Gartner's Strategic Technology Trends for 2022

Gartner released their report on strategic technology trends for 2022. It makes for some interesting reading. Major trends are:

• Data Fabric – a process of integrating data so it is available across platforms. It uses metadata and embedded analytics to discern characteristics, leading to more efficient data management.
• Cybersecurity Mesh – Extending beyond perimeter protection to integrate security systems across platforms and data systems
• Privacy-Enhancing Computation - (PEC) approaches allow data to be shared across ecosystems, creating value but preserving privacy. Approaches vary, but include encrypting, splitting or preprocessing sensitive data to allow it to be handled without compromising confidentiality.
• Cloud-Native Platforms - Lift-and-shift cloud migrations focus on taking legacy workloads and placing them in the cloud. Because these workloads weren't designed for the cloud, they require a lot of maintenance and don't take advantage of any of the benefits.
• Composable Applications - These are made up of packaged-business capabilities (PBCs) or software-defined business objects.
• Decision Intelligence - improves organizational decision making by modeling decisions through a framework. Integrating data, analytics and AI allows the creation of decision intelligence platforms to support, augment and automate decisions.
• Hyperautomation - a business-driven approach to identify, vet and automate as many business and IT processes as possible. It requires the orchestrated use of multiple technologies tools and platforms, including RPA, low-code platforms and process mining tools.
• AI Engineering - the discipline of operationalizing updates to AI models, using integrated data and model and development pipelines to deliver consistent business value from AI.
• Distributed Enterprise - is a virtual-first, remote-first architectural approach to digitize consumer touchpoints and build out experiences to support products.
• Autonomic Systems - self-managing physical or software systems that learn from their environments. But unlike autonomous or automated systems, they can dynamically modify their own algorithms with no software updates. This allows rapid responses to change, enabling management at scale of complex environments.
• Generative AI - a form of AI that learns a digital representation of artifacts from sample data and uses it to generate new, original, realistic artifacts that retain a likeness to the training data but don’t repeat it. That allows generative AI to be an engine of rapid innovation for enterprises

Get the full report here:

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