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From Big to Small Data
By Driss Temsamani, Head of Digital Channels and Enterprise Banking Services, Latin America, Citi
Big data has not gone away: the problem was that data has become too big. According to the International Data Corporation (IDC), the global ‘datasphere’ will grow by ten times between 2016 and 2025 to 163 zettabytes (trillion gigabytes) . Not only does the volume of data continue to grow exponentially, but so too does the speed of data and the number of ways that we produce and consume data, not least with the increase in smart devices and the internet of things (IoT). The more data that is produced, and to which individuals and businesses are exposed, the more difficult it becomes to make sense of it. Ultimately, however, the challenge is to decide which data is important, and what you can do with it to create new insights or opportunities i.e. picking out the small data from the big data.
From Big Things, Little Things Grow
However many people or technology you employ, it is almost impossible to find that (small data) needle in a haystack, and use that needle to create something of value, without harnessing technologies such as artificial intelligence (AI) and machine learning (ML). Using ML, you can create algorithms to sift through data and work out what you can learn, while AI offers an alternative to human analysis, but on a far more scalable basis. Essentially, AI extends beyond the collation and identification of the right data to the ability to recommend or take an action that fulfils a useful function.
Big data is not dead, but there is a greater realization that bigger is not necessarily better
Despite the benefits, if sensitive data, such as medical or financial information amongst many others, falls into the wrong hands, or is used in the wrong way, the potential to harm as well as help society and individuals can be enormous. Unfortunately, as the quantity and influence of data in society increases on one hand, so too does the incidence and severity of cybercrime.
Distributed trust technology?
New technologies, such as blockchain or distributed ledger technology (DLT), are likely to play a vital role in creating an environment of trust that is essential to a data-driven society.
Currently, information or assets, such as photos, files or documents, are copied every time they are transmitted to another party. Once transmitted, the originator does not know, and has no control over, how it is disseminated or altered. This creates an issue of trust. How do you know the asset that you’re working with is the original, unadulterated version? Scale this up to entire communities and the problem of trust and integrity becomes a serious concern: at the very least ‘fake news’. Consequently, a critical challenge is how to harness the data for the benefit of individuals, businesses and societies whilst keeping both the data and the people to which it relates safe and secure.
In contrast, data exchanged through blockchain-based solutions exists in one iteration, whether a picture, document or contract etc. As this asset passes across the network from one node or entity to another, a unique stamp means that every action or amendment is visible, therefore creating trust and protecting community participants.
Big data is not dead, but there is a greater realization that bigger is not necessarily better. Instead, a fundamental objective in today’s data-driven society is to find the right data, and use it to create meaningful insights and accurately informed decisions. New and emerging technologies such as DLT and AI play complementary roles in achieving this. By building trust into the exchange of data, the greater the validity of decisions and actions that are based on it for individual, commercial and societal benefit.