The enterprise has unprecedented access to data and analytics, and is increasing its ability to use them. The core of modern enterprises is changing to the point that it’s threatening to become obsolete. This will result in a change of guard for those who are exemplary.
There are many new platforms and approaches that can help you fulfill the imperative. It’s a great time to work with enterprise data and analysis. It will be a thrilling journey to use enterprise data in 2022. These are the trends you should be following as the year progresses:
Embedded database at the edge are a common use of database technologies. Enterprises have become pseudo-software factories, creating mobile apps, and supporting IoT. This is why embedded databases have been so popular.
IoT-enabled businesses can use embedded databases to transfer aggregated sensor data to an online back-end database. This allows operations to directly benefit from the data. Data from all devices is also being stored in the back-end database. This allows for analytics to be developed to improve the business.
Artificial intelligence chips are taking centre stage in these environments. AI chips is a new generation microprocessor that was specifically designed to handle artificial intelligence tasks more quickly and with less power. They excel at processing artificial neural networks, and can be used to train and infer machine learning models at the edge.
Edge computing hardware will need to be more efficient as new applications and sensors, along with larger AI models and better sensors, will become possible. It is becoming more common to use data inference to make better decisions, and not send it to the cloud. Distributed sites can also be connected to an enterprise computing environment in order to create a unified computing ecosystem.
The demand for intelligent edge applications is growing rapidly. With widely available development tools, and new features being launched by semiconductor companies, edge ML applications are likely to become a major trend. Graph databases will also be a major trend this year.
Wide Adoption of Containerized Environments
Enterprises have expressed an interest in containerized environments. Containerization has been hindered by the fact that legacy infrastructure is still being used for stateful applications that require persistent storage. In their most recent releases, Kubernetes-ready distributed RDBMS platforms addressed stateful persistence issues. This will increase the Kubernetes potential this year.
The solution simulates one logical database and guarantees transactions while allowing for scalable deployment across clusters and regions without federation.
The number of high-quality Kubernetes products has increased. Kubernetes will be boosted by advances in security and orchestration.
AI, Based on Data, Moves Hard into Design
We’re not all in the NFT art, whiskey, music, or paintings businesses, but we can look to them as examples of what AI-based design is capable of and create bridges into our designs in the enterprise. These nascent pieces of art are just as bad as AI based design. It will only get worse from here, much like AI-based enterprise design.
Ignoring AI and relegating design to humans is dangerous this year. This applies to all technology and software that we create in an enterprise.
Google claimed that an AI can create a chip in six hours that takes humans months to design. Already, the AI was used to create the latest version of Google’s Tensor Processing Unit chips.
AI is able to explain code in English, and offer suggestions for improvements. It is beginning to write code. One example is the Probabilistic Programming for Advancing Machine Learning (PPAML), a program of The Defense Advanced Project Agency that develops new technologies to improve machine learning for specific questions.
So will AI be writing the majority of enterprise code? It will eventually be true, and it all begins this year. AutoML, a machine-learning model that selects algorithms and creates other machine learning models through reinforcement learning, may be the first step in making it happen.
Additional trends in enterprise data to be watched are data observability and data catalogs.