Previous Articles
Stratton's number and the pursuit of fame in data (Ned Stratton: 25th October 2024)
The Devops-ification of data analytics (Ned Stratton: 13th June 2024)
Data beyond AI: Microsoft Fabric vs Data Contracts (Ned Stratton: 30th January 2024)
No-code data part II (Ned Stratton: 22nd November 2023)
White Paper: The free-role data analyst (Ned Stratton: 4th September 2023)
Do data analysts need to read books? (Ned Stratton: 10th May 2023)
No code data tools: the complexity placebo (Ned Stratton: 17th March 2023)
The 2023 data job market with Jeremy Wyatt (Ned Stratton: 24th January 2023)
Making up the Numbers - When Data Analysts Go Rogue (Ned Stratton: 2nd December 2022)
Data in Politics Part 2 - Votesource (Ned Stratton: 12th September 2022)
Data in Politics Part 1 - MERLIN (Ned Stratton: 2nd September 2022)
Interview: Adrian Mitchell - Founder, Brijj.io (Ned Stratton: 28th June 2022)
The Joy of Clunky Data Analogies (Ned Stratton: 14th April 2022)
Event Review - SQLBits 2022, London (Ned Stratton: 17th March 2022)
Interview: Susan Walsh - The Classification Guru (Ned Stratton: 21st February 2022)
Upskilling as a data analyst - acquiring knowledge deep, broad and current (Ned Stratton: 31st January 2022)
Beyond SIC codes – web scraping and text mining at the heart of modern industry classification: An interview with Agent and Field's Matt Childs (Ned Stratton: 8th December 2021)
Debate: Should Data Analytics teams sit within Sales/marketing or IT? (Ned Stratton: 26th October 2021)
Event Review: Big Data LDN 2021 (Ned Stratton: 27th September 2021)
The Swiss Army Knife of Data - IT tricks for data analysts (Ned Stratton: 9th September 2021)
UK Google Trends - Politics, Porn and Pandemic (Ned Stratton: 15th October 2020)
How the UK broadcast media have misreported the data on COVID-19 (Ned Stratton: 7th October 2020)
The Power BI End Game: Part 3 – Cornering the BI market (Ned Stratton: 21st September 2020)
The Power BI End Game: Part 2 – Beyond SSAS/SSIS/SSRS (Ned Stratton: 28th August 2020)
The Power BI End Game: Part 1 – From Data Analyst to Insight Explorer (Ned Stratton: 14th August 2020)
Excel VBA in the modern business - the case for and against (Ned Stratton: 13th July 2020)
An epic fail with Python Text Analysis (Ned Stratton: 20th June 2020)
Track and Trace and The Political Spectrum of Data - Liberators vs Protectors (Ned Stratton: 12th June 2020)
Defining the role of a Data Analyst (Slawomir Laskowski: 31st May 2020)
The 7 Most Common Mistakes Made in Data Analysis (Slawomir Laskowski: 17th May 2020)
COVID-19 Mortality Rates - refining media claims with basic statistics (Ned Stratton: 10th May 2020)
Ned Stratton: 20th June 2025
In June 2015 – 10 years ago – I got my first job in "data". This is a ubiquitous catch all term for several roles, ranging from professional buyer of email addresses for spam marketing all the way to planet-brained data scientists responsible for driverless cars and Microsoft Copilot, which is pleading Clippy-style to write this blog for me.
I thought I'd share my journey – learnings, roles, enthusiasm curve, perspectives on how things have changed - one paragraph for each year. Let’s start in the golden, pre-Brexit, Power-BI-not-yet-released, is this dress blue or gold? year of our lord, 2015.
2015
Role: "Database Officer" at a medium-sized, "eccentrically managed" let's say, but fun and profitable events company. It was my job to shunt data from CRM to email service provider using Excel, with a bit of analytics on the side.
Skills: Excel VLOOKUPS and Pivot tables with two-line SQL queries in Access.
Enthusiasm curve: I had a vague sense of being on a good career path in an emerging job role, but I was in my mid-late twenties and mostly in love with fitness regime and teeth-straightening Invisalign process, with the aim of hitting on bridesmaids at the weddings in my 2016 social calendar.
Other remarks and perspectives: This was the very start of my journey. Nonetheless, I have one vivid memory from this time. This was being micromanaged in a piece of analysis I was doing for a charlatan marketer to prove his point, followed by the lightbulb moment that I was being asked to use statistics in a blatantly biased and dishonest way. Good to experience and process the emotions of that early on, as this situation will happen at least once to every data analyst.
2016
Role: "Data procurement and quality executive" at the same company. Peak buying email addresses from data brokers for desperate marketers.
Skills: More Excel and Access, but starting on VBA macros and SQL.
Enthusiasm curve: Still very vain and fitness obsessed, but starting to do data projects in my spare time, namely my Tinder-dating database in MySQL.
Other remarks and perspectives: By this point, I'd still barely scratched the surface of serious coding, relational database management theory, or statistics, but I was called a "data scientist" lazily for the first time (a theme I'll return to). More importantly, 18-months in I had acquired enough knowledge of the company's data and its business use that I was able to resign with a counter offer and leverage a pay rise, job title change, and "please don't leave us you're so important". This was an eye opener on the fleeting, quiet, but satisfying power that tech employees can (or could) wield in companies led by the opinions of salespeople.
2017
Role: "Data quality manager" at the same company. Still buying emails, but I'd normalised the marketing database and designed and implemented an industry-categorisation taxonomy for it.
Skills: Killing Excel VBA and writing SQL stored procedures.
Enthusiasm curve: High. I was super proud of reducing the marketing duplication rate from 25% to 0.5%, was going to regular meetups and had given a lightning talk to a data viz group about my Tinder dating database (sadly the video has been taken down), and did an evening course in relational database architecture in SQL Server. This blew my mind.
Other remarks and perspectives: I was starting to feel very devvy, and was getting ideas about actually being a data scientist. I was told by recruitment agents however that my arts/humanities background was a bit of a no-no, and that I needed to brush up on statistics. I did this and own at least 5 books on statistics now, but that advice turned out to be total bollocks.
2018
Role: "Database manager". Not just the quality of the database. The whole database.
Skills: Excel VBA, SQL, Python, and basic Power BI – so basic that I didn't even know what a measure was, but that didn't stop me from giving a 45-minute, keynote talk to a data meetup group on my Tinder dating database, which was now in Power BI.
Enthusiasm curve: Loving it. I was managing the hell out of the quality of the database (duplication still at 0.5%, job function taxonomy designed and implemented), doing talks, and doing a data science immersive in my spare time. The rude-math of this was a bit of a stretch of my humanities background, but I was still scraping data from football sites in Python and running logistical regression algorithms on it to place bets. For the second time in my career, I was described as a data scientist. Not for any of the above, but for the database job function taxonomy, which was basically WHERE JobTitle LIKE '%manager%' in SQL.
Other remarks and perspectives: GDPR – which would put the citizen's rights at the heart of data management and put data brokers and spam emailers out of business with record Euro fines – came and went. Its legacy is that we now have those annoying cookie acceptance pop ups every time we go to a website.
2019
Role: New job as a data analyst in finance for a challenger bank…with a proper data warehouse.
Skills: SQL like never before (embedding HTML as string variables and using it to send COLOUR FORMATTED EMAILS FROM THE DATABASE OMG!), Power BI, Python
Enthusiasm curve: Peak. With the new stuff I was learning at my place, I thought I was cross-industry data specialist and set up my own website and blog – this one.
Other remarks and perspectives: 2019 was the zenith of engaging and well-attended data meetups and hackathons in London. The pandemic the following year would kill this stone dead, and writing this sentence genuinely makes me sad.
2020
Role: Same.
Skills: SQL/Power BI/Python, but now dipping my toes into HTML/CSS.
Enthusiasm curve: Still keen to learn and produce content (blogs + data viz and an online quiz built in connected HTML forms and PHP), but COVID-forced online meetups and talks were a poor imitation of the fun I had at them in 2019. I got back into running, fitness, and reading polemics and political theory books.
Other remarks and perspectives: This was the year of COVID and the new phenomenon of WFH. I was poleaxed by this, and the fact it was so popular in the company I was working at and in my team. Not realising this was a wider and persistent nascent trend, I took my frustrations out on the company and jumped ship with unfinished business and plenty still to learn. This was also the year I learned what my kryptonite was as a data analyst – web-event analytics dashboards with wide scopes and nebulous purpose. It's the combination of bottomless unstructured data and needing to build a dashboard for a dashboard's sake that kills one's motivation to start work before 2 in the afternoon.
2021
Role: Senior data analyst at a large, multi-national events and publishing company.
Skills: Same as before but getting good at APIs and working in no-code blending tools.
Enthusiasm curve: Mixed level of enthusiasm. There was an internal political battle in the new company I'd joined between Power BI and Qlik which forced me back into Excel, plus general lack of role definition and sense that I was a goon who was there to validate the investment in an obscure no-code blending tool rather than genuinely provide valuable insight, all made me feel for the first time that I could be a classic hybrid of Duct Taper and Flunky from David Graeber's "Bullshit Jobs". However, 2021 was the year that I really got stuck into APIs, web scraping, and Power BI to build 2 of my best dashboards on UCOVI – PMQs and Timeline of a Pandemic online.
Other remarks and perspectives: My lightbulb moment of 2021 came at BigDataLDN in September – the first big expo back after COVID restrictions were lifted. It was about the over-saturation and general irrationality of the data technology supply market and how things were going no-code.
2022
Role: Same as 2021.
Skills: PowerShell scripting was the main addition of 2022, alongside keeping the web languages (HTML/CSS/JS) ticking along nicely with some satisfying grid effects on key UCOVI pages. I was maintaining rather than improving my core trio of SQL/Power BI/Python.
Enthusiasm curve: I was getting itchy feet at the multi-national publishing and events company. Not due just to the neggy David Graeber vibes from 2021, but also to new gripes about failed warehousing and integration projects upstream of my role, and the to the return of my old nemesis in the form of a submarine web analytics reporting project involving meandering daily meetings and disinterested business stakeholders. Receiving my third misdirected "data scientist" compliment (for an Excel VBA macro to merge CSVs) also cemented in me the idea that I wasn't getting great technical direction.
A budding enjoyment of acrylic painting was also starting to compete for my spare time against above-and-beyond technical learning.
On the bright side, I was still keeno enough to add consistent blog and new Power BI infographic content to UCOVI, which I was announcing to my LinkedIn network through a monthly newsletter that was read, liked, and complimented enough by colleagues for its comic elements to send my dopamine soaring. Face-to-face data meetups in London were also making something of a sustained resurgence as COVID receded. It was the fusion of these two silver linings in late 2022 that led me my next role, as I met with a Power BI consultant on a Tuesday evening meetup. He was looking to scale up, and impressed enough with my UCOVI content to take me on. It was heartening to know that my professional network and online portfolio could keep my career moving on in what was then a buoyant, pre-tech-layoffs/Liz Truss job market.
2023
Role: Senior Data Analyst and BI Developer for boutique Microsoft Data Solutions Consultancy.
Skills: PowerShell, SQL, Python, Power BI, whilst making a start on C# development.
Enthusiasm curve: On the back of feedback about UCOVI and my LinkedIn newsletter writing, I was hoping to make 2023 the year I became a big player or influencer in my data field of excellence. I tried to obtain this by doing talks at expos, conferences and meetups (I managed 7 in total), and coming up with a big idea, which was the free-role data analyst.
Sadly, this didn't come to pass. Despite carving out time to do this, the transition from FTSE-250 company to boutique consultancy diminished my online presence somewhat, which was far more reliant than I'd expected on my network at my old company. I was also starting to find the idiosyncracy of being mid-stream technical grating after 8 years - data people are too techy to be seen as business stakeholders but not techy enough to get computer admin rights – and began to watch Youtube videos advising that data analytics should be seen as a transition career into software engineering. This awakened the putative interest in C# development.
Other remarks and perspectives: Other vibe-kills of 2023 were the rise of ChatGPT and AI, drowning out more interesting and unresolved conversations about data analytics, and the Microsoft's fanfare launch and subsequent ramming-down-throat of Fabric, which might be good in 3-5 years.
2024
Role: Same as 2023 with an end-of-year lateral move to a mid-size FinTech offering more money.
Skills: More of the same.
Enthusiasm curve: C# took hold of my spare-time learning in 2024, ending up in a capstone project password manager app in .NET (yielding 4 LinkedIn likes and no active users – click here to be the first). Seeing my blog style getting snarkier (comparing devops-obsessed data folk to needy zebras impersonating giraffes) and realising I was probably too late in the day to transition to employed software development (AI), I had a mid-year evaluation of what my career in data was actually FOR. Well, "evaluation" is a touch grandiose - it was a split-second realisation prompted by a recruiter LinkedIn DM. Money. An interview process followed, then an offer to do the same work for another £25k a year. When climate change makes a marsh of Britain and the machines enslave the human race, I'd like to say I was paying comfortably in the 40% tax bracket.
2025
Role: Same as 2024 but more full on and enough late nights writing lengthy SQL to stop me spending my pay rise in Clapham gastro pubs (which I am too old for anyway).
Skills: I've suddenly become much more enthusiastic about data again, but out of necessity. The new company doesn't give me a second to sit back and pontificate about the software dev career that might have been, and I need to crank out my best DAX on a daily basis to keep on top of deadlines. But since it's growing, useful, and innovative (a SaaS platform for global financial regulations and news), and preferring to be busy not bored, I've fully embraced being a cog in a top-speed machine. I'm also so sick of data by Friday evenings that I devote my weekends to the people and hobbies that I love – namely acrylic painting, which is getting me Insta likes to replace the dwindling LinkedIn engagement for my blog posts, and yielding a new piece of passable art to hang in my home office every few months.
Other remarks and perspectives: It's not just me: burnout in the data community is widespread this year. Three manifestations of this have been "weekly" blogs becoming quarterly, people posting on LinkedIn saying that deriving personal identity from their data careers and skill specialisms isn't doing it for them anymore, and a supressed, transactional, "free pizza, watch talk, train home " vibe at formerly thriving London-based meetups (not to do down for one second the stirling efforts of speakers and organisers). This last one hasn't been helped my Microsoft's tea-total new head office in Paddington, which has the security paraphernalia and overall charm of an American super-max jail. The houseplants and transparent lifts are fooling no-one.