Bryan Harris, Executive Vice President and Chief Technology Officer, SAS outlines the top tech and analytics predictions for 2023.
Emerging technologies disrupt the industry. Emerging technologies, like blockchain, esports and simulation, are disrupting traditional industries, offering future-focused innovation and converging into the next iteration of the web. These technologies are causing an explosion in the rate, complexity and volume of data, and creating an even more pressing need for analytics, machine learning and AI to help make sense of it all. Looking to the future, they offer opportunities to reimagine the ways we solve complex problems, and ultimately scale human observation and decision-making.
Disruption fuels analytics innovation. In 2023, disruption will continue to change us, our society and the way we do business. Advancements in areas such as natural language processing, conversational AI, predictive modeling, complex simulations, and computer vision will continue to make positive impacts on businesses and society. The stark reality is that the volume of data being created daily is far exceeding the collective human capacity to make sense of it. I believe the human need to overcome this information overload will lead to even more innovation in analytics and AI.
Trust and explainability are top priority. As organizations ramp up their adoption of AI models in their organization, trust and explainability will be the number one expected feature. You can’t deploy hundreds of AI models in a business if the users/consumers don’t trust the results. AI-driven decisions must be defendable and explainable, especially when AI makes a recommendation or decision that is surprising or unintuitive.
Emergence of AI model marketplaces. Coming soon are industry-specific AI model marketplaces that enable businesses to easily consume and integrate AI models in their business without having to create and manage the model lifecycle. Businesses will simply subscribe to an AI model store. Think of the Apple Music store or Spotify for AI models broken down by industry and data they process.
Data management becomes automated with AI. We continue to see organizations struggling to keep up with the speeds and feeds of their data, spending 80% of their time simply wrangling data and 20% of their time performing analysis and modeling. Over the next decade, one of the largest impacts AI can make to overcoming the information overload is by automating data management processes so customers can spend 80% of their time performing analysis and deploying more models into production.
Digital and synthetic twins take center stage. The next generation of the analytics life cycle will see a focus on simulating complex systems to help prepare for any possible scenario or disruptive event with digital and synthetic twins. Introducing rare events into our modelling and simulation will be key to understanding the highest probabilities of outcomes when the past is not a predictor of the future. From there, businesses can make rapid and resilient decisions to minimize risk and maximize profits.
Blockchain moves from hype to mainstream. Blockchain will continue to evolve out of the hype cycle and into the of role of transforming more traditional business realms. Blockchain’s strong connections to the emergence of NFTs, cryptocurrencies and other headline-grabbing technology developments can make blockchain appear unsuited to more traditional environments and challenges. But leaders across industries will need to find ways to square blockchain’s exotic reputation with its practical, everyday potential capabilities to be a true force in business. Blockchain is the natural progression of how software reduces the cost of business.
Blockchain simplifies the mundane but increases risk. Blockchain and cryptocurrency are disrupting traditional payment methods across multiple industries and a “blockchain bellwether,” the financial services industry offers a glimpse of the future of blockchain. We are going to see blockchain move from away from high tech activations towards simplifying mundane and everyday processes like reading an article behind a paywall. Enabling microtransactions for online activities will give control back to content providers and offer new incentives for content owners to develop new markets and for others to discover content.
On the risk side, the decentralized digital currency supports a natural extension of the internet that isn’t controlled by a centralized authority. And since money can be swapped almost instantly across blockchain platforms, it has created new avenues for money laundering and fraud. With heightened security and privacy threats of blockchain will come the need for analytics to predict and prevent risk.
Blockchain and esports converge. Blockchain and esports industries will continue to converge and there will be blockchain-first built games in the next five to 10 years. Esports now relies heavily on analytics, machine learning and AI to provide a professional experience for many gamers and fans across the world. From finding parity in players during matches and analyzing interactions inside each universe to tracking and promoting inventory you can buy – the data to analyze in gaming is endless and will continue to grow.