CAGR: 13.6% (2018-2022) - Forecast growth rate for Internet of Things spending. RPA market size: $3.1 billion by 2025 - Grand View Research estimate cited for RPA. RPA CAGR: 31.1% - Projected compound annual growth rate for RPA through 2025. NLP market size: $16.07 billion by 2021 - Growth estimate from 2016 base of $7.63 billion. NLP CAGR: 16.1% - Projected annual growth rate for NLP market. Big data/Hadoop analytics market: $7.7 billion in 2018 - Compared with $8.1 billion in 2017, indicating a slowdown. Citizen data scientist automation: More than 40% of data science tasks automated by 2020 - Gartner prediction used to support data literacy and self-serve analytics.

Pivotal Quotes: "AI is the new electricity" — Kirill Aram (citing Andrew Ng): Used as the central framing for why AI adoption is a lasting business trend. "The big trend for twenty nineteen is going to be the hybrid intelligence systems optimized by deep neuroevolution." — Adlan de Pontoves: Announcement of the hosts’ top 2019 AI prediction. "The cloud is not hype. Cloud is the new norm." — Kirill Aram: Used to explain that cloud adoption has moved from buzzword to infrastructure standard.

Implications: Listeners should focus on AI, IoT, cloud, NLP, explainability, and automation, while building communication and data-literacy skills. The biggest career advantage will come from learning technologies that are converging into hybrid systems and helping organizations apply them responsibly.