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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:30 - 21 Apr 2025 -
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by "Favja hah Nkaj" <3047892@gmail.com> - 02:17 - 20 Apr 2025 -
The week in charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:28 - 19 Apr 2025 -
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by "Bob" <Bob@styudongpack.com> - 01:27 - 19 Apr 2025 -
How to prepare your company for cascading risks
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of McKinsey Classics. We hope you find our perspectives useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
April 2025
In honor of the 60th birthday of McKinsey Quarterly, the McKinsey Classics newsletter is dedicating a full year to enduring Quarterly articles that were ahead of their time. Enjoy, and check out other celebration highlights such as special themed issues and bonus digital features.
Today’s organizations face no shortage of risks. But companies that think their problems are limited to the most immediate threats should think again. Indeed, risk managers often fail to anticipate how acute, obvious threats can multiply across the entire value chain and wreak even greater havoc.
Consider the global financial crisis of 2008, whose lessons, underscored in this 2009 McKinsey Quarterly classic, can be instructive for global companies today. In the wake of the 2008 crisis, Canada’s currency appreciated 30 percent in value versus the US dollar. Manufacturers in Canada knew the currency change would affect their cost competitiveness in US markets. But what they didn’t plan for was the flurry of cross-border shopping from Canadian consumers, who started purchasing big-ticket items in the United States, which forced Canadian companies to lower their prices at home. The combined effects of profit compression in both Canada and the United States did far more damage than these companies had initially anticipated.
Understanding how risk can cascade across a company’s value chain—including its suppliers, distribution channels, and customers—and how it might change the dynamic with competitors is crucial for developing a comprehensive risk plan. For more on how executives can better prepare for second-order effects, read Eric Lamarre’s 2009 classic, “Risk: Seeing around the corners.”Read our latest thinking on this classic topic
The six habits of highly successful chief risk officers
Anticipating and preparing for risks—especially hidden ones—is only part of implementing a successful risk assessment plan. To effectively embed future-ready resilience into the organization, risk leaders can follow the habits of successful chief risk officers, including relentlessly pursuing a guiding principle for risk and resilience and investing in the next generation of risk leaders. Read our latest insights on the topic from Ida Kristensen, Marc Chiapolino, María del Mar Martínez, and Ritesh Jain.
—Edited by Drew Holzfeind, editor, Chicago
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by "McKinsey Classics" <publishing@email.mckinsey.com> - 12:28 - 19 Apr 2025 -
EP159: The Data Engineering Roadmap
EP159: The Data Engineering Roadmap
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This week’s system design refresher:
The Data Engineering Roadmap
Popular interview question: What is the difference between Process and Thread?
What do version numbers mean?
How Transformers Architecture Works
Top YouTube Channels and Blogs for AI Learning in 2025
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Data engineering has become the backbone of effective data analysis. It involves managing, processing, and optimizing data to derive actionable insights.
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Programming Languages
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Learn batch processing tools like Spark and Hadoop and stream processing tools like Flink and Kafka.Databases
Focus on both relational and non-relational databases. Some examples are MySQL, Postgres, MongoDB, Cassandra, and Redis.Messaging Platforms
Master the use of platforms like Kafka, RabbitMQ, and Pulsar.Data Lakes and Warehouses
Learn about various data lake and warehousing solutions such as Snowflake, Hive, S3, Redshift, and Clickhouse. Also, learn about Normalization, Denormalization, and OLTP vs OLAP.Cloud Computing Platforms
Master the use of cloud platforms like AWS, Azure, Docker, and K8SStorage Systems
Learn about the key storage systems like S3, Azure Data Lake, and HDFSOrchestration Tools
Learn about orchestration tools like Airflow, Jenkins, and LuigiAutomation and Deployments
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Master the use of tools like Jupyter Notebooks, PowerBI, Tableau, and Plotty
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DORA, SPACE, and DevEx: Which framework should you use? (Sponsored)
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Read this guide for insight into:
Which developer productivity frameworks are widely used
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What to consider when selecting the right framework for your team or organization
Popular interview question: What is the difference between Process and Thread?
To better understand this question, let’s first take a look at what is a Program. A Program is an executable file containing a set of instructions and passively stored on disk. One program can have multiple processes. For example, the Chrome browser creates a different process for every single tab.
A Process means a program is in execution. When a program is loaded into the memory and becomes active, the program becomes a process. The process requires some essential resources such as registers, program counter, and stack.
A Thread is the smallest unit of execution within a process.
The following process explains the relationship between program, process, and thread.
The program contains a set of instructions.
The program is loaded into memory. It becomes one or more running processes.
When a process starts, it is assigned memory and resources. A process can have one or more threads. For example, in the Microsoft Word app, a thread might be responsible for spelling checking and the other thread for inserting text into the doc.
Main differences between process and thread:
Processes are usually independent, while threads exist as subsets of a process.
Each process has its own memory space. Threads that belong to the same process share the same memory.
A process is a heavyweight operation. It takes more time to create and terminate.
Context switching is more expensive between processes.
Inter-thread communication is faster for threads.
Over to you:
Some programming languages support coroutine. What is the difference between coroutine and thread?
How to list running processes in Linux?
What do version numbers mean?
Semantic Versioning (SemVer) is a versioning scheme for software that aims to convey meaning about the underlying changes in a release.
SemVer uses a three-part version number: MAJOR.MINOR.PATCH.
MAJOR version: Incremented when there are incompatible API changes.
MINOR version: Incremented when functionality is added in a backward-compatible manner.
PATCH version: Incremented when backward-compatible bug fixes are made.
Example Workflow
Initial Development Phase
Start with version 0.1.0.First Stable Release
Reach a stable release: 1.0.0.Subsequent Changes
Patch Release: A bug fix is needed for 1.0.0. Update to 1.0.1.Minor Release: A new, backward-compatible feature is added to 1.0.3. Update to 1.1.0.
Major Release: A significant change that is not backward-compatible is introduced in 1.2.2. Update to 2.0.0.
Special Versions and Pre-releases
Pre-release Versions: 1.0.0-alpha, 1.0.0-beta, 1.0.0-rc.1.
Build Metadata: 1.0.0+20130313144700.
Everyone talks about Transformers. How Transformers Architecture Works?
Transformers Architecture has become the foundation of some of the most popular LLMs including GPT, Gemini, Claude, DeepSeek, and Llama.
Here’s how it works:
A typical transformer-based model has two main parts: encoder and decoder. The encoder reads and understands the input. The decoder uses this understanding to generate the correct output.
In the first step (Input Embedding), each word is converted into a number (vector) representing its meaning.
Next, a pattern called Positional Encoding tells the model where each word is in the sentence. This is because the word order matters in a sentence. For example “the cat ate the fish” is different from “the fish ate the cat”.
Next is the Multi-Head Attention, which is the brain of the encoder. It allows the model to look at all words at once and determine which words are related. In the Add & Normalize phase, the model adds what it learned from attention back into the sentence.
The Feed Forward process adds extra depth to the understanding. The overall process is repeated multiple times so that the model can deeply understand the sentence.
After the encoder finishes, the decoder kicks into action. The output embedding converts each word in the expected output into numbers. To understand where each word should go, we add Positional Encoding.
The Masked Multi-Head Attention hides the future words so the model predicts only one word at a time.
The Multi-Head Attention phase aligns the right parts of the input with the right parts of the output. The decoder looks at both the input sentence and the words it has generated so far.
The Feed Forward applies more processing to make the final word choice better. The process is repeated several times to refine the results.
Once the decoder has predicted numbers for each word, it passes them through a Linear Layer to prepare for output. This layer maps the decoder’s output to a large set of possible words.
After the Linear Layer generates scores for each word, the Softmax layer converts those scores into probabilities. The word with the highest probability is chosen as the next word.
Finally, a human-readable sentence is generated.
Over to you: What else will you add to understand the Transformer Architecture?
Top YouTube Channels and Blogs for AI Learning in 2025
Some great YouTube Channels are:
Two Minute Papers
DeepLearning AI
Lex Fridman
3Blue1Brown
Andrej Karpathy
Sentdex
Matt Wolfe
Google YouTube Channel
Also, here are some great blogs focusing on AI:
TowardsDataScience
OpenAI Blog
MarkTechPost
DeepMind Blog
Anthropic Blog
Berkeley Bair
Huggingface Blog
Over to you: Which other channel and blog will you add to the list?
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 19 Apr 2025 -
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by "Connie" <Connie@xmetalmaterials.com> - 07:16 - 18 Apr 2025 -
Tools for the trade shock
The Shortlist
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Curated by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of the CEO Shortlist, a biweekly newsletter of our best ideas for the C-suite. This week, we dive into our latest thinking on tariffs and simmering trade tensions. You can reach us with thoughts, ideas, and tariff chat at Alex_Panas@mckinsey.com and Axel_Karlsson@mckinsey.com. Thank you, as ever.
—Alex and Axel
Now they’re here. Now they’re not. Leaders are grappling with day-to-day issues related to the rapidly changing global economic order. Understandably, they are hesitant to make strategic moves given the overwhelming uncertainty of the trade situation. Cindy Levy, Shubham Singhal, and their coauthors argue that traditional forecasting and planning methods are insufficient for this particular set of circumstances. What may help is to establish a geopolitical nerve center: a central hub that tracks, plans, and guides decision-makers in mitigating the impact of tariffs.
This flavor of uncertainty is new. But tariffs are old. Some say the Babylonian king Hammurabi was the first to enact tariffs on foreign goods, in his famous code. The Smoot-Hawley Act of 1930, and the trade war that ensued, has been more in the news lately. In this article from December 2024, our authors look back on the history of tariffs, examine some recent examples, and present a range of responses that executives can take to seize potential opportunities.
We hope you find these ideas novel and insightful. See you in a couple weeks with more McKinsey ideas for the CEO and others in the C-suite.Share these insights
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by "McKinsey CEO Shortlist" <publishing@email.mckinsey.com> - 04:26 - 18 Apr 2025
