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Structured and unstructured. How to deal with different types of data

Automation and data extraction are the areas that are growing rapidly. Just a data extraction business is expected to have almost 5 bln USD valuation with a growth rate of 55-65% per year. The same source is indicating that the ratio between structured and unstructured data is around 1:9 that bringing us to a conclusion that the data ‘market’ is highly unstructured. But how does this affect your business and how can you leverage data by using so-called AI-based tools, including natural language processing?

Let’s jump to definitions. Structured data is clearly defined and in an easily readable format, e.g. excel spreadsheet. It is the most appropriate (or the most effective) source of data for machine learning models, however, also requires the biggest effort as it has to be – usually – manually transferred and structured. When it comes to the unstructured data that consists of around 90% of the whole data around the globe, it becomes a little bit more complicated yet still manageable.

Unstructured data – according to Wikipedia (that at some point can be a reliable source of data) – is the information that either does not have a pre-defined data model or is not organized in a pre-defined manner. According to Seagate the amount of unstructured data may hit 163 zettabytes by 2025 and it is increasing every day as we put more and more data to the web. What data? Movies, photos, and text are the most common examples of unstructured data. Now make an exercise. How much of your data is structured and unstructured? E-mails, commercial and internal contracts, pdf files. How many of them do you have in comparison to well-organized data in spreadsheets?

Ok, having that in mind, how can you handle this data and make it more readable and beneficial for the organization? In previous articles, we were providing your business and non-commercial examples, such as data extraction and processing, automation and classification, and categorization of documents and other data. All of these examples can be done by using AI-based tools, in particular machine learning and natural language processing.

Such tools can learn correlations, contexts, search for certain phrases, and link them to other parts or even documents. More advanced models can even propose summaries and recommendations. The most beneficial here is that everything can be done ‘in shadow’ – at least following the training phase – so you and your co-workers or employees will not be disturbed and will be become much more effective.

[Example] if you are looking for certain information (e.g. clauses in the contracts with counterparties) and all documents that contain such information are unstructured you can either read every document and highlight relevant text or apply the NLP tool to find relevant phrases, extract them from documents and put into one file with relevant additional information, including source and date.

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