Identify Relevant Data Faster with Data Mining

Identify Relevant Data Faster with Data Mining

26 Jun 2025 3 minutes

In an age of exponentially growing data volumes, analysis methods are more critical than ever. Data mining allows organizations to extract valuable insights from large, often unstructured, datasets. This blog reveals how this technique brings added value to the financial sector when it comes to key financial data.

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In the past, companies had to manually gather and analyze data from various sources to generate insights. Decision-making often relied on limited information and subjective judgment. Today, the landscape has shifted.

Thanks to modern technologies like data mining, vast amounts of data can be automatically analyzed. This enables businesses to identify relevant information faster. But what exactly is data mining?

What Is Data Mining?

Data mining is the process of analyzing large datasets to uncover patterns, trends, and relationships. It combines techniques from statistics and artificial intelligence. Various approaches can be applied depending on the use case.

What Key Data Mining Methods Are There?

Data mining methods are typically divided into two task types:

  • Predictive tasks (classification and forecasting)
  • Descriptive tasks (clustering and association analysis)

Classification is used to predict categorical outcomes – for example, to determine whether a customer is likely to be interested in a specific product based on historical data.

Forecasting involves predicting numerical values. One example would be estimating the electricity consumption of a building for the next year using past data.

Clustering groups similar data objects based on shared characteristics, even without predefined categories. This can be used to segment customers by purchasing behavior.

Association Analysis identifies patterns that frequently occur together in large datasets. For example, discovering that customers who buy laptops often purchase laptop bags as well.

How Is Data Mining Used in the Financial Sector?

In the financial sector, data mining is strategically used to extract insights from unstructured data. A core objective is to improve data quality by cross-referencing information from different sources and verifying its accuracy.

It also enhances data timeliness by enabling the rapid identification and processing of relevant information. Text extraction plays a vital role in this context.

What Is Text Mining?

With the rise of natural language processing (NLP), the focus has shifted increasingly toward text mining. This refers to the extraction of meaningful information from unstructured text or spoken language.

Why Is Text Mining So Relevant to Modern Financial Analysis?

This area is particularly promising for businesses, where much of the data comes in the form of documents. One major use case is extracting key clauses, deadlines, or obligations from contracts automatically. Similarly, important insights can be drawn from customer communications – such as emails or support tickets – to better understand client needs and improve efficiency.

Text mining also helps extract relevant key indicators and content from investor relations documents like annual results or sustainability reports. Another example is the automated processing of risk evaluations from key information documents (KIDs) related to financial products or the observation of business news to extract information relevant in the context of corporate actions.

Discover How SIX Uses Data Mining

SIX has been using data mining technology for years, but with recent improvements in technology, the topic has become more important and transformative in terms of quality and coverage.

Leveraging advanced data mining technologies allows SIX to transform unstructured content from financial reports, news, and websites into structured, actionable insights. SIX empowers financial institutions to make faster, data-driven decisions based on accurate and up-to-date information.

For example, SIX provides corporate actions data in a timely manner by observing business news and extracting relevant information. Subsequently, this information is marked as preliminary and sent to our clients. This has a significant impact because our clients are aware early on of upcoming events.

To create greater value for our clients, SIX will further invest in this technology to provide even better quality, broader coverage, and improved timeliness.

Find Out How SIX Is Extracting Relevant Information to Provide Corporate Actions Data

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