How Auto Insights Uses Advanced Analytics, AI, and Machine Learning
A lot of people ask us what parts of Auto Insights use advanced analytics, AI, or Machine Learning. This article covers everything about Auto Insights' core capabilities.
How Auto Insights Works
The 5 main features make up Auto Insights' core capability:
Missions: Auto Insights' ability to build a curated list of insights that reflect your business objectives.
Playbooks: Playbooks allow you to easily identify analytical use cases and customize them to your specific company, role, and business problem using artificial intelligence.
Pencil: Auto Insights' data contextualization algorithm. This is where data is uploaded, analyzed, and contextualized by Auto Insights.
Search: Auto Insights' gateway to explore your data and uncover answers to questions you didn't know to ask.
What Caused This?: Auto Insights' ability to determine which data points are most significant.
Analytics | ||
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Data Contextualization | Pattern Analysis | Guided Journey |
A collection of algorithms:
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Auto Insights Features | ||
Pencil | Search Missions | Missions Playbooks What Caused This |
What is Data Contextualization?
Raw data is just numbers, and often it does not make sense to people. Humans have a world model in which we operate. If a person sees data with columns named Country and City, they automatically know that both columns are about places and City has a hierarchical relationship with Country. Data in its raw form does not have this ontology and taxonomy.
Auto Insights uses data contextualization to build models over the data, allowing for it to understand the data from various aspects such as:
Data quality: Does the data look clean? Clean meaning does it contain missing values? If some columns have a high percentage of missing values, Auto Insights calls this out and provides its recommendation (to remove).
Natural language: Data readiness for natural language: Does the data contain natural language materials or is it all codes that are unfriendly to business users? If the data contains a lot of codes, Auto Insights calls this out to the user and suggests either renaming or removing the columns in question.
Relationships: Auto Insights understands correlations and relationships that exist between columns in the dataset.
Like a human, it uses the information in decision-making, later on, to answer questions, guide users to value, and build stories from the data.
What is Pattern Analysis?
Two areas of Auto Insights use pattern analysis to provide insights to users—Search and Missions.
Search and Missions: When you select a measure such as sales, and break it down by department, Auto Insights looks at the trend for each department and can call out patterns among them. For example, do they perform differently, or there are groups of departments that share the same pattern?
What is a Guided Journey?
Creating a Guided Journey in Auto Insights allows non-technical users to get relevant insights quickly and explore their data easily. Three areas of Auto Insights Auto Insights use advanced analytics to create a guided journey for users—Playbooks, What Caused This? and Mission Templates.
What Caused This?—Auto Insights uses models to understand what data points are most important or sensitive, based on what’s in your data. However, it’s important to note that Auto Insights can’t make inferences—the drivers have to be in your data for Auto Insights to be able to analyze them.
Mission Templates—Auto Insights uses the raw data uploaded via pencil in conjunction with models to automatically generate a Mission layout highlighting the key measures or segments for your data, sorted by relevance.
Playbooks—Auto Insights uses the input provided (role, company, or business problem) to generate use cases and create Missions based on those use cases.