Digital transformation is driving the business growth from insights to opportunities, speed delivered from increased productivity and experienced customers.
The deep techs like AI & ML, IoT, Analytics and Integration API will help the business to build smarter applications, give them the ability to analyze, detect, predict and act on intelligence and deliver the differentiation, growth the business is looking for.
Let us deep dive in to some of these key technologies to see how they help to build smarter applications.
Some of the top advanced analytics use cases for enterprises:
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1. Artificial Intelligence
AI optimizes business processes, increasing productivity and efficiency while automating repetitive tasks, and supporting human capabilities.
Within business scenarios, artificial intelligence (as well as machine learning, in many cases) provides an advanced degree of responsiveness and interaction between businesses, customers, and technology, driving AI embedded applications to an elevated performance level.
There are numerous features, that can be imbibed in to applications based on artificial intelligence. A simple example is data alerts. With an AI algorithm using the most advanced neural network for anomaly detection, and a machine-learning algorithm for pattern recognition, these data alerts learn from trends and patterns and let you know as soon as something important happens. That way, when a pre-defined goal is met, or when something unexpected happens, the application notifies and enabling IT to keep continuous control over business.
When application is combined with AI capabilities, it enables businesses to obtain better value from their data, automate and personalize services, improve security, and supplement human capacity.
How will AI help to build intelligence features for applications?
Personalization: With the use of technologies such as natural language processing (NLP), which automatically processes human speech patterns and voice control, the application become easier to use. This can be deployed across customer service functionality to improve customization and better address client needs.
Speed: Artificial intelligence-enabled applications speed up internal processes and operations, allowing businesses to obtain fast answers to questions, make quick forecasts, and speed up their overall level of responsiveness.
Security: Due to artificial intelligence-enabled automation, and the ability of machine learning to recognize patterns, application security is enhanced by the quick identification and remedy of potential threats with built-in self-recovery.
2. Machine Learning
Machine learning, a subset of AI, is one of the fastest-growing segments of software and many ML platforms are emerging to support a host of use cases across business segments. Machine Learning (ML) is embedded in applications to automate responsiveness in customer service reports and applications, such as AI-powered chat operations with live chatbots.
ML is built on an autonomous operational model; it will facilitate software and platforms that empower developers to automate significant chunks of internal operations other than customer service or experience alone.
3. Centralized Analytics
The third technology which can imbibe smartness in to application is Analytics. As digital transformation across industries accelerates, businesses across sectors look to data to streamline their organization while gaining a deeper insight into their customers or users.
These centralized analytics will enable users to look into their data from a single point of truth, discovering hidden insights by utilizing modern solutions such as performance dashboards, where every member of the team has access to the most vital business information. Moreover, the centralized nature of SaaS models will enable users to access data from any device, at any time. Modern online business intelligence makes this happen with the help of advanced software capabilities and the online environment where each member of the team has access to their analytics.