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By Anwesha Pattnayak
Artificial intelligence is augmenting the entire analytics lifecycle. AI can deliver deeper, more meaningful insights, provide natural language interaction and automate manual process by making use of innovative capabilities such as machine learning, large language models like OpenAI’s ChatGPT etc.
Let us dive into the basics and explore typical scenarios that illustrate the pivotal role of AI in the world of analytics.
What is Data Analytics?
It involves the examination of raw data to extract valuable and actionable insights. This practice falls under the umbrella of business intelligence, empowering organisations to make informed decisions guided by the insights derived from the data.
What is Artificial Intelligence?
Artificial intelligence is a technology designed to imitate the human mind, particularly in areas such as analysis and learning. It is designed to understand concepts, draw conclusions on data, become self-learning and even interact with humans. AI systems work by ingesting large amount of labelled training data, analysing the data for correlations and patterns, and using these patterns to make predictions about future states.
How AI changes analytics
AI-driven software has the capability to autonomously analyse data from its source and provide valuable insights. Employing AI-guided systems in your data operations allows for the automatic cleansing, analysis, interpretation, and visualisation of data.
Artificial intelligence software equipped with machine learning leads to minimal human intervention. By supplying the initial training data, encompassing machine learning algorithms and labelled text samples, AI tools can autonomously acquire knowledge from the data.
Benefits of AI-driven Analytics
Greater Productivity: The speed and scope of AI free workers from the laborious and time-consuming task of analysing data themselves.
Pattern Recognition: AI analytics can recognise and extract useful information from enormous amounts of data, even across multiple platforms. A combination of AI and ML (Machine Learning) can spot trends, patterns and improve your business insights and predictions.
Flexibility: Machine learning algorithms mean that AI analytics can constantly learn and adapt.
Challenges with AI-driven analytics
Expensive: AI analytics is an investment in terms of both time and resources. The adoption of AI analytics may also require upgrading your existing systems to meet the necessary prerequisites.
Prone to Error: While AI analytics reduces the chances of human error, it does not eliminate the possibility of mistakes. Human involvement remains crucial in AI analytics, leaving room for user errors. Additionally, design errors can occur from the quality of training data, inadequacy, or inaccuracies.
Lack of Regulation in Some Sectors: AI regulations are slowly being introduced around the world. For many industries, there is still a lack of regulations. It is up to companies to keep up the pace with regulations and ensure their AI analytics systems are in line with the evolving regulatory framework in their industry.
Common Use Cases of AI
Process large volumes quickly: AI helps find insights and patterns in large dataset that cannot be seen manually.
Forecast based data trends: AI also made it possible to create systems which can predict the outcomes and courses of action.
Use data from multiple sources: AI can consolidate vast amounts of data from various platforms. It makes use of its efficiency, speed and scale to pull together all your customer data into a single, unified view.
Wide range of applications: AI data analysis can go beyond the simple analysis of quantitative data and tackle data for predictive analytics for a more comprehensive approach.
Glimpse of AI in future
AI has become an everyday component of life. The technology behind AI continues to evolve at a fast pace and I believe this will only accelerate in the future. We are now entering a new era defined by technology and artificial intelligence: the Fourth Revolution as mentioned by many. Perhaps, this will be the start of a new partnership between humans and machines where technology will facilitate the process and human will drive the cause. We will have the freedom to build new ways of working that promote our best qualities getting freed from repetitive tasks.
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