The Agile Phone Line is Cracking Up: Is it Time to Hang Up?

Ah, March 3rd, 1876. A momentous date indeed, when Alexander Graham Bell first summoned Mr. Watson through the magic of the telephone. A groundbreaking invention that revolutionized communication and paved the way for countless innovations to come. But amidst our celebration of this technological milestone, let’s turn our attention to a more recent communication phenomenon: Agile.

Agile, that wondrous methodology that promised to streamline software development and banish the demons of waterfall projects, has become as ubiquitous as the telephone itself. Stand-up meetings, sprints, and scrum masters are now the lingua franca of the tech world, a symphony of buzzwords and acronyms that echo through the halls of countless software companies. But as we reflect on the legacy of the telephone and its evolution, perhaps it’s time to ask ourselves: Is Agile starting to sound a bit like a dial-up modem in an age of broadband?

Remember Skype? That once-beloved platform that connected us across continents, now destined for the digital graveyard on May 5th. Skype, like Agile, was once a revolutionary tool, but time and technology march on. Newer, shinier platforms have emerged, offering more features, better integration, and a smoother user experience. Could the same fate await Agile? With the rise of AI, machine learning, and automation, are we approaching a point where the Agile methodology, with its emphasis on human interaction and iterative development, becomes obsolete?

Perhaps the Agile zealots will scoff at such a notion, clinging to their scrum boards and burn-down charts like a security blanket. But the writing may be on the wall. As AI takes on more complex tasks and automation streamlines workflows, the need for constant human intervention and feedback loops might diminish. The Agile circus, with its daily stand-ups and endless retrospectives, could become a relic of a bygone era, a quaint reminder of a time when humans were still the dominant force in software development.

And speaking of communication, who could forget the ubiquitous “mute button” phenomenon? That awkward silence followed by a chorus of “You’re on mute!” has become a staple of virtual meetings, a testament to our collective struggle to adapt to the digital age. It’s a fitting metaphor for the challenges of communication in an Agile world, where information overload and constant interruptions can make it difficult to truly connect and collaborate.

So, as we raise a glass to Alexander Graham Bell and his telephonic triumph, let’s also take a moment to reflect on the future of Agile. Is it time to hang up on the old ways and embrace a new era of software development, one driven by AI, automation, and a more streamlined approach? Or can Agile adapt and evolve to remain relevant in this rapidly changing landscape? Only time will tell. But one thing is certain: the world of technology never stands still, and those who fail to keep pace risk being left behind, like a rotary phone in a smartphone world.

From Zero to Data Hero: My Google Data Analytics Journey

Just a few short months ago, the world of data analytics felt like a vast, uncharted ocean. Now, after completing Google’s Data Analytics Professional Certificate (or at least the 12+ modules that make up the learning path – more on that later!), I feel like I’ve charted a course and am confidently navigating those waters. It’s been an intense, exhilarating, and sometimes head-scratching journey, but one I wouldn’t trade for anything.

My adventure began in October 2024, and by February (this week) 2025, I had conquered (most of) the learning path. Conquer is the right word, because it was definitely an intense learning curve. 2000’s dev junior SQL skills? Yeah, they got a serious dusting off. And my forgotten Python, which was starting to resemble ancient hieroglyphics? Well, let’s just say we’re on speaking terms again.

The modules covered a huge range of topics, from the foundational “Introduction to Data Analytics on Google Cloud” and “Google Cloud Computing Foundations” to more specialized areas like “Working with Gemini Models in BigQuery,” “Creating ML Models with BigQuery ML,” and “Preparing Data for ML APIs on Google Cloud.” (See the full list at the end of this post!) Each module built upon the previous one, creating a solid foundation for understanding the entire data analytics lifecycle.

But the real stars of the show for me were BigQuery and, especially, Looker Studio. I’ve dabbled with other data visualization tools in the past (mentioning no names… cough Microsoft cough Tableau cough), but Looker Studio blew me away. It’s intuitive, powerful, and just… fun to use. Seriously, I fell in love. The ease with which you can connect to data sources and create insightful dashboards is simply unmatched. It’s like having a superpower for data storytelling!

One of the biggest “aha!” moments for me was realizing the sheer power of data insights. Mining those hidden gems from large datasets is incredibly addictive. And the fact that Google makes it so easy to access public datasets through BigQuery? Game changer. It’s like having a data goldmine at your fingertips.

This learning path has ignited a real passion within me. So much so that I’m now pursuing a Data Analysis Diploma, which I’m hoping to wrap up before June. And, because I apparently haven’t had enough learning, I’m also signing up for the Google Cloud Data Analytics Professional Certificate. I’m all in!

I have to say, the entire Google Cloud platform just feels so much more integrated and user-friendly compared to the Microsoft offerings I’ve used. Everything works together seamlessly, and the learning resources are top-notch. If you’re considering a career in data analytics, I would wholeheartedly recommend the Google path over other options.

I’m especially excited to dive deeper into the machine learning aspects. And the integration of Gemini? Genius! Having it as a code buddy has been a huge help, especially when I’m wrestling with a particularly tricky SQL query or trying to remember the correct syntax for a Python function. Seriously, it’s like having a data analytics guru by my side.

Stay tuned for future posts where I’ll be sharing more about my data analytics journey, including tips and tricks, project updates, and maybe even some data visualizations of my own!

Coursera do an official course = https://www.google.com/url?sa=E&source=gmail&q=https://www.coursera.org/professional-certificates/google-data-analytics – this you get a recognised formal professional certificate.

Or jump into Google Cloud Skills Boost: https://www.cloudskillsboost.google/ and get yourself a Cloud account and friendly with Gemini.

Modules Completed:

  • Work with Gemini Models in BigQuery
  • Analyzing and Visualizing Data in Looker Studio
  • BigQuery for Data Analysts
  • Boost Productivity with Gemini in BigQuery
  • Create ML Models with BigQuery ML
  • Derive Insights from BigQuery Data
  • Developing Data Models with LookML
  • Google Cloud Computing Foundations- Data, ML, and AI in Google Cloud
  • Introduction to Data Analytics on Google Cloud
  • Manage Data Models in Looker
  • Prepare Data for Looker Dashboards and Reports
  • Prepare Data for ML APIs on Google Cloud

So Long, and Thanks for All the Algorithms (Probably)

The Guide Mark II says, “Don’t Panic,” but when it comes to the state of Artificial Intelligence, a mild sense of existential dread might be entirely appropriate. You see, it seems we’ve built this whole AI shebang on a foundation somewhat less stable than a Vogon poetry recital.

These Large Language Models (LLMs), with their knack for mimicking human conversation, consume energy with the same reckless abandon as a Vogon poet on a bender. Training these digital behemoths requires a financial outlay that would make a small planet declare bankruptcy, and their insatiable appetite for data has led to some, shall we say, ‘creative appropriation’ from artists and writers on a scale that would make even the most unscrupulous intergalactic trader blush.

But let’s assume, for a moment, that we solve the energy crisis and appease the creative souls whose work has been unceremoniously digitised. The question remains: are these LLMs actually intelligent? Or are they just glorified autocomplete programs with a penchant for plagiarism?

Microsoft’s Copilot, for instance, boasts “thousands of skills” and “infinite possibilities.” Yet, its showcase features involve summarising emails and sprucing up PowerPoint presentations. Useful, perhaps, for those who find intergalactic travel less taxing than composing a decent memo. But revolutionary? Hardly. It’s a bit like inventing the Babel fish to order takeout.

One can’t help but wonder if we’ve been somewhat misled by the term “artificial intelligence.” It conjures images of sentient computers pondering the meaning of life, not churning out marketing copy or suggesting slightly more efficient ways to organise spreadsheets.

Perhaps, like the Babel fish, the true marvel of AI lies in its ability to translate – not languages, but the vast sea of data into something vaguely resembling human comprehension. Or maybe, just maybe, we’re still searching for the ultimate question, while the answer, like 42, remains frustratingly elusive.

In the meantime, as we navigate this brave new world of algorithms and automation, it might be wise to keep a towel handy. You never know when you might need to hitch a ride off this increasingly perplexing planet.

Comparison to Crypto Mining Nonsense:

Both LLMs and crypto mining share a striking similarity: they are incredibly resource-intensive. Just as crypto mining requires vast amounts of electricity to solve complex mathematical problems and validate transactions, training LLMs demands enormous computational power and energy consumption.

Furthermore, both have faced criticism for their environmental impact. Crypto mining has been blamed for contributing to carbon emissions and electronic waste, while LLMs raise concerns about their energy footprint and the sustainability of their development.

Another parallel lies in the questionable ethical practices surrounding both. Crypto mining has been associated with scams, fraud, and illicit activities, while LLMs have come under fire for their reliance on massive datasets often scraped from the internet without proper consent or attribution, raising concerns about copyright infringement and intellectual property theft.

In essence, both LLMs and crypto mining represent technological advancements with potentially transformative applications, but they also come with significant costs and ethical challenges that need to be addressed to ensure their responsible and sustainable development.