Are Careers In Data In Trouble?

Описание: This week's episode is different. Instead of my usual off-the-cuff rambling, I'm sharing the full presentation I gave to the ETF Foundation about analytics careers - and I've got some uncomfortable truths to share.
The Hot Take: Despite analytics being well-paid, in-demand, and seemingly thriving, I believe it's actually under serious threat.

What We Cover:
Why data professionals are caught between two completely different worlds
85% of data projects fail for non-technical reasons
The 119 distinct tools you're somehow expected to know

Five Threats to Analytics Careers
Companies aren't seeing value from data (and why that's on us)
The 2022 wage boom that saturated the market with low-level talent
Why AI makes analytics look painfully slow
The perception problem: expensive but ineffective

How Data Actually Works
The real pipeline: extract, warehouse, model, report
The four layers of data modelling (and why modelling is everything)
Why "all-in-one" tools like Power BI and Azure Data Factory are dangerous

The Money: UK Salary Breakdown
2024 starter salaries by city
Why London has 10x the opportunities (but also 10x the competition)
Cost of living vs salary in London, Manchester, Edinburgh, Cardiff, Birmingham, Bristol

Five Bold Predictions for the Next 5 Years
Salaries will fall as SaaS tools improve
Greenfield sites will become rare
Hybrid roles will replace standalone analysts
Robust machine learning will become the norm
The fundamentals become non-negotiable

Career Advice That Actually Matters
Why understanding concepts is the most underrated skill
SQL and modelling: the only skills guaranteed to matter in 20 years
Why principles matter more than tools
How to structure your portfolio (and why earthquake data projects won't get you hired)

The "So What?" Game
My framework for connecting analysis to business impact
Why stakeholder management isn't a soft skill
How to bridge the gap between data and decisions

The Uncomfortable Truth
Analytics is being seen as expensive and ineffective. The way to counteract that isn't to learn everything about business AND everything about analytics. It's to go back to first principles:
What do they do to make money? How can data inform the things they can do to make more money?
If you focus on that, you'll be fine.

Mentioned in This Episode:
ETF Foundation - Supporting lecturers and higher educational professionals: https://www.et-foundation.co.uk/
173 Tech - The most impactful data agency (where I work): https://www.173tech.com/