As we enter a new decade, the potential for new technology and discoveries seems somehow more present than it usually is. And, given how far big data has come in the last 10 years, it’s exciting to imagine what might come next.
In recent history, we have seen data influence elections, pave the way for machine learning technology, and generally increase at such an astoundingly exponential rate that the amount of new data created every day has skyrocketed to a figure of 2.5 quintillion bytes. And it’s still going up.
“Data is on overdrive,” Domo reported in 2017. “It’s being generated at break-neck pace, flooding out of the dozens of connected devices we use every day, and it shows no signs of slowing down. In fact, the number of internet users has grown over a billion in the last five years, more than half of the world’s web traffic now comes from mobile phones.”
In their 2019 report, they claimed that, by 2020, “there will be 40x more bytes of data than there are stars in the observable universe” – and that should give some idea of the potential it holds.
Here, we discuss five key areas that are likely to be shaped by advances in big data technology during the 2020s.
Compared to where it was at the beginning of the 2010s, AI has already come a staggeringly long way. By the end of 2019, in fact, it was reported that there were approximately 100 new papers being published on machine learning technology every single day.
As we move into the 2020s, big data is set to have a huge influence on further developments. Data scientists and analysts will become more centric to AI creation and moderation, programming tools and options will become more diverse, and the quality of data will be far more important than the quantity (which may prove to be a problem given just how much is out there).
Building on this last point, we can also hope to see positive changes in this kind of technology thanks to a stronger focus on data ethics and what kind of data is harvested in order to teach AI. If the last decade has taught us anything about this area, it’s that we do not want a repeat of Amazon’s biased recruiting tool or Microsoft’s easily corruptible chatbot.
In the working world, this will mean that AI will expand to focus more on augmentation rather than automation, meaning technologies will be developed to work alongside humans, not in place of them.
“Human augmentation also has the potential to make the world a lot more accessible,” reports Forbes. “According to Gartner Analyst Daryl Plummer, AI, virtual reality, and other new technologies will triple the number of people with disabilities employed by 2023 — leading to a brand new workforce.”
And data will provide the blueprints for it all.
“Companies are in the midst of many profound changes: The amount of data available and the speed of producing new data has been increasing rapidly for years, and business models as well as process improvements increasingly rely on data and analytics.” That’s according to BI Survey, who polled 2,900 business intelligence professionals in order to establish their predictions for BI tech in the coming decade.
Of all the trends offered as possibilities (which included things such as self-service BI and mobile BI capabilities), the three that came out as being the most important to focus on were data quality and management, data discovery and visualisation, and data-driven culture; essentially, data is the driving force here.
What this indicates is that businesses and organisations are progressing beyond the mindset of ‘harvest as much data as possible’ and instead transitioning into a new phase of analytics: one in which the quality of data is inherent to informing business decisions.
Using prescriptive analysis, companies will be able to pinpoint precise details about their audiences that they were unable to before: their habits, their reservations, their hopes. Ultimately, BI makes it easier for companies to understand what people want from them.
A knock-on effect of this will no doubt be better engagement between companies and their customers/clients, and an ability amongst businesses to tailor their services to very specific needs. This new age of consumerism will be data-driven, and – more importantly – more closely consumer-dictated.
Data use AI and BI technologies is certainly groundbreaking, as will be its effects on the next decade – but they may not be immediately noticeable for the average person. Instead, data’s influence on the abilities of commercial tech, most prominently Internet of Things devices, will be front and centre of the public eye.
According to a report from Intel, “Our IoT world is growing at a breathtaking pace, from 2 billion objects in 2006 to a projected 200 billion by 2020. That will be around 26 smart objects for every human being on Earth!” Admittedly, the vast majority (around 85%) of those devices won’t be in homes – but the fraction that will be is also set to grow.
Virtual assistants will become better at analysing data and using machine learning programs to engage effectively with users; for example, it may be possible for smart fridges to learn how often their homeowners buy milk, or determine whether they have specific dietary requirements, or even be able to give recipe suggestions based on what items are currently being stored. Interaction with devices like this will be seamless and refined, to the point where they will eventually (maybe not in this decade, but the next few) become the norm.
Cars will also benefit from integrated devices, again powered by consumer data and machine learning. In fact, Jaguar already have plans in place to buy their customers’ data from them in order to have material with which to teach AI to be able to drive a car autonomously.
On a much grander scale, though, the single biggest visible impact will emerge in smart cities – urban environments that integrate technology and infrastructure to moderate and understand things like air quality, transport regulation, and resident satisfaction. Projects such as this will obviously take some time to get off the ground but, once they do, data will ensure that they are constantly being improved and tailored to our changing needs.
Big data is obviously a vital resource in the medical world, but it’s only in recent years – and thanks to sophisticated machine learning technology and the digitisation of health records – that we’ve been able to truly utilise it.
In March 2018, Reuters reported that “Half of the world’s 1,800 clinical studies involving real-world or real-life data since 2006 have been started in the last three years, with a record 300 last year, according to a Reuters analysis of the U.S. National Institutes of Health’s clinicaltrials.gov website.”
These studies have been able to process multiple times the amount of data that researchers have previously been able to, and in only a fraction of the time. Plus, thanks to intelligent algorithms, new discoveries could potentially be made in a manner that is only really possible by analysing data en masse.
Outside of research and in everyday practice, too, big data is having a positive influence on medical care. Already, there are companies that intelligently handle prescriptions using big data to predict whether or not a patient might be at risk on certain medication, and AIs being used to help diagnose patients.
Big data requires big storage, and the analysis of it requires a lot of processing power. In-house data warehouses can stretch to the task to some extent – but for large tasks, cloud computing is a necessity. Up until recently, we’ve conventionally thought of these two fields (big data and computing) as collaborative rather than cohesive. But that may change in the next decade.
“Data scientists and software engineers are two different fields, but that doesn’t necessarily mean overlap doesn’t happen,” writes Smart Data Collective. “It absolutely does and professionals must understand that achieving it is a vital part of staying relevant in today’s market.”
Cloud computing is where the two disciplines converge, and big data would struggle a lot without it. Because of this, it has become increasingly important for professionals to understand the two fields – so expect to see some change in their skillsets.
What’s more, there are so many advantages to using cloud based storage that it makes little sense not to use it. With increased ease of scalability, lower costs, and better business continuity/disaster recovery capabilities, it’s far superior to warehouse storage.
And, as Thorntech points out, “When your team focuses on analyzing data instead of managing servers and databases, you can more easily and quickly unearth insights that can help you augment product lines, boost operational efficiency, improve customer service, and more.”
By the end of the 2020s, cloud computing will most likely be the norm for big data analytics.
Data for a better future
There is no hyper-accurate way of telling exactly when these sorts of technologies will emerge, or even how quickly they will progress once we do start to see them become a more prominent part of our lives.
However, as data-driven technologies are essentially self-perpetuating in their improvement (using the technology provides yet more data which, in turn, can be interpreted to make any necessary adjustments), it is logical to assume that, once they are here, they’ll make rapid progress.
The 2010s and previous decades certainly laid the groundwork for everything that is yet to come, but the 2020s are where we’re really set to see a boom in data-driven technology.
For more articles like this, plus unique insights and job opportunities, make sure to sign up to the WE ARE DATA community.