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Data Structure Basics

Data Structuring
Unstructured Data makes up the bulk of the world’s Data and businesses are overflowing with it: emails, papers, photographs, presentations and even handwritten notes.

A barrier in successful Data structure is the range of formats in which data is kept.

By structuring and storing data using AI/ML you may automate reporting, analysis and compliance procedures, streamlining your company processes and allowing you to make better educated business choices faster.

This has a particularly big influence on industries with a lot of Unstructured Data and a lot of bureaucratic laws and processes, like:

Banking and Financial Services

Banking and Financial Services

Insurance and Legal Services

Insurance and Legal Services

Healthcare

Healthcare

Mobility

Mobility

Advantage of Automated Data Digitization and Structuring
Automated Data Digitization and structuring
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How Can Data Digitization and Structuring Be Automated

Here are a few things to think about while Automating your Data structure operations

Data Gathering

Data Gathering

A Data lake collects and stores data from paper-based and Unstructured digital documents (emails, presentations, PDFs).

Pre-processing of data

Pre-processing of Data

The Data is converted into a format that may be used to train a model. Data annotation, cleansing, and organizing are examples of pre-processing.

Training of Model

Training of Model

A model is taught to recognize certain keywords associated with a particular use case. (e.g., medical and financial) and learns standard formatting as an output.

data output

Output

The model can take Unstructured Data and organise it into a consistent and ordered format.

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