What Is Advanced Analytics?

Advanced analytics uses sophisticated techniques such as multivariate statistics, data mining, machine learning, visualization, simulation, text mining, graph (network) analytics, forecasting, optimization and simulation to uncover insights, identify patterns, predict outcomes, and generate recommendations.

Why Is Advanced Analytics Important?

To accelerate innovation and outflank competition, companies make use of advanced analytics to generate predictive insights, and make better, more informed decisions faster. Advanced analytics are used to optimize and improve business operations, reduce risks, and personalize customer experiences.

Advanced analytics can solve problems that BI reporting can’t and can be applied to different cases such as monitoring and evaluating social media, predicting machine failures, and forecasting supply and demand, dynamically adjusting prices, detecting fraud, customer attrition, and many more.

Advanced Analytics Techniques

Techniques used in advanced analytics dive deeper than BI or descriptive analytics. While BI focuses on historical, structured data from various sources, advanced analytics tackles both structured and unstructured data from disparate sources. BI usually yields a summary of past performance, while advanced analytics looks to the future to help optimize and innovate in the present. To do so, advanced analytics employs, as the name implies, advanced techniques such as:


Clustering: Groups things together to easily recognize similarities and differences in a dataset, which makes it easier to make comparisons
Cohort Analysis
Cohort analysis: Looks at the behavior of a group of people to draw broad insights
Complex Event Analysis
Complex event analysis: Provides real-time insight by analyzing event data from various sources and pointing out cause-and-effect relationships. Also known as complex event processing (CEP).
Data Mining
Data mining: Identifies sequences, relationships, and outliers across large datasets, which can be used to assess opportunity and risk
Machine Learning
Machine learning: Finds complex patterns and produces accurate predictions that can be used in personalization, fraud detection, and micro-segmentation
Predictive Analytics
Predictive analytics: Predictions about business outcomes based on historical data, statistical modeling, and machine learning
Predictive Analytics
Retention analysis: Used to understand user/customer cohorts, which help determine retention factors and growth strategies

How Advanced Analytics Works

Advanced analytics is applicable to every industry and can be
used across every business function within an organization.

Business Operations

In a fast-paced world, businesses need to be able to react quickly. With advanced analytics, a company can make decisions based on accurate predictions, which can improve performance and productivity and increase revenue.

Human Resources

Advanced analytics can harness HR data for good by helping reduce the costs of recruiting and hiring, decrease turnover, and maintain/increase overall employee satisfaction.

Manufacturing and Inventory

Demand, preferences, and cost are constantly changing, which impact what products get made, sold, and distributed — and how. Advanced analytics can help to prevent machine failure, reduce irrelevant stock, expedite ordering, and lower distribution costs.


Understanding customers is key to predicting how they’ll behave in the future. Advanced analytics can help create personalized marketing experiences and identify sales opportunities.

Risk Analysis

Managing large datasets in real time can help detect fraud, monitor customer reputation, and reduce future risk.

Business Intelligence

After collecting and analyzing data, organizations present their insights to others as business intelligence (BI). You can display BI using reports, dashboards, and other data visualizations to make it easier to understand. This information helps decision-makers make informed, data-driven decisions that solve business problems, drive growth, and provide a competitive advantage.

Traditional Business Intelligence vs. Modern Business Intelligence

Whereas traditional BI focuses mainly on historical data, modern BI incorporates machine learning, data science, and predictive analytics. This modern data analysis approach allows organizations to combine historical and real-time data for forecasting and predictive modeling. The most important benefits of these features are increased accuracy and organizational agility in decision-making. This information can help decision-makers understand potential future outcomes before they make decisions.

Benefits of Modern BI

  • Actionable Insights: Modern BI delivers insights that help decision-makers understand the impact of each potential decision. When BI is enriched with insights from machine learning and data science, it can even highlight the best choice to make
  • Identifying Trends and Patterns: BI provides decision-makers with valuable insights into how internal and external factors may affect their business. These metrics can highlight how internal decisions, customer demand, and market shifts led to increases or decreases in sales, marketing, finance, revenue, and more.
  • Auto Insights: The increase in machine learning and AI has led to self-service reporting tools and platforms. These newer reporting services can automatically surface insights plus find answers based on user queries. For example, they can highlight which factors led to sales increasing (or decreasing).

Data Science

The primary purpose of data science is to find patterns and correlations in data. For example, a data scientist may help an organization find a correlation between a specific price range and an increase in sales to help decision-makers determine product pricing.

When analyzing data, data scientists will use algorithms that perform cluster analysis, natural language processing (NLP), and sentiment analysis to explore data. These techniques can help provide deeper insights and a deeper understanding of nuanced data.

  • Cluster Analysis: Used to group similar data points together to find correlations
  • NLP: Processes and analyzes human language to find patterns and relationships
  • Sentiment Analysis: Uses NLP to understand the opinions and emotions behind text

Artificial intelligence (AI), machine learning (ML), and deep learning are also part of data science, and aid in pattern matching.

Data scientists often use AI to increase accuracy and improve the results of data science projects. AI is also used to perform ML and serves as the brain. Both contribute to deep learning, a subset of ML, which is more adept at classifying data and identifying patterns. Data scientists use deep learning models to help machines identify and analyze images, text, and speech.

Because data science can easily and accurately detect patterns by combining multiple data sources, organizations often use it to detect anomalies, identify potential issues, and predict outcomes.

In previous years, an understanding of programming languages and math, such as statistics, was needed to perform data science. However, modern analytics platforms can now eliminate the need for people to know how to code to execute ML models using AI for data science.

Use Cases for Data Science

  • Finance: Detecting potential fraud in transactions and alerting decision-makers so they can evaluate
  • Supply Chain: Predicting customer behavior, including what factors make people more likely to purchase products
  • Healthcare: Identifying potential health issues and helping medical professionals assess and diagnose

Getting Started With Advanced Analytics Tools

The Alteryx Analytics Automation Platform  offers both Machine Learning and an Intelligence Suite with computer vision and text mining on an accessible platform featuring both no-code, low-code building blocks and an easy-to-understand visual platform. Alteryx integrates advanced analytics into data preparation, blending, analysis, and enrichment using:

  • A/B testing
  • Computer vision
  • Clustering and segmentation
  • Decision trees and random forests
  • Demographic and behavior analysis
  • Machine learning
  • Multivariate statistics
  • Optimization and simulation
  • Forecasting and time-series
  • Network analytics
  • Neural networks
  • Predictive and prescriptive analytics
  • Regression
  • Spatial analytics
  • Supervised predictive models
  • Text mining