Financial services sector fails to optimise the use of unstructured data

In most banks and financial institutions it has been common practice to use conventional tools and practices to analyse data.

The problem with this is that it restricts institutions to the use of structured data. This means that professionals like CEOs, CFOs and FDs could be missing out on the vital insights that the use of unstructured data could bring.

It’s estimated that around 80% of unstructured data is not used by businesses and organisations. It’s important that this waste of a resource is addressed.

The difference between structured and unstructured data

For financial professionals to understand the full value of unstructured data, it’s important to have a detailed understanding of the difference between structured and unstructured data.

Structured Data 

Data that is structured represents the easiest task when it comes to analysis. This is due to the fact that it can be stored in organised fields, in the rows and columns of databases and spreadsheets. Traditionally, this is the data that is most often used during analysis in the world of finance.

Unstructured Data

Unstructured data does not lend itself to being stored on a spreadsheet. For instance, it could be the content of an email or text message. It is most often text based which means it can be ambiguous. This means that this type of data has traditionally been a lot harder to manage and analyse.

How to analyse unstructured data

If financial professionals are to make better use of the unstructured data that is available to them, they need to understand how it can be analysed effectively.

In many cases, extraction, text analysis and text abstraction with a relational database are used in order to analyse this type of data. The growth in the use of artificial intelligence and machine learning algorithms is also making it easy for accurate analysis to be completed.

The challenge to the world of finance

The financial world is steeped in tradition. This means that it can be difficult for banks and other financial institutions to make changes to their existing infrastructure and processes. This reluctance to change is damaging to the finance industry’s ability to utilise all of the data it has at its disposal to optimise its potential performance.

It’s vital to the future success of the financial services industry that smart analytic tools and machine learning are employed in order to obtain the full potential of all available data; both structured and unstructured. There are several benefits that this can provide for the financial services industry including developing a more rounded and in-depth view of the markets and being able to identify financial opportunities earlier.

Currently, the financial services industry still has a long way to go, when it comes to the management and analysis of unstructured data. However, financial professionals are beginning to understand the value of unstructured data and why the optimisation of its use is a vital goal.

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