Top Four Data Analytics And AI Trends That Will Drive Businesses In 2023
As we enter yet another year of brilliant technological possibilities, businesses will make decisions where rapid scaling and shortening the journey from data to outcomes will take centerstage
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The year 2022 has been a fascinating year on many levels: improved enterprise-wide data adoption, fierce migration to the cloud, heavy doses of automation, predictive analytics and real-time visualization. As we enter yet another year of brilliant technological possibilities, businesses will make decisions where rapid scaling and shortening the journey from data to outcomes will take centerstage.
Let's unpack four hot data analytics and AI trends that will skyrocket value creation in the year to follow.
Platform intelligence: collect, curate and collaborate
Organizations seek solutions that help them operate smarter and deliver better throughout a project or asset lifecycle for confident decision-making. And to make this process easy, platformization has become a critical part of any business strategy today. In 2023, you can expect faster adoption of platform intelligence: a new breed of products that will help businesses achieve higher levels of efficiency to improve project and company outcomes.
Platform intelligence comprises several participating, intelligent products to create a well-functioning ecosystem to give you a better playing field for experimentation. This year, you will see a good influx of co-created products and combined services, improving customer and partner participation across industries.
Explainable AI (XAI): reason out your decisions
Good decisions make good businesses. And every decision must be justifiable. With AI, transparency is a fundamental necessity. But how do you attain this openness while utilizing the benefits of AI? This is where XAI can help.
Banks, for instance, employ XAI to give credit officers risk data about their clients. They learn why credit applications are approved or denied and give customers pointers on strengthening their submissions. An opaque system, in this case, could breed prejudices and result in significant losses for the bank. But with XAI, you are supporting your decisions with reasons. Building an XAI model requires creating a black box representation and, concurrently, building a post-hoc justification for how the model manifested in a given condition. XAI analyzes the internal workings of the model to comprehend the significance of various attributes and corresponding choices.
XAI will undoubtedly play a vital role in the upcoming years across any business ecosystem.
Edge artificial intelligence (Edge AI): scale rapidly
What if your phone could recommend and arrange a game night with your friends on your birthday based on the predicted weather condition that day? That's moving AI to the network's edge to unlock unique experiences and deliver data-driven outcomes. According to IDC, by 2023, more than half of new enterprise-class IT will be installed at network edges rather than in centralized data processing units.
Today, most edge AI use cases are in consumer electronics. But with the ongoing healthy surge of intelligent automation and predictive analytics, you can expect corporate edge AI to expand more quickly. An effective edge AI model features an edge computing infrastructure that handles heavier AI workloads at and close to the edge. Edge AI and storage solutions can help organizations utilize their data effectively with speed and limitless scalability.
Composable analytics: reuse to create new solutions
Composable data analytics is like using the same Lego blocks to build new structures every time. It is a method that enables organizations to make analytics capabilities modular to build tailored plug-and-play analytics solutions without intense programming knowledge. Composable analytics can offer greater adaptability. Businesses can reduce data center expenditures using composable analytics even after migrating to the cloud.
You can assemble existing analytics sub-components in exciting ways to address user requirements. Analysts and citizen data scientists will likely become the architect of such reusable analytics assets to create new business value. This neat way brings consumers closer to future applications. Composable analytics makes self-service analytics culture widespread within an organization, thus, easing analytics adoption at scale.
Future belongs to autonomous enterprises
These intriguing trends will shape the faces of future businesses. Organizations have already set the foundation right, embedding analytics and AI at the core of processes and operations. But how fast we can teleport to that era where our organizations are self-driven to create thoughtful socio-economic impact is something only time will tell.