A lot of people ask us what parts of Anna use advanced analytics, AI, or Machine Learning - this article covers everything about Anna’s core capabilities.

How Anna works

There are 5 main features that make up Anna's core capability:

  • Pencil - Anna’s data contextualisation algorithm. This is where data is uploaded, and analysed and contextualised by Anna.

  • Search - Anna’s ability to process natural language into analytics topics

  • Discover - Anna's ability to automatically create insights on what's changed in your data without human input 

  • What Caused This? - Anna's ability to determine which data points are most significant or important 

  • Unexpected Changes - Anna's ability to detect anomalies or unexpected events based on historical trends

These features are built on 4 advanced analytics capabilities. We will cover each of these topics in a little more detail below:

What is data contextualisation? 

Raw data is just numbers and often, it doesn’t make sense to people. Humans have a world model that we operate in. 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 & taxonomy.

Anna uses data contextualisation to build models over the data, allowing for her to understand the data from various aspects such as: 

  • Data quality - Does the data look clean? By clean: does it contain missing values? If some columns contain high percentages of missing values, Anna will call this out and provide her 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, Anna will call this out to the user and suggest either to rename, or to remove the columns in question.

  • Relationships - Anna understands correlations and relationships that exist between columns in the dataset.

Like a human, she uses the information in decision making later on to answer questions, guide users to value, and/or build stories from the data.

What is Natural Language Processing?

The graphic below explains how Anna uses NLP.

What is Pattern Analysis?

There are two areas of Anna which use pattern analysis to provide insights to users - Unexpected Changes and Search.

Unexpected Changes: Pattern analysis is used to detect anomalies or unexpected events from a trend. For datasets that span more than 18 months, Anna is able to perform de-seasonalised analysis as well.

Search: When you ask a question such as sales by department over time, Anna looks at the trend for each department and be able to call out patterns among them e.g. Do they perform differently, or there are groups of departments that share the same pattern?

What is Guided Journey?

Creating a Guided Journey in Anna allows non-technical users to get to relevant insights quickly and explore their data easily. There are two areas of Anna which use advanced analytics to create a guided journey for users:

  • Discover -  Anna uses the raw data uploaded via pencil to automatically generate a report highlighting key changes in the data, what's driving them, and unexpected changes associated with them

  • What Caused This? -  Anna 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 Anna can’t make inferences - the drivers have to be in your data for Anna to be able to analyse them!

If you have any questions, please contact us at support@hyperanna.com.

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