Our world is filled with more types of data that can be used for an infinite array of human possibilities. You might have heard about the differences between structured and unstructured data, but what exactly does this mean? What are the advantages and disadvantages? Moreover, how can we extract insights from each of these types of data?
In this article, we will dive deeper into both types, dig the main differences between structured and unstructured data, including data collection practices, how organizing data differs depending on the data structure, etc.
There are several things you need to understand when it comes to comparing structured vs. unstructured data. This guide has you covered.
Big data is a buzzword not only for FLAGG companies. Data science is revolutionizing the world and bringing a vast amount of information into small units for analysis.
The first thing that you need to know is that not all data is created equal.
There are different types of data available depending on the source or system that created it. Some of this data is structured, however most of it is unstructured. This has major implications for how the data is collected and processed.
Structured data is most analogous to quantitative data. It has been predefined in a systematic structure before being stored to the cloud or on-premise.
Structured data can easily be subdivided into columns and tables. Users are able to quickly place any type of structure data into a spreadsheet for analysis, including names, dates, stock information, location, or payment information, etc.
The traditional programming language that has been used to analyze structured data for decades is called structured query language, also known as SQL. This language was created in the 1970s by IBM and has been used for many years as a primary source of data analysis.
When handling structured data, you will often need to use machine language or an analysis tool to get valuable information. The benefit is that this information can be easily extracted with some basic programming commands.
Structured data has traditionally been the type of data that businesses use for insights. However many businesses are now shifting towards deconstructing this information into unstructured data for use in future opportunities.
Unstructured data is more analogous to qualitative data, that it cannot be analyzed and processed in the same way as structured data using machine language.
Some examples include video files, audio files, mobile search activity, satellite imagery, or social media posts. This is an endless list but allows you to see the complex types of information that companies are becoming interested in.
The challenge is that unstructured data is difficult to deconstruct since no predefined model that can be used to neatly sort this information together in a single place for analysis.
However, developers still options for analyzing this type of data. One common technique is to use a non-relational database. You can also have the data flow into a data lake, which keeps it in its raw unstructured style.
Nonetheless, trying to find insights with what appears as unstandardized information is challenging. It requires a personalized and high level of analytics. This can be challenging and more expensive for many companies.
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However, companies that are able to use unstructured data for their analysis are at a competitive advantage. They help to drive insights that are unique and novel, which can inform important decision-making.
AI and machine learning processes are becoming commonplace for handling this difficult task. AI development is the future of handling unstructured data. Companies that jump on the bandwagon early can set themselves up for success.
There are also other types of data such as semistructured data and metadata. Semistructured data lies somewhere in between structured and unstructured data.
Oftentimes there is some component of the data that is identifiable in a spreadsheet. However, attached to that piece of information is another form of unstructured data. This typically makes storage of this information easy, but in cases, analysis remains difficult.
Metadata is a type of master data that is often used in big data analysis. It helps to describe other types of data.
It usually involves preset fields that help to provide additional information about individual data sets within it. One example of this might be an online article that has metadata that includes the headlines in the article, or any bullet points.
As you can see, there are different challenges in handling structured vs. unstructured data. Both will require a distinctive approach, yet they can provide unique insights to any analyst.
We believe that AI and machine learning tools are the future of handling unstructured data. If you are interested in learning how Graviti can accelerate your analysis and give you competitive insights, please contact us today.