Modern Utilization of Business Information – From Document-Based Data Processing to Structured Information and AI Utilization

Handling, utilizing, and analyzing information are central functions in business. However, a common problem is finding data scattered across various systems and the format of the data once found. If the data is in a document-based format, accurately and cost-effectively utilizing it is often nearly impossible due to the cost of manual labor or the inaccuracies of machine reading. Managing such data is also highly prone to errors due to human involvement. In the future, users should be increasingly provided with automatically generated structured information from various sources for easy utilization.

What is the main difference between document-based and structured information?

We are accustomed to document-based electronic data processing, where we manually extract the desired data elements from a document by copying them. These could be, for example, revenue data, company identification numbers, profit, and expenses. We then transfer these individual data elements to tools like Excel to calculate various metrics and make comparisons. In contrast, when we talk about automatically generated structured information, all the aforementioned processes are done automatically and presented to the user in an easily usable format.

Document-based data also complicates the integration and proper utilization of information. Moreover, document-based data is often not directly compatible with different systems or applications, making data transfer, comparability, and integration complex. From an analytics and reporting perspective, this leads to slow and error-prone decision-making processes.

A good example of document-based data is the financial statements that all companies must prepare and save as PDF documents in a public service. Transitioning from document-based financial statements to structured (database-stored) data offers significant benefits. When financial data is in a structured format, it is more easily integrated, analyzed, and compared.

Structured data also enables automated reporting processes, reducing the need for manual labor and the risk of errors. Additionally, when data from multiple companies is in a standardized, structured format, it can be easily and automatically compared.

Structured and automatically database-integrated financial data offers several significant advantages over traditional PDF-based documents. Below are three examples:

  • Structured data allows for easy and quick comparison and analysis of companies. This makes it possible, for example, to efficiently handle and analyze multiple companies simultaneously. This avoids the traditional method of manually collecting data from different companies, transferring it to an Excel file, and conducting comparisons there.
  • Structured financial data, with pre-calculated metrics, can also be used to enrich target business groups. With this enriched target group, desired companies can be more easily identified from a large pool, for example, for sales purposes.
  • Structured and automatically generated data can also be fed to AI for various analyses, both of individual companies and for comparing companies with each other. To properly utilize AI, systematically organized data is required to produce meaningful analyses.

In summary, the more structured, automatically generated data companies can produce from various operations, the more benefits they can achieve. When this structured data is combined with AI utilization for various analyses, significant business benefits are realized.