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Big data has been transforming companies and entire industries worldwide for well over a decade but money continues to pour into the field as enterprises become ever more data driven. Indeed, figures show a 54 per cent increase in data analytics spending worldwide in 2024.

A combination of cutting edge analytics and the latest AI innovations is turbocharging the potential utility of data as a provider of strategic insights, and that impact is being keenly felt across the investing landscape.

Expanding data universe

The scale and breadth of data that’s being created today is mind-boggling. Internet penetration rates of 67 per cent mean there are already around 5.5 billion people online globally, while GSMA’s recent predictions suggest there will be a very similar number of mobile internet users by 2030. Add to that the spread of Internet of Things (IoT) technology, and there is little wonder as to why data creation continues to gather pace. So much so in fact that UBS expects the overall ‘data universe’ to grow tenfold between 2020 and 2030, from something like 66 zettabytes to 660 zettabytes.

For its part, the fintech sector is unusually, if not uniquely, well positioned to harness this torrent of information. Companies routinely generate vast amounts of transactional data, capturing intricate details about their customers, suppliers, and everyday operations. There are now also millions of networked sensors embedded in devices like smartphones, smart meters, and industrial machines of various kinds, continuously creating and communicating data, all of which contributes to the IoT ecosystem. Everyday digital interactions such as social media activity, smartphone usage, and online transactions, also add to this growing repository of big data.

In fintech settings, being able to quickly and purposefully analyse diverse and expansive data sets in real time translates to tangible and potentially highly valuable insights. For investors, these insights can enhance predictive accuracy and optimise portfolio management, while allowing fintech companies to better assess credit risks and tap into underserved market segments. In doing so, big data analytics not only supports the identification of new revenue streams but also supports much more informed and effective investment decisions.

The various Vs 

One lens through which the fundamental advantages of big data analytics can be viewed is what’s termed ‘the Vs’, generally taken to include some or all of ‘volume’, ‘velocity’, ‘variety’, ‘veracity’ and ‘value’. In essence, the concept emphasises the importance of establishing how much data is being collected, how quickly that happens, how broad the array of data being gathered really is, how reliable it should be considered and what its potential value might be. 

These aspects of big data analytics support and inform business decisions across industries and among investors, regardless of their priorities or areas of expertise. Investors have always sought to gather as much relevant information as possible, such as financial statements and stock prices. However, big data analytics has significantly expanded the scope of what might be considered relevant information.

Now, large sets of data are analysed using advanced technologies to reveal patterns, trends, and associations. This includes data from traditional sources like financial statements, but also extends to diverse and novel sources such as news reports, social media platforms, and alternative data like online reviews and geolocation data. By leveraging these vast and varied data sets, contemporary investors gain a comprehensive view of the market landscape, uncovering insights that provide an informational advantage. This advantage can be translated into better investment outcomes, informed by a deeper understanding of market dynamics and emerging trends.

Growth and change

Taken as a whole, the big data analytics market was worth around $300 billion globally 2023, according to Fortune Business Insights, but should be worth an extra $50 billion or so this year. What’s more, it may well be more than three times larger by early next decade. 

Goldman Sachs, BlackRock and JPMorgan & Chase are just a few of the finance sector behemoths that have led the way in the use of big data as an investment tool in recent years. They’ve each found their own methods of harnessing the power of big data to better inform investment decisions and risk assessments. 

In investment banking, Goldman Sachs strategic use of big data is based on what the bank describes as “a disciplined application of quantitative techniques to capture both the fundamental as well as the behavioural aspects driving companies’ stock returns”.  For its part, BlackRock, the world’s largest asset manager, made some big bets over the past decade into big data analytics, with ‘Aladdin’, its risk analytics and portfolio management platform among the most prominent outcomes. 

Meanwhile, JPMorgan & Chase has been consistently innovating around big data as an investment decision-making tool, pointing to improved data management as a fundamental means through which insights can be more effectively identified and leveraged. The bank has made the case that by combining machine learning and human intelligence, data can be better labelled in ways that have a powerful impact as it essentially enables investors to “learn more from less data”. 

Big challenges

Data security is well understood as being a vital issue for investment decision-makers, as it is for any contemporary business or organisation aiming to make use of big data analytics. Cyberattacks are a feature of the modern world and can be enormously costly, particularly for finance firms, where they are not prevented or handled properly. Beyond the potential for major losses due to lax data security, investors also need to ensure that the data they’re working with is decipherable and of a suitable quality, as much as possible, and they also need to adhere to whatever regulations are stipulated in their specific jurisdictions.

Other key challenges include plugging skills gaps and overcoming data silos, which can lead to a problem of “jammed data”, as JPMorgan calls it in some of its most recent contributions on the topic. Indeed, the banking giant’s experts have described easy access and integration of data as being of crucial importance for institutional investors as they aim to make strategic use of advanced analytics and to be ready for AI innovations that emerge to have an impact in the space.

For investors and businesses across industries, data management is now strategically vital for many different reasons but not least because it helps them be ready for and to keep pace with the very latest technological advancements. As such, determining how information is best stored, identified, protected, integrated, moved around and made available represents a major ongoing challenge for anyone looking to access the fundamental benefits of big data. 

Big opportunities

The research firm Gartner forecast that by 2025 more than 75 per cent of all venture capital and early stage investment decisions will rely more on data science and AI than on the “gut feel” instincts of investors. There’s no doubt that AI tools and developments around big data have had a huge impact on how investment decisions are made. However, evidence and more recent expert testimony suggests that human expertise functioning in harness with big data will continue, for some time yet at least, to represent the best recipe for consistent success in the realms of investment decision-making. 

What’s certain is that the scale and variety of sources from which investment decision-makers can gather and use information will continue to expand. After all, Gartner also recently forecast that the scale of global banking and investment service companies’ IT spending would be worth over $650 billion in 2023 alone, with data analytics among the foremost investment areas. That spending reflects something of the scope of opportunity that exists currently across the extremely fast-changing and hypercompetitive data analytics landscape.    

Ultimately, the future of investment will depend on seamlessly integrating human insight with cutting-edge data analytics. As the data landscape evolves, embracing these advancements will be essential for driving innovation and staying ahead. In fintech, those who effectively merge human judgement with sophisticated data tools will not only navigate the complexities of the financial world but also capitalise on emerging opportunities.