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Many companies today are using advanced analytics to gain insights from multiple sources of data, insights that can be used to make predictions and streamline business processes. The number of companies using artificial intelligence (AI) to analyze massive volumes of data is growing at a fast pace. Companies are using AI to find patterns in data and make predictions. Advanced analytics and AI sound like they are the same thing, but these terms are not synonymous. This post highlights some of the similarities and differences between advanced analytics and AI.
Advanced Analytics is a term that refers to a wide range of analytics tools and techniques which includes data mining, machine learning, forecasting, and pattern matching. Advanced analytics is also a marketing term used to categorize analytics capabilities beyond basic business intelligence (BI) solutions. Advanced analytics covers a wide range of analytics including predictive, prescriptive, and AI-driven.
Artificial intelligence is a set of technologies that simulate human intelligence. AI technologies aim to mimic the human ability to analyze and draw conclusions from data, understand complex concepts, and interact with humans in a human-like way. AI technologies are also trained to process and understand natural language and become self-learning.
All analytics tools require quality data. AI algorithms and models require quality data. Without an appropriate amount of quality data analytics tools and AI models will not make accurate predictions or provide the best insights. So, any data fed to analytics tools and AI models must be collected, cleaned, and normalized. Data scientists spend nearly 80% of their time preparing data for use in models and business systems. Some analytics tools like Zylotech automate data preparation tasks so that data scientists can spend less time on data preparation and more time on core data science tasks.
AI is a subset of advanced analytics, so advanced analytics is broader than AI when it comes to analytics capabilities. While advanced analytics has a broad range of use cases, they all involve some form of analytics. AI also has a broad range of use cases, but AI is not used for analytics use cases only. Other examples of AI use cases include autonomous vehicles, chatbots, and automatic image tagging. AI often involves the development of self-learning algorithms which enable analytics tools to learn from and make assumptions based on data. AI also allows predictions to be made at a scale and speed not possible for human analysts to achieve. And analytics tools would need AI to be self-learning.
We live in a world where data is generated from just about everything- smartphones, cars, utility grids, appliances, wearables, social networking sites, weather satellites, and the list goes on. Companies all over the world are using analytics to discover valuable business insights from many sources of data. Companies using advanced AI-powered analytics will have a competitive advantage over companies that are only using traditional business intelligence tools.
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