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Advanced Analytics

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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
Clustering


Cohort analysis: Looks at the behavior of a group of people to draw broad insights
Cohort 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).
Complex Event Analysis


Data mining: Identifies sequences, relationships, and outliers across large datasets, which can be used to assess opportunity and risk
Data Mining


Machine learning: Finds complex patterns and produces accurate predictions that can be used in personalization, fraud detection, and micro-segmentation
Machine Learning


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
Predictive Analytics

How Advanced Analytics Works

Advanced analytics is applicable to every industry and can be used across every business function within an organization. Examples can be seen in the graphic below.

Advanced Analytics Benefits LOB

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.

Marketing

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.


Getting Started With Advanced Analytics

The Alteryx Analytic Process Automation (APA) Platform™ offers an accessible platform featuring both no-code, low-code building blocks and an easy-to-understand visual platform. The APA Platform 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
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