Data is an indispensable part of modern technology, enabling businesses to read customer behavior and make changes that improve productivity and boost profitability. However, while we all would agree that data is indispensable, not all data is the same and it can be easy for amateurs to discount or overlook certain types of data simply because the insights are more difficult to unlock.

Enter natural language

Some experts believe that natural language is structured data (which can be stored and manipulated to some degree in like manner to numerical data sets), while others abide by the more widely accepted norm that it falls within the unstructured category. So which is true?  How easy is it to unlock the relationships between natural language data sets?  Are amateurs right in simply ignoring natural language data?

The following information seeks to answer this critical query as best as possible while offering a brief albeit pivotal outline of the significant data types.

What they mean

The multi-billion-dollar global NLP market is expected to increase manifold in the next couple of years, making it one of the largest revenue-generators in recent times. This technology is critical to all businesses as it helps process vast amounts of data.

But as mentioned above, data is primarily of two types, structured and unstructured, with recent mentions of semi-structured data in the market.

Structured data is remarkably organized and interpretable and is typically recognized as quantitative information. Its simplicity makes it easy for machine learning algorithms and amateur users to quickly access and interpret it with only a basic understanding of the relevant topic.

Unstructured data, in contrast, is generally recognized as qualitative information that one cannot quickly process or interpret with ordinary (or traditional) methods. Examples of this variant include social media posts, texts, and mobile activity. Research indicates that almost eighty percent of all enterprise data is unstructured, making it critical for businesses to invest in managing this information above other data.

artificial intelligence

Which is better for business?

The importance of both these variants cannot be undermined as they are both pivotal to an organization for different purposes. For instance, structured data is run through CRM software to help companies study consumer behavior in industries like ticket sales and accounting. While unstructured data can do the same, it can also aid in predictive analytics, enabling businesses to predict future activity and plan accordingly (i.e. introducing and optimizing chatbots).

Natural language could well be structured

Natural Language Processing (or NLP) is a crucial computation tool that helps technically process human language found in essential business records, such as texts and emails. Invariably, it is a powerful tool that helps extract unstructured data, ultimately assisting businesses in cutting costs of manually maintaining data records.

Several experts believe that natural language is structured data, citing that the human language is quite structured. As per the belief, there has been a very structural evolution of natural language, with humans quickly learning to include sound, tone, pitch, and other critical patterns to include more vital information in language. Furthermore, though not all of the textual data present worldwide incorporates natural language, textual data that does incorporate natural language can provide a wealth of insight for businesses.  Graph databases, for example, are able to display textual patterns based on how words are related and interconnected to other textual concepts within a business and the greater marketplace.

Hire a consultancy for expert guidance

While it is recommended to read up at least a little on natural language patterns and the effectiveness of graph databases, experienced consultants can enable you to learn and implement graph databases (such as Neo4j) effectively, helping you unlock the insights you need to scale your business faster.

When looking for a consultant, look for companies that do not overlook the insights available from the structure of natural language. Look for experts in the critical tools to help you manage your business data efficiently.

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