The industry has been rapidly changing with the explosion of computing power and the rise in the digitization of transactions and information. There has been a steady rise, as nearly 56% of IT organizations are piloting, shaping, and implementing Artificial Intelligence (AI) based solutions, as per 2021 survey data.
Also, in the 2020 survey, nearly 50% of respondents report AI adoption in at least one functional area.
AI can be used in every single industry vertical – Seismic analysis for Oil and Gas explorations, Automobile, Video Creations or Surveillance, Manufacturing, Genomics research, Medicine and Drug Development in Healthcare, and Banking applications.
The currency of tomorrow is Data. We have been told that Data is the new oil and Artificial Intelligence (AI) is the accelerator. The ability of AI to transform every industry vertical can be noticed in our day-to-day lives. Let’s learn more about how Data Apps have been fueled by AI.
Data Apps
The term ‘app’ is a short term for ‘applications, used commonly to denote a free-standing software that has a very clear set of applications. Apps are built once and deployed with easy-to-use interfaces for a large user base.
Similarly, data apps are specifically geared to manage data-intensive operations. Data operations in the form of Excel macros, pythons scripts, or complex routines are maintained by the analysts and engineers.
Artificial intelligence Fueling Data Apps
“The promise of AI is that knowledge gained from applying analytics to the wealth of data that is available today will enhance any decision-making process with additional intelligence, helping us produce quicker, more effective outcomes.” – Microsoft
AI Data Apps is an application that mimics human behavior by learning various data practices and insights. Artificial Intelligence, with Machine learning, is used to provide users with the needed functionality and drive the business process into a much simpler one.
Artificial Intelligence is used to build and develop an intelligent data output from scratch with the help of Machine learning and deep learning capabilities.
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These are the top Artificial Intelligence-based Data Management Apps:
1. Salesforce Einstein is an AI-driven CRM system developed for businesses. This app caters to numerous business needs, such as sales, analytics, marketing, community, and commerce.
2. Infosys Nia is a data management tool that AI powers. This application is utilized by companies to streamline their complex tasks. The software comes in three parts: data platform, knowledge platform, and automation platform.
3. Butter.ai: Powered by AI, Butter’s job is to find the document you are searching for in the company archives, no matter how deep it has been kept. It brings you all the information with your entire team. It works even with vague references or file names.
4. Microsoft Azure: Azure Machine Learning Studio is an AI-driven programming tool designed for data analysis. It gives users the freedom to create models by appropriately dragging and dropping the objects. MS Azure has the option for low-code and no-code when managing and authoring projects.
5. IBM Watson is a machine learning tool developed by IBM. It enables users to gain maximum information from minimum data. After integrating IBM Watson with your business operations, you will be able to create complex methods from the data.
6. Content DNA Platform: This is an AI-based tool for content creators. It is available on both cloud and premise. Content DNA Platform helps creators with deeper video analysis and automates all the processes to cut video processing times. It automates all functions and reduces hours of video processing and editing times.
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Wrapping up
The challenge of working with a lot of data from various sources has been tackled by big data. Like adding more outside data sources, sharing data, querying data, and visualizing and storing data. Many analytics methods focus on big data, and there are measures to improve forecasts emanated from big data powered by AI.
Data is a significant development and an opportunity to watch and leverage. However, the trend to add too much data to AI can cause the quality of the AI decision to suffer.
So it is crucial to take the benefits from data and analytics to prepare your Data for AI and ensure and measure the quality, but don’t get carried away by adding data or complexity to your AI projects.
Most AI projects are mainly narrow artificial intelligence projects and do not require big data to provide value. They need a good quality of data and a large number of records.
