It's no secret that businesses have more data than ever before. This data contains a wealth of value with the potential to improve cost savings, redefine processes, and establish a competitive advantage. The biggest barrier? Much of its value is trapped and not easily accessible. Companies keep starving for insights with the growing amounts of unstructured data in documents, images, emails, etc. The sea of data is meaningless unless turned into business value. In this article, we'll cover:
- AI for unstructured data
- Quality and findability of documents
- Extracting fundamental values and data points
- Automating repetitive tasks
- Intelligent document processing (IDP)
- Compliance with legislation
- Next steps
Value of unstructured data
For a long time, getting value from unstructured data has been challenging. Companies could not manually process and analyze the sheer volume of facts, figures, and documents. It was time-consuming, error-prone, and expensive. However, recent advances in innovative technologies have made it possible to automate the processing of vast amounts of data, allowing companies to unleash the full potential of their content cost-efficiently. By applying intelligent technologies for unstructured data management, businesses can benefit in four significant ways:
- Increase the quality and findability of documents
- Extract critical values and data points to structure information and streamline business processes
- Automate manual, time-consuming tasks beyond the capabilities of Robotic Process Automation (RPA) offerings
- Ensure compliance with legislation such as GDPR
Let's discuss each of these benefits in more detail.
Increase the quality and findability of documents.
No matter how valuable or useful your data is, it is useless if no one can find it when needed. The goal is to move from recreating information to reusing it repeatedly. However, for many organizations, the findability of information is still a big challenge. On average, an enterprise with 1000 workers wastes from €2.2 to €3.1 million per year searching for nonexistent information, failing to find existing data, or recreating information that can't be found (Source: IDC). Some companies have already turned their paper documents into digitalized machine-readable text to improve information findability. It's easier to find the correct information in a digital space rather than physically going through each paper file. The digitalization of paper documents is essential but often underestimated first step. If you do this incorrectly, all the files will be digitalized but still challenging to find. Why? Let's start at the very beginning. Once you scan the paper, you'll create a digital document. Usually, it exists only in a non-text format. So, you can read it from the screen, but the computer doesn't recognize any words in it. Optical Character Recognition (OCR) technologies should be applied to convert scanned documents into searchable and editable text files. OCR adds a text layer on the scan, making documents machine-readable. In this case, they are easily retrieved, edited, and searched. But there is a catch. Even the most sophisticated OCR technologies make mistakes and misinterpret characters. As a result, the information you store digitally can be incorrect; thus, it can be challenging to find it back.
