The data age | The exponential growth of data will change the world
Harnessing the power of rich data is finally within reach
Over the past decade, I have watched business evolution take place, in real time. The adoption of technology into sectors that had previously been firmly stuck in the dark ages (in the business of health, fax machines were only banned in the NHS in 2020!); data-informed decision making at executive level (or at least earnest attempts to do so); and, the mainstream adoption of generative AI (it only took 70 years – the first program taught itself how to play checkers in 1952).
Here is the first of three articles with my thoughts on technology evolution, which in my opinion comes full circle to the need for us humans to adapt our own behaviours and professional development to thrive with the very technology we've created.
Over the past decade, I have watched business evolution take place, in real time. The adoption of technology into sectors that had previously been firmly stuck in the dark ages (in the business of health, fax machines were only banned in the NHS in 2020!); data-informed decision making at executive level (or at least earnest attempts to do so); and, the mainstream adoption of generative AI (it only took 70 years – the first program taught itself how to play checkers in 1952).
It is 80 years since the first computers began processing information, ushering in an era where data could be stored, analysed and utilised. These early combinations of circuits and relays were precursors to modern computers as we know them today. Designed to automate calculations and manage information in ways previously unimaginable, hinting at a future where vast amounts of data would be harnessed. Early demonstrations showed how computers could revolutionise industries by performing tasks that seemed almost magical at the time; real-time calculations that would take ‘human computers’ days, weeks and years to crack.
Today, computers are long past the experimental phase. Data centres now occupy an area around the land mass of Wales and process such a vast quantity of the world's data, simply unfathomable all those years ago. Yet, this historic and relentless growth is only the second-most-remarkable thing about the rise of data. The most remarkable is that it is nowhere near over.
To call data’s rise exponential is not hyperbole, but a statement of fact. The volume of data created every day is incomprehensible – and at an ever-increasing rate (if industry reports are to be believed, 200 zettabytes of data will be generated in 2025 alone – the seesaw heavily tipped by the ubiquity of rich data-hungry content such as video, exponentially driven by the rise of video sharing social media platforms and their addictive social magnetism).
That makes it hard for business people to get their heads around what is going on. When it was a hundredth of its current size, ten years ago, data was still seen as being of important-but-intangible-value. The next hundred-fold increase will be equivalent to multiplying the world's current data capacity by the land mass of a small continent in less than the time it typically takes to develop a major software platform.
Data will in all likelihood soon be the single biggest driver of innovation and efficiency on the planet. Or, should I say, the harnessing of data. It may be the largest source not just of business insights but of operational efficiency and gaining competitive advantage across all sectors. This will not solve humanity’s problems (or that of the average enterprise business), but harnessing that plethora of data effectively could address them more efficiently and rapidly. Think healthcare, genome sequencing and clever innovations that somehow make all our lives better.
In 2006, British mathematician Clive Humby said “Data is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used”.
If data is really the new energy of business; as wood turned to coal, coal to oil and oil to gas. Then truly, properly harnessed data is not oil at all, but the solar energy yet to power the economies of the world. Rich in superabundant possibilities but, thus far, so tragically under harnessed.
Building to success - block by block
I delivered a talk nearly a decade ago about how companies were getting merry on the hype and buzzwords of the industry. Generative AI, blockchain, digital transformation, 7G technologies.
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The talk essentially made fun of this dizzying new world of shiny new technologies and possibilities, not because they should be ignored. In fact, unquestionably, they should be adopted with open arms. After all, these technologies are about to have a forever-changing impact on the very fabric of how businesses operate, the way their consumers consume and with far reaching [positive?] consequences across all the markets they serve. The point was that companies should first take a breath to get the basics right – as the proverb goes, you must learn to crawl before you can walk. But then, running is just so much more invigorating, isn’t it!
The imperative was for companies to get a grasp of their own master data, shore-up a cohesive business strategy and adopt scalable, future-proof and interoperable technology – using a standards-based approach. This is the first, necessary, step towards survival in an inevitable data-centric future. It will enable companies to build from those foundational blocks a strategy to unlock informed decision-making at blistering speed. In the form of powerful new technologies and AI strategies; delivering value, growth and competitiveness. This talk was, expectedly, met with eye rolls and quiet groans. Perhaps the basics are the already known, unexciting pitfalls of nearly every business (bad data in, bad data out - yawn); and the future felt a little too out of reach.
Now, I suspect, if I dusted off the same deck and hit repeat, it would be met with eager executives, ready to sign up to the future.
How times change.
Crawl, walk, run
If running is blistering speed data-led decision making to get ahead of the competition, to grow faster and to serve customers better. Then, crawling is setting the foundational principles of standards, data management and technology strategy.
The aim should be to crystallise those foundational principles by stitching them into the very fabric of an organisations structure, its’ values and its’ ambitious outcomes. Sponsored and signed off at c-level and then threaded through everything a company does.
Collecting, cleaning and unifying the data flowing through an organisation is building block number one. Only then can raw data be transformed into useful information. The trusted source of truth and lifeblood of a company that’s future ready.
The benefits start right here, from block number one and always with a boost to productivity. Anything that companies use information for, even creating content or actioning tasks will become more efficient – and that includes pretty much everything. Of course, the risks of hyper focus and complacency will lead many well-intentioned business people to get blinkered by new shiny tools. And, so will well-intentioned business people from the competition. Simply put, here is where competitive advantage is fleeting, commoditised and easily lost. The prize lays ahead: Kaizen - continuous improvement. Build upon those foundational blocks, stitching the golden thread ever-up the value pyramid.
Then come the things fast, accessible and usable data will make possible. Clean, trusted data – made available in real time – can optimise business performance and even improve strategy and agility. It can drive the machinery of artificial intelligence. It can make companies smarter and more responsive to ever-complex evolving market environments and customer needs.
But it is the things that nobody has yet thought of that will be most consequential. In its radical abundance, more accessible data will free the imagination, setting tiny circuits and relays of the mind sparking with excitement and new possibilities. Releasing the human beings still left rattling around within companies to be creative, to unleash their brain power to value-added innovative initiatives.
The dawn of generative AI, finally harnessed by enterprise businesses at large, marks a significant milestone in the journey toward a data-rich world. As we move forward, the ever-increasing flow of data – and the technology that enables it – promises a future where no company need go without the blessings of its’ own extra-rich information. Transforming it in a moment into wisdom and decisive action.
Of course, the winners and losers will be defined in retrospect. Those that got it right, those that timed the markets they serve to perfection – not forgetting those that made it lucky (although, I’m sure company executives will be flush with faux-confidence that luck had no part to play). And, of course, those that failed to make a mark on the future, those that had their Kodak Moment of failure. The truth is, for long-term, sustainable success, my money is on those companies with a foundation of preparedness and purpose, who act decisively and with a clear understanding of the data flowing through their own organisation, with built-in resilience and agility.
Senior Director, Partnerships and Alliances Europe
5moA super read Phil!
Alliances + Channel Strategy - Building Successful Two-Way and Mutually Beneficial Long-Term Partnerships
5moThis is a great reflection, Philip James-Bailey. One thing I often think about, particularly as it relates to GenAI; the context of the data and its relativity to a specific domain is often times just as important as having access to a large public dataset. The problem, if you were an enterprise looking to tangibly own/train your own internal AI models/LLMs, is that current methods are A) hyper-inefficient to train a model on a domain specific topic/task, requiring hundreds to thousands of data points in order to accomplish something as simple as remembering a name (Fine-Tuning) and B) Work around's to models actually remembering/generalizing facts/learned experiences from it's interactions with users/new pieces of information (RAG). My theory is that one of the winner's in the space will not only be the one's with the largest 'data moat', but those who have the capability to hyper-efficiently train models with context/domain specific datapoints. In fact, this will fundamentally democratize the use of this technology for individuals and organizations of all sizes, not just the enterprise.