Rise of Analytics

Why is modern analytics so successful?

1. More data
2. More computing power
3. New algorithms
4. New analytics processes

More data for the analysis

The explosion of data

Unit	Equivalent	Approximate meaning
Gigabyte (GB)		A HD movie file or a few hundred photos
Terabyte (TB)	1,000 GB	Storage of a modern laptop or external drive
Petabyte (PB)	1,000 TB	Data of a large company or several large data centers
Exabyte (EB)	1,000 PB	Roughly the yearly internet traffic of a small country
Zettabyte (ZB)	1,000 EB	≈ 1 trillion gigabytes; global data creation scale

Data production

Enterprise and transactional data
Enterprise systems such as ERP, CRM, and supply chain platforms generate large volumes of structured data through everyday business transactions.

E-commerce
Every search, click, purchase, and review creates behavioral and transactional data used for recommendations and personalized marketing.

Social media and user-generated content
Platforms such as TikTok, YouTube, and Instagram generate enormous data volumes through uploads, interactions, and live-streaming.

IoT and smart devices
Connected devices—from wearables to industrial sensors—continuously produce real-time data across interconnected systems.

Digital transactions
Online banking, mobile payments, and blockchain systems generate detailed financial records for transactions and security monitoring.

AI-generated data
Machine learning and generative AI create large datasets during training and operation, further accelerating global data growth.

Computing power

Growth of computing power

The rapid acceleration of computing power—driven by advances in hardware, cloud infrastructure, and parallel processing—has enabled modern analytics and machine learning to scale to massive datasets.

Evolution of computing power

The growth of modern analytics is enabled by changing strategies for increasing computing power.

Era	Main strategy	Explanation
1970s–2000s (*)	Moore’s Law & miniaturization	Smaller transistors → more components per chip → faster processors
2005–today	Parallel computing	Performance increases by using multiple processors simultaneously
2010s–today	Specialized hardware	Chips optimized for specific workloads (GPUs, TPUs, AI accelerators)
Emerging	New computing paradigms	Alternative computing models such as quantum computing

(*) Miniaturization continues today, but progress has slowed as transistors approach physical limits. Advances such as extreme ultraviolet (EUV) lithography are required to continue scaling. See how $400 million machines are constructed to build modern chips: https://www.youtube.com/watch?v=MiUHjLxm3V0.

New algorithms

Algorithms

An algorithm is a step-by-step procedure for performing a computation and thereby solving a problem.

Algorithms determine how efficiently computers can process data and solve tasks.

Examples:

• Linux Scheduling Algorithms (1990s–present) — Efficient process scheduling enabling operating systems to run tasks concurrently
• RSA Encryption (1977) — Public-key cryptography algorithm enabling secure internet communication
• PageRank (1998) — Algorithm ranking webpages based on link structure, enabling scalable web search
• Blockchain (2008) — Distributed consensus algorithm enabling decentralized digital ledgers and cryptocurrencies
• Deep Learning (2010s) — Improved neural network training enabling major advances in vision, speech, and language AI
• Gradient Boosting (2014) — High-performance ensemble learning algorithm widely used in predictive analytics and data science
• GPT / Transformer Models (2017–present) — Transformer architecture enabling large language models and generative AI

Learning note

No need to memorize everything.
Be able to give a few illustrative examples.
This also applies to the data production areas.

Algorithms enable more powerful analytical models

Traditional Regression

Decision Tree

Neural Network

Algorithmic breakthroughs drive AI progress

Recent breakthroughs in artificial intelligence (AI)¹ show how new algorithms can rapidly surpass human performance. DeepMind provides good examples.

AlphaGo (2016)

• Uses deep neural networks + reinforcement learning
• Defeated world champion Lee Sedol in the game of Go

Go was long considered too complex for computers due to the enormous search space.

AlphaFold (2020–2022)

• Uses deep learning to predict protein structures
• Achieved breakthrough performance in the CASP competition²

Predicting protein folding had been a major unsolved problem in biology for decades.

1. AI systems are based on algorithms; in the following, we use the broader term AI when referring to such algorithm-based systems.

2. Hassabis and colleagues were awarded the 2024 Nobel Prize in Chemistry, in part for the development of AlphaFold.

Jagged frontier of AI

AI progress often occurs through algorithmic breakthroughs, enabling machines to outperform humans in increasingly complex tasks.

Recent studies suggest that AI creates a “jagged frontier.” [@DellAcquaEtAl2023], i.e., some tasks are well suited to AI, while others that appear similar remain outside its capabilities.

• Within this frontier, AI can strongly enhance knowledge work, improving productivity and the quality of outputs.
• Outside the frontier, AI can reduce performance, especially when users rely too heavily on its outputs without verification.

Figure based on the work of @HendrycksEtAl2025 on Artificial General Intelligence.

Analytics processes

Maturing analytical capabilities

Descriptive analytics: What happened?

• Summarizes historical data to understand patterns and trends.
  
• Example: Sales reports, dashboards, KPIs

Predictive analytics: What will happen?

• Uses statistical models and machine learning to forecast future outcomes.
  
• Example: Demand forecasting, churn prediction

Prescriptive analytics: What should we do?

• Recommends actions based on predictions and optimization.
  
• Example: Pricing optimization, recommendation systems