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What is your next job? 💸- Mass Media vs. Audience of one 🏏- Musk finally did it 🐤- Sales & CSMs as 2 brothers 🎭- What is a great data scientist ? 🚀
“When in doubt simplify”, Eric Ries.
Episode #60. Hey Sunday lover!
This is Gilles from sunny Paris!
Some say Paris will soon be the global epicenter for tech 😎
Here are 5 new readings I picked for you this week that you might have missed:
What is the future of work really?
Is the future and audience of one?
Elon Musk finally did it (buying Twitter for $44bn),
Sales and CSM as 2 sides of the same coin?
What makes a great data scientist?
#1. The future of (your) work
TLDR From Digitally-native jobs, self-employment, and the antiwork movement, an article written by Rex Woodbury in his newsletter Digital Native published on April 27, 2022.
Do you know what "the future of work" is? really?
The reality is that it may encompass many things but it is undeniable that it created a suite of new tools that we are starting to use more and more at work: Monday, ProductBoard, Slack... The 'future of work' created an explosion of new saas tools for a better workplace.
"The future of work also encompasses how workers prepare for work, find work, and carry out that work."
The author states that the underlying trend is the disaggregation of work aka the rise of freelance workers. In 2027, America is expected to become a majority freelance economy for the first time ever.
The way you look at your income stream from one (and only) primary job should probably evolve. A recent study states that 70% of GenZ have some kind of second job.
"In 10, 20, 30 years, I expect the average worker’s income to look less like a river and more like a set of tributaries all contributing to a larger body of water. You might work at a startup by day, but run a YouTube channel by night."
#2. The audience of one (you)
TLDR From What happens when most media is produced for an audience of one?, an article published in The Diff on April 22, 2022.
Media has often been synonymous with trying to reach a large audience, the so-called 'mass media'. With the transformation of distribution and a game-changing shift to digital, addressing a small piece of the audience interested in a niche topic turns media into a 'mass audience ... of 1'. Niche content becomes king!
But is it really niche content or AI giving you (the reader) the feeling you are unique?
The way algorithms are trained on media you consume implies an automatic personalization of content that you are exposed to as a reader or viewer (you already experience this either on Kindle or Netflix).
The bias AI generates is nothing less than the fundamental question " is the job of an analyst to tell their manager what the manager wants to hear or what they need to hear? Ideally it's the latter".
"Which will end up leading to a situation where the only things you can trust are old books, new data, and perhaps your peers"; human-created content may well become the most valuable content in the end.
#3. Musk pockets Twitter
TLDR From Elon Musk buys Twitter, an article written by Casey Newton and published in The Platformer on April 26, 2022.
At last, something that started as a passive investment some days ago ended up with Twitter moving from being a public listed company to belonging to Elon Musk a private investor.
On the internal Slack channel of Twitter, the news triggered some mixed feelings. On the positive side some employees "like the fact that he wants to eliminate harmful bots and bring more clarity to how recommendation algorithms work."
About what will happen next at Twitter: "The $44 billion question, though, is … transform it into what?"
One hypothesis is that Twitter would stop its advertising business to move into a subscription business "by expanding its $2.99 Twitter Blue product or by scrapping it and building a new subscription product from scratch."
"Or Twitter could lean harder into becoming a decentralized protocol, selling API access to enable developers to build a variety of different front-end experiences. That could enable different users to choose different styles of content moderation, while effectively turning the core service into an enterprise software product."
Based on Musk's recent statement, it is likely he does not know yet what it will do with Twitter ... or does he?
#4. Sales and CSMs together much better
TLDR From CSM et Sales, deux frères, an article written by Aurore Lanchart and published on Medium on April 18, 2022.
Aurore is probably one of the best CSM leaders I have had the chance to meet and work with. I am sharing below 3 good tips to improve the customer-centricity of your organization:
1- Align Sales and CSMs incentives and bonuses so that they have the same goal of completing a successful trial period. This will avoid the sales team to sign customers with unrealistic expectations (or features you do not have).
2- Align your organization so that Sales and CSMs can work together as one. This will shorten the resolution time of issues when they come up. Both brains will make it easier to analyze the situation.
3- Have your sales and CSMs work together means using the same tools (ie. The same CRM). This is also important Sales and CSMs can sit together and work next to each other so that they understand each other interactions with the customer.
This may seem obvious but trust me; in most companies, it is not.
#5. What makes a great data scientist
TLDR From How to be a great data scientist, an article published on Medium on April 25, 2022.
When building a model with data. Beware of the quality of the data you use to train your model. « A machine learning model with defects in the data won’t necessarily fail or produce an error. You may just end up with a bad model. »
Understanding statistics and how/when to apply them to data science can be a great differentiator for data scientists.
“When in doubt simplify”, Eric Ries. A great model is able to have a great ROI. A truly great data scientist is able to solve valuable business problems in a way that will generate maximum, and in many cases, enduring value. To do this a data scientist needs to understand and make the rights trade-offs between complexity and simplicity. Is able to successfully deploy models in production and keep them stable and fresh once they are there. Whilst at the same time ensuring that these data science products are robust, high quality, and unbiased.
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See you next Sunday!