Here’s how AI analytics has transformed traditional analytics:
Posted: Wed Jan 22, 2025 10:56 am
Descriptive analytics: Interprets performance based on existing data and focuses on reporting a snapshot summary of the insights. AI analytics gives a more comprehensive report on continuous luxembourg b2b leads performance using trend analysis so brands have a holistic view of the various factors and changes over time.
Diagnostic analytics: It is used to understand a business outcome by analyzing historical data for causation analysis. It identifies relationships in data to understand the root cause of an outcome. AI can analyze large volumes of historical and real-time data from disparate sources to find patterns, trends and anomalies quickly and more efficiently.
Predictive analytics: Used to predict future trends and outcomes based on historical data using statistical models and forecasting techniques. AI uses machine learning and deep learning to predict future trends, providing faster, more accurate forecasts.
Prescriptive analytics: Provides actionable insights from predictive analysis insights to improve business performance going forward. It is a combination of historical data and predictive variables, which can make the suggestions obsolete by the time you put them to use for a campaign. These AI insights enable you to analyze data in real-time by computing the change in variables as they happen so businesses can monitor and pivot campaign strategies promptly.
What are the four pillars of AI Analytics?
AI analytics depends on four critical AI sub-tasks to provide insights—machine learning, natural language processing, neural networks (NNs) and deep learning (DL). These technologies work together to extract meaningful information from raw and disparate data.
Card showcasing the four main pillars of AI analytics that help provide insights from data. These are machine learning, natural language processing, neural networks and deep learning.
Machine learning
Machine learning helps AI analysis tools automatically identify data patterns so they can be synthesized for actionable insights. It assists in processing big data efficiently to identify relevant data points for predictive analysis.
This enables companies to, for instance, automate customer data analysis by processing customer experience feedback, product reviews and social listening data, in near real-time, to get timely insights.
Diagnostic analytics: It is used to understand a business outcome by analyzing historical data for causation analysis. It identifies relationships in data to understand the root cause of an outcome. AI can analyze large volumes of historical and real-time data from disparate sources to find patterns, trends and anomalies quickly and more efficiently.
Predictive analytics: Used to predict future trends and outcomes based on historical data using statistical models and forecasting techniques. AI uses machine learning and deep learning to predict future trends, providing faster, more accurate forecasts.
Prescriptive analytics: Provides actionable insights from predictive analysis insights to improve business performance going forward. It is a combination of historical data and predictive variables, which can make the suggestions obsolete by the time you put them to use for a campaign. These AI insights enable you to analyze data in real-time by computing the change in variables as they happen so businesses can monitor and pivot campaign strategies promptly.
What are the four pillars of AI Analytics?
AI analytics depends on four critical AI sub-tasks to provide insights—machine learning, natural language processing, neural networks (NNs) and deep learning (DL). These technologies work together to extract meaningful information from raw and disparate data.
Card showcasing the four main pillars of AI analytics that help provide insights from data. These are machine learning, natural language processing, neural networks and deep learning.
Machine learning
Machine learning helps AI analysis tools automatically identify data patterns so they can be synthesized for actionable insights. It assists in processing big data efficiently to identify relevant data points for predictive analysis.
This enables companies to, for instance, automate customer data analysis by processing customer experience feedback, product reviews and social listening data, in near real-time, to get timely insights.