Making Unstructured Data Usable – This Is How It Works!

Numbers and data sets don’t even cover half of an organization’s knowledge. Companies can gain new insights and make better decisions by looking at their entire database.

Today, data accumulates in such gigantic amounts that it should be used extensively to gain better insights into business processes. Word of this has long since got around in companies. But while the idea is easy to understand, it can be challenging to execute. There are many reasons for this: insufficient or insufficiently qualified personnel for data analysis, inadequate toolsets and incorrect assumptions.

One of the biggest obstacles is that not all of the data is viewed and understood. It is undoubtedly tempting to create data warehouses from existing databases and use the resulting information for analysis. The problem with this approach is that it relies too heavily on structured data. Unstructured data such as B. in emails, collaboration tools such as Microsoft Teams and documents are usually ignored. However, this severely affects the accuracy and effectiveness of the data analysis process.

What Is Unstructured Data?

To compare structured data with unstructured data, one must first understand their different natures. Structured data includes numbers or text that fit into a relational database management system (RDBMS) such as Oracle or Microsoft SQL Server. They take the form of rows and columns in a database: names and addresses, demographic statistics, smartphone locations, and so on.

Structured data is easy to edit and search through, but it makes up only one-fifth of all data in a company. By far, the most significant part is the unstructured data. This is understood to mean all information that does not fit into an RDBMS because it does not have the uniformity of structured data. They can be found in PDFs, Office documents, PPT presentations, email threads or social media posts. These are text and numbers or videos, sounds and images that are not arranged according to a line and column scheme.

Where The Brand Mood Is Hidden

They are more challenging to capture, process, search and analyze than their structured counterparts, yet they must not be ignored. Not because of their sheer majority, and above all, because they hide valuable values ​​that are not immediately recognizable. Much of what marketers call “brand sentiment” is hidden in unstructured data.

Problems in customer loyalty can perhaps be identified from structured data records in CRM systems or sales statistics. If customers are placing fewer reorders, it could be an indication of a brand sentiment issue. However, negative brand sentiment can be identified much better from an analysis of social media posts. If nine out of ten comments say, “This product is terrible,” act immediately. To recognize such moods, one must be able to analyze unstructured data.

Another compelling reason is data classification. This means the identification and subsequent labelling of data using categories such as “intellectual property”, “confidential”, or “personally identifiable information (PII)”. Data classification is fundamental to data security and compliance. After all, it is impossible to effectively protect data without knowing where and what it is or means.

The purpose of data security programs is to protect a company’s “crown jewels”: its most valuable and sensitive information. To know what counts, you first have to look at all possible data sets and identify which parts belong in this highly protected classification. Doing this correctly means examining unstructured data as well.

Unstructured Data: Only What Is Classified Can Be Adequately Protected

For example, a company might attach great importance to protecting its patents. That sounds simple, but what if information that supports patent filing is spread across the company? Documents dormant in file drives and cloud storage could contain rich intellectual property such as technical drawings and research reports. They must not fall into the wrong hands but are vulnerable to unstructured data. To protect it, you have to analyze the data and find where the intellectual property is hidden. It must then be classified as such to be able to defend it at all adequately.

Compliance is another use case. Regulations such as HIPAA or GDPR, which aim to protect personal data, require the analysis of unstructured data. For example, PII data can easily be included in email messages and attachments that may be contained therein, such as PDF documents. If you do not know that this data is available, you cannot protect it against data breaches or unauthorized access and therefore run the risk of significant financial penalties.

Natural Language Processing Recognizes Nuances

The best way to find and analyze unstructured data is with an enterprise search solution. Their crawlers search the content of Microsoft Office documents, PDFs, email servers and every other source of unstructured data in the company. They feed the data back to the search engine, creating a searchable index of the unstructured and structured data. It can then use built-in functionality or third-party tools to add data classifications to the unstructured data indexed. The use of Natural Language Processing (NLP) functions, i.e. the ability of a computer program to understand human language as it was spoken or written, helps. A good NLP solution recognizes nuances in unstructured data,

Unstructured data is an essential part of a company’s data analysis strategy. You should also play a crucial role in data security and compliance efforts because the consequences of not doing so can be severe. Modern enterprise search solutions help to discover, classify and analyze unstructured data. They should therefore be part of the standard equipment of a company today.