Welcoem to posts!!
in the future - u will be able to do some more stuff here,,,!! like pat catgirl- i mean um yeah... for now u can only see others's posts :c
If I say, "Users clicked 10% less in the "Book Now" button compared to the same period last year",
Is this an observation, an insight, or a recommendation?
Most people create projects that provide observations and insights but no recommendations.
The best way I can explain the difference is to transform these terms into questions.
If you have any doubts about your current actions, you can always refer to these questions:
1. What happened?
2. Why did it happen?
3. What should we do?
What happened - Observation
Observations are facts or findings you get directly from data gathering or research.
They show what has been seen, measured, or recorded without interpretation.
They are objective and the foundation for further analysis.
Example: "Users clicked 10% less in the "Book Now" button compared to the same period last year."
You must add the proper context when you're collecting observations.
That's the only way to connect them to the next step.
Why did it happen - Insights
Insights are interpretations or points drawn from analyzing multiple observations.
(You cannot solely rely on one data point to get an insight).
Think of insights as a spiderweb, where observations flow to the most logical explanation.
Example: "Users skipped the 'Book Now' button because they prioritize credibility over convenience and prefer speaking to an advisor before booking."
Insights explain "why" something is happening and provide further context or patterns.
What should we do? - Recommendations
Recommendations are actionable steps based on insights to address observations.
They say what should be done to improve processes.
They are practical and specific.
They come from a logical perspective that follows insights.
Example: "Redesign the webpage to improve credibility:
- Customer testimonials;
- Visible contact number for phone bookings".
As you can see, you cannot drive recommendations without insights, and you cannot have insights without observations.
Use them all and make your projects even better.
Share with others if you found this helpful.
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Some great advice from Company of One by Paul Jarvis: "If you aren't embarrassed by the first version of your product, you've launched too late."
I know the book is mainly for people who want to be solopreneurs, but almost all of the teachings in the book can be transferred to people who want to succeed in their 9-5 corporate jobs.
Instead of forever working on the idea and talking about theories and implementation, how about you just go ahead and build a minimum viable product for whatever it is you're working on?
I've said this a million times if not more; those who can talk the talk and walk the walk (those who can do the thinking and have the ability to build actual solutions) are destined to succeed.
Is there something you built previously that you refined so much by now that you're a little bit / very embarrassed by the first version?
If yes, that's great! I would love to hear it. Please share below.
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Look beyond “Data Analyst” roles:
- Business Intelligence Analyst
- Data Business Analyst
- Business Intelligence Developer
- Insights Analyst
- Financial Analyst
- Regulatory Reporting Analyst
- Marketing Analyst
- Product Analyst
- Healthcare analyst
- Substantiation Analyst
- Operations Analyst
- Data Delivery Analyst
- Insights and Analytics Analyst
- Research analyst
- Business Process Analyst
- Risk Analyst
- Quality Analyst
All of them relate to data.
Let me know in the comments which ones you would add to this list.
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See if you notice any difference:
Identification
- Analysis of UK population trends over the next 30 years, focusing on demographic changes and fertility rates.
Hypothesis/Assumption
- Analysis showing how the UK population would decline 14% over the next 30 years if fertility rates keep the same pattern.
Which project would you open first if you were a recruiter?
Now, look at your projects and see where they fit.
Change them accordingly.
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Having technical skills without domain knowledge is like a doctor buying surgical tools before understanding anatomy.
There's a reason why so many people "transition" into data analytics from their current roles.
If you don't have ANY domain knowledge, learn EVERYTHING inside here before you even start with Excel or SQL.
Choose a specific domain and study it.
It's not what you want to hear, but it's what you have to do if you want to succeed in Data.
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I see countless qualified professionals struggling in today's job market.
From laid-off tech workers and data professionals, to career switchers seeking new opportunities, to people stuck in jobs they hate, to those worried about future layoffs, to ambitious professionals ready for their next big career leap.
The traditional approach isn't working anymore.
When even those with master's degrees and years of experience can't break through, it's time to reflect on what you must do and approach this situation with a proven strategy.
Getting ghosted by employers, fighting against AI screening systems, and competing with thousands of other people is not your fault.
But not having a strategy and just applying to 1,000+ jobs, hoping that recruiters will specifically select you over everyone else, is.
Here's what you need to do:
- Take advantage of LinkedIn to attract recruiters, create your own opportunities, and develop your professional brand;
- Create and test multiple versions of your resume;
- Build a portfolio that shows you can handle what's required in the job descriptions;
- Track and analyze your job applications while setting REALISTIC expectations for response rates;
- Implement a proven job application strategy — NOT the easy apply option;
- Build meaningful professional connections in your industry.
STOP BEING JUST ANOTHER APPLICATION IN THE STACK.
Use this code on my new JOB APPLICATION ACCELERATOR and see what happens mochen.info/product/job-application-accelerator/
startyourtransformation
P.S → It will only work for the first 5 people.
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A few days ago, I painted a room in my house.
I didn't like doing it and it made me very tired.
So I did what I always do when this happens—I tried to find a logical solution.
How much would it cost to get someone else to do this?
After looking it up, I found the price.
Then, I asked myself:
Could I make enough money to pay someone to do the work?
The answer was "yes".
After this, I had two choices:
- Do it myself and feel unhappy;
- Pay someone else to do it.
That's a simple process I always use when dealing with things I don't like to do.
Instead of just saying "I don't want to do this," I find better ways to handle it.
That's also my philosophy as a data mentor.
I don't want people to follow my advice simply because "I said so."
I want them to think and develop a problem-solving mindset.
If you've been following me for a while, I'm curious:
Why did you start following my content? Was it the logical next step?
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"Just one more tutorial and I'll be ready."
Guided projects are learning experiences designed to help you apply your theoretical knowledge.
Here’s the basic structure:
- The project creator starts by addressing the main problem.
- They guide you through a series of logical small tasks that achieve the solution to that problem.
- You get to SEE the entire process, which helps connect what you learn in theory.
You don't have to guess the outcome.
The system guides you to the most logical answer.
But if you're not careful:
- You become dependent on step-by-step guidance;
- You don't develop critical thinking and problem-solving skills;
- You can't start projects from scratch.
A quick solution for these problems is to stop thinking about the "how-to" and focus on the "why".
Specifically, two "why's"
- Why are they taking this step?
- Why does it matter?
It will force you to look for answers outside the tutorial, which makes the learning process ACTIVE.
You'll notice a shift in the way you learn that is much more effective.
Have you been stuck in "guided-projects dependency"?:
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I'm trying to come up with the title for one of my next videos. Could I ask which one you prefer?
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This is what might happen if you start reaching out to recruiters and expanding your network with other data professionals:
- A lot of ghosting;
- A list of interesting people to connect with and learn more;
- A chance to land a job.
This is what might happen if you DON’T start reaching out to recruiters and expanding your network with other data professionals:
- A lot of thinking: “Is this for me?”;
- Getting passed up for opportunities;
- Not landing your job in the data domain.
You choose.
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I'M HERE TO HELP YOU LAND YOUR NEXT TECH & DATA JOB
🌐 My website: mochen.info/
LET'S COLLABORATE
🤝 Sponsorships & long-term partnerships: mo-chen.notion.site/Partnerships-19af8411d7208065b…
ABOUT ME
I'm Mo and I work as a data analytics manager / content creator. I make videos about how best to get your next job in the tech & data space; whether you're a beginner, career switcher, or someone looking to take their tech & data career to the next level.