Tue, 29 Nov 2022

Not all data should be treated equally. In fact, most data is actually vastly different. Depending on whether data is structured or unstructured, the storage formats, collection techniques, and steps to prepare said data will vary.

In order to more effectively conduct business analysis, companies must understand where their data comes from and how they should deal with it. The first step in this pipeline is distinguishing whether that data is structured or unstructured.

In this article, we'll turn toward the industry developments around structured and unstructured data. We'll trace what these data formats mean, and explain how businesses are changing the way they approach these distinct categories. Let's get right into iot.

What is Structured Data?

As the title of this data format suggests, structured data is anything that already fits into a predefined format. From the moment a company collects this data, it is already formatted in precise fields that can then easily be analyzed.

Structured data related directly to SQL - structured query language - as this language can easily navigate and ask specific questions of formatted data. Due to its formal rigidity, structure data comes with a range of benefits:

  • Data Analysis - As mentioned above, structured data has historically been the go-to format for data analysis. Due to the ease of using SQL, many data tools are geared toward using and analyzing structured data. Most of the time, the categories that data is already built using will naturally lead into effective analysis.
  • Machine Learning - Structured data is incredibly logical, making it perfect for analysis by AI. Equally, machine learning tools can quickly and easily work out a strategy for approaching, summarizing, and understanding the logic behind this data. This allows businesses to automate much of their handling of structured data, working well with embedded analysis or live data tools.
  • Simple - Structured data is in a format that absolutely anyone can understand. With clear documentation and, well, structure, this is always an intuitive data set. Considering its simplicity, teams beyond just data engineering can access, view, and draw meaning from their data.

Structured data is a wonderful tool that allows businesses to rapidly produce insight.

What is Unstructured Data?

Unstructured data is any information that's collected in its own native context and format. This means that instead of having data that's in neat and categorized rows, unstructured data could be absolutely anything.

On one hand, unstructured data could be pictures or presentation slides; on the other, it could be results from natural language processing analysis on social media. Considering the sheer amount of social channels that businesses operate over, unstructured data is absolutely everywhere.

Although the format can be complicated, there is actually a range of benefits to using unstructured data:

  • Collection Speeds - As unstructured data is collected in its native format, this type of data is incredibly easy to find and collect. You can accumulate data in whatever form it's currently in, not making it undergo any changes before being stored. This level of collection convenience allows businesses to collect more data in a shorter period of time.
  • Flexibility - As unstructured data doesn't necessarily follow any particular structure, businesses have a lot more variety when it comes to data collection. Structure, although helpful, is also a form of restriction. Removing the need for structure allows companies to draw upon raw data about whatever they'd like.
  • Storage - Most of the time, companies will store unstructured data in data lakes. With a range of online options and scaling price plans, cloud data lakes offer a great solution for collecting large quantities of data at once. The sheer quantity of data that can be collected in an unstructured format allows businesses to produce high-quality insights.

While unstructured data does require more expertise to handle - or just the correct tools - turning to it is one of the most effective ways of producing better analysis for your business.

What Happens if My Business Needs to Deal in Both Structured and Unstructured Data?

Your business is definitely not alone. A business cannot focus on only one format of data, as they would be closing off their analysis pool to a truly representative sample. A balance of structured and unstructured data is vital.

Using a Delta Lake helps businesses manage and contain both structured and structured data. Data engineers can easily use Apache Spark to analyze structured data, but run into problems when doing the same for unstructured data. Instead of spending lots of money converting all of their unstructured data into structured data, companies often use a Delta Lake on Data Bricks to create a single home for their data. All data, semi-structured, structured, and structured, can be hosted in Delta Lake.

Modern solutions like that provided by Apache Spark ensure that businesses have unbridled access to their data, helping to increase analysis speed and ensure scalability. With this, businesses are able to manage both unstructured and structured data simultaneously.

How Is Data Collection and Processing Changing?

Structured data is undoubtedly much easier to analyze and draw meaning from. By simply using SQL, data engineers can pose certain questions and almost instantly get a range of responses. But, over 90% of data that a business collects is unstructured, meaning that companies need to adapt.

We cannot spend time continually structuring data, using company resources to turn data into a more manageable format. Due to the sheer quantity of unstructured data that's currently being siphoned from online sources to a business, we need to adopt a more proactive approach to data.

Using data - in all of its formats - is vital to continue growing in our data-driven world. If businesses are unable to manage unstructured data just as efficiently as its structured data, then they'll fall behind. Adaptability and flexibility win out over structure and order.

Final Thoughts

It's not an industry secret that businesses now thrive on data-driven decision-making. Companies that haven't adapted to these strategies have already fallen behind, with data bringing more revenue, better customer experiences, and better product development.

Yet, the source of that data has also begun to change. While precise insights from structured data were once the go-to solution, this is slowly starting to shift. With the quantity of data that businesses receive that's in an unstructured format, learning how to deal with different data sources and sets simultaneously has become vital.

Businesses that can rise to the challenge of handling both their unstructured and structured data will further accelerate their rise to success.

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