online. COMB, or Compilation of Many Breaches, as it's being called, contained 3.2 billion emails and passwords – roughly 40 percent of the entire population of the planet.8 Data is a valuable asset, but it also brings with it considerable business risk.
Another risk, of course, is that companies simply drown in all this data. Thus it's essential that companies develop smarter approaches to turning data into insights – and, in turn, ensure that those data-driven insights can be translated into action. Businesses must work to raise data literacy across the organization, and this means all decision makers in the organization must have access to the data they need, understand the value of that data, and have a basic ability to use that data. As such, we can expect to see more and more organizations implementing data literacy programs.
Artificial intelligence will help to raise data literacy and accessibility. Indeed, many off-the-shelf cloud data storage solutions offer some form of AI capability to help businesses make sense of their data. Going forward, augmented analytics will play an increasingly key role. Driven by AI, augmented analytics essentially means systems can automatically detect patterns in data by themselves, without being programmed according to a specific set of rules, and then push insights out to users without having to be asked specific questions. In other words, data will become more democratized, meaning people right across the organization will be able to exploit data, without the need for data science skills. This data democratization is an exciting trend to watch, and is just one of many advantages that have sprung from the rise of AI, which brings us to another trend.
Trend 4: Artificial Intelligence
The fact that our world is increasingly driven by data has brought about incredible leaps in artificial intelligence (AI). Data is a core enabler for AI, in the sense that the more data intelligent machines have to learn from, the better they become at spotting patterns, extracting insights, and even predicting what may happen next.
AI is developing at such an incredible pace that today's intelligent machines are now capable of carrying out a wide range of tasks previously reserved for humans. We have machine vision, where intelligent machines can “see” and interpret images or the world around them; natural language processing, where machines can learn to understand human language; natural language generation, where machines can generate human-like responses; and robotic process automation, where business processes are automated by software robots. To put it in everyday terms, AI is behind self-driving cars, facial recognition technology, recommendation engines, fraud detection, the content that shows up on your social media feed, and more. In 2020 alone, smart speakers answered 100 billion voice commands – 75 percent more than in 2019 – all thanks to AI.9 This “conversational AI,” where machines can use language with human-like accuracy, has made huge breakthroughs in recent years.
One of the most exciting recent advances in this vein is the GPT-3 AI. Created by OpenAI, a research company founded by Elon Musk, GPT-3 is better at creating any content that has a language structure – whether a human language or machine language – than any AI that has come before it. GPT-3 can answer questions, write essays, translate languages, summarize long pieces of text, take memos, and even create computer code. In one demo, GPT-3 created an app similar to Instagram – demonstrating how AI could play a huge role in software and app development in future.
Crucially, AI gives intelligent machines the ability to learn from data and make decisions, sometimes without human intervention. This is where the terms machine learning and deep learning come from. If we think of AI as the umbrella term, machine learning and deep learning are cutting-edge disciplines of AI that both involve getting machines to learn in the same way as humans do (i.e., by interpreting the world around us, sorting through information and learning from our successes and failures). Deep learning is the more advanced of the two because you can simply feed a deep learning system data and let it work out for itself how to find patterns.
One thing that hasn't yet been achieved is the idea of general AI, or the hypothetical ability of machines to understand the world as well as humans and learn any task. This is the AI of sci-fi movies and books. For now, AIs tend to carry out specific, narrow tasks. But just because general AI hasn't been achieved yet doesn't mean it's impossible. General AI is certainly the goal of several AI companies, and I suspect that if we put all the existing AIs together, they would be able not only to match what humans can do, but even exceed it.
That's why I believe AI is one of the most powerful developments we've seen as humans – matched only by gene hacking (more on this coming up). The takeaway for businesses is that, as our interactions with machines become increasingly intelligent, customers will expect all manner of products and services to feature some sort of AI capability. Then there is the potential for AI to enhance your internal business processes, whether it's through automation or helping human workers carry out their work and make decisions more effectively. In other words, every business is going to have to get smarter.
Trend 5: Extended Reality
As you can probably guess, advances in AI have fueled new developments in other technologies, including extended reality (XR). XR is an umbrella term representing the spectrum of immersive technologies we have today – virtual reality, augmented reality, and mixed reality – as well as those immersive technologies that are yet to be created. Currently, virtual reality (VR) offers the most immersive experience, by effectively blocking out the real world around the user and immersing them in a computer-simulated environment (usually with the aid of a VR headset). Augmented reality (AR), on the other hand, blends the digital and real worlds by overlaying digital objects or information onto the real world (often via a smartphone app or filter). Meanwhile, mixed reality (MR) sits somewhere between the two, creating an experience where the digital and real worlds can interact with each other – for example, letting a user manipulate virtual elements as if they were real.
XR is primarily known for immersive gaming, but it is finding very real, very practical uses across a wide range of industries – often being used to create more immersive, personalized experiences for customers. House buyers, for example, can go on immersive virtual house tours. Customers can try out products virtually (for example, by overlaying a new style of glasses over their face or digitally placing a new sofa in their living room). And sports fans can immerse themselves in the stadium experience from the comfort of their home. The list of exciting new XR applications goes on. But as well as giving organizations new ways to engage with customers and users, XR also brings exciting new opportunities to improve business processes, including training, education, and hiring. For example, trainees can learn in more immersive environments, with information being visualized in much more exciting ways.
The key takeaway here is that XR can help your business turn information into experiences. If you think about it, this has the potential to change pretty much everything, from the way we consume information to the way we interact with others. In the future, then, XR could extend to all aspects of life as we know it – to the point where we could potentially transform the world around us into something personalized, using special glasses, headsets, or even (looking further afield) contact lenses and implants. I believe our experience of the world will increasingly take place in this blurred space between the real world and the digital one. If you think of the time people spend on social media, crafting their online persona, it's clear the line between the digital world and the real one has already become pretty porous. I'm certain XR is going to accelerate this.
And that means we need to find innovative new ways to boost trust in the digital world.
Trend 6: Digital Trust
Digital trust can be defined as the confidence users place in organizations to build a secure digital world, where transactions and interactions can take place safely, securely, and easily. Many believe blockchain and distributed ledger technology will