In this article, we look at the differences between structured and unstructured data and how to manage best and analyze them to ensure organizational prosperity. Computers are better able to process structured data than unstructured data. But how do humans analyze unstructured data? How can organizations benefit from the use of unstructured data? We discuss the pros and cons of structured vs unstructured data and how to handle them effectively in an increasingly competitive world.
Structured data is easier to search through and process by computers
Typically, structured data is stored in relational databases, such as SQL Server, PostgreSQL, or MariaDB. These systems are available in free, open-source, and licensed flavors. Depending on the data you’re storing, you might want to use a Relational Database or a Data Warehouse. The latter is an efficient way to store structured data from multiple sources.
Compared to unstructured data, structured data is easier to sort and process by computers. Because it’s organized in columns and rows, structured data is much easier to search and process. This type of data is commonly stored in databases such as CRM platforms, association management systems, and sales information. It’s easy to store and analyze structured data, which makes it easier for programs to use. For example, a payroll database will contain an employee’s name, pay rate, hours worked, and compensation delivery.
As mentioned, structured data is easier to search through and process by a computer. The data is structured in relational databases (RDBMSs). This means that the information is stored in tables with rows and columns, which are more organized and more easily accessible. The tables and columns are often tabular, with columns defining various attributes. Structured databases use a structured query language called SQL to manage them.
Unstructured data is more accessible to analyze by humans
In the past, organizations could not utilize the potential of unstructured data and limited themselves to structured data. Today, companies can use machine learning opportunities and artificial intelligence to gain valuable insights from data. For example, Google is making significant strides in image recognition, allowing it to identify objects in photos and draw insights from that data. However, gathering unstructured data can be challenging, and human analysis requires specific tools and processes.
Structured data consists of information that follows a clear, logical pattern and can be searched using a database tool. Unstructured data is not as easily searchable and cannot be categorized by human analysts. The latter, on the other hand, is more difficult for humans to analyze. Data in this format is often in the form of text files, social media posts, satellite images, sensor data, and unstructured business transactions.
Traditional analytics tools cannot parse unstructured data because it lacks any predefined structure. Unstructured data comes from a variety of sources, including social media platforms such as Facebook and Twitter, communications such as phone recordings, collaboration software, digital photos, and business applications like MS Office documents. In addition, machines can generate unstructured data, including scientific and surveillance data and other data.
Managing and analyzing unstructured data can help organizations prosper in highly competitive environments
Managing and analyzing unstructured data can be a powerful way to generate valuable marketing intelligence for an organization. By combining data from various sources, companies can quickly scan enormous datasets and uncover trends and patterns in customer behavior. This can help organizations determine which products and marketing initiatives are worth pursuing. In addition, as a valuable business asset, unstructured data can provide valuable insights into customer behavior and help the organization thrive in competitive environments.
Today, the majority of data generated by businesses is unstructured. Managing and analyzing unstructured data is essential for companies to succeed. By harnessing this data, organizations can use it to make vital decisions and achieve. The challenge is that unstructured data can be very difficult to analyze manually. Therefore, managing and analyzing unstructured data is essential for organizations to stay competitive and profitable.
Businesses must first determine what the data is to manage unstructured data effectively. For example, email messages are unstructured, but the metadata that comes with them provides structure. Text files are also unstructured and include word processing documents, spreadsheets, presentations, log files, social media and websites, photo sharing sites, and email. Other data types include mobile and communications data (such as phone recordings). Collaboration software includes Instant Messaging and Chat. Media data includes digital photos and video. Scientific data includes satellite imagery, seismic data, and military movements.
