The rise and rise of ‘data-driven’ businesses

In 2017, the Economist published an article claiming that ‘the world’s most valuable resource is no longer oil, but data.’ Ten years ago, this headline might have seemed more at home in a tech publication or even a plot twist in a bond film. These days, with the explosion of commodified data, it hardly even feels worthy of a headline.

Data is the fuel to deliver growth in a digital economy. Data has become more available, easier to store, and more valuable, with no signs of slowing. Businesses clamour to be labelled ‘data-driven,’ throwing around buzzwords like ‘big data,’ ‘metaverse,’ ‘AI’, and ‘machine learning,’ while in reality, many still display low levels of data maturity. Mistrust in data, manual processes instead of automation, and data silos are all too common. Businesses that have embraced data-led strategies have redefined their industries (think Netflix, Uber and Airbnb), while those that dug their heels in have perished (such as Blockbuster and Nokia).

The volume of data has exploded in recent times. In November, two new units of measurements were even introduced to account for the large numbers — for those interested, a ‘quetta’ has more than 30 zeroes. It can be hard to appreciate the expanse of data, but to provide some context:

• 64 zettabytes of data were produced in 2020, compared to just 2 zettabytes in 2010, driven by the rise of the internet, mobile devices and IoT. This is enough to fill 250 billion laptops*.
• This growth will not stop there. Technology is becoming more sophisticated and producing more data. For instance, wearables can take 250,000 measurements daily, while a self-driving car may generate between 3 and 40 gigabits of data per second.
• By 2025 we are expected to generate 181 zettabytes per year, almost three times the amount produced in 2020.

Businesses are attempting to gather more data by every means possible for fear of missing out on this lucrative resource. The good news is that storing the data is easier than ever before, as cloud computing means organisations can flexibly scale their storage with near immediate capacity. With only a few clicks online, we can achieve the same outcomes that previously required significant time, investment, and hardware.

While data has become abundant and the cost of storage has decreased, its value has seen a meteoric rise. Data can drive value in a multitude of ways. For example, it can trace every step of a customer journey, personalise their experience, deliver targeted advertising, perform predictive analytics, and monitor the health of assets. We have witnessed the returns from data in multiple high-profile examples in recent years:

• Apple’s privacy policy changes in iOS 14 limited Facebook Ads functions and its tracking for web and app conversions, costing Meta (the parent company) ~$10 billion per year from targeted advertising;
• Netflix has become a worldwide leading streaming service with over 200 million subscribers due to personalised recommendations and data-driven content; and
• Cambridge Analytica used information from social networks to enhance campaign targeting and influence multiple election outcomes.

It has reached a stage where Netflix knows more about which TV shows we like to watch than we would care to admit. Many people have even accused our devices, beyond just Alexa, of listening to us due to the accuracy of targeted advertising.

As data value has increased, businesses are acutely aware of the need to go all in. Data science is trending, enterprises are investing in training AI and machine learning on their data, and leaders are appointing Chief Data Officers. This new role reflects the growing value placed on data within an organisation. Data analyst graduate programs are also rising in popularity for many industries which were not previously data-heavy (e.g. agriculture, property).

The World Economic Forum estimates that by 2025, 85 million jobs might be displaced by a shift in the division of labour between humans and machines, while 97 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms.

In a world where data reigns supreme, squandering even a fragment of data can be considered a missed opportunity, so it can be tempting for companies to gather every scrap of data in the hope that they can later extract value. Is this an appropriate data strategy, or does it risk turning data into a liability rather than an asset?

In part II we will discuss our take on the equation.

Footnote: *Assuming an average of 256gb storage

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