Understanding data is an important part of running any business. Typically, analysis is performed on data that is stored in forms, spreadsheets, and relational databases. These are all related to what we know today as structured data. However, constraining data with forms and tables in a modern business environment is no longer sufficient.
The Evolution of Data
While structured data is the most common type of business data to be analyzed, it is not the most common type of information. Structured data represents only 5% to 10% of the information that modern businesses need to deal with on a regular basis.
The majority of the data that most businesses deal with is unstructured data. This type of data is predominantly text and images. The many documents we generate, email messages, photos, and social media posts are all examples of unstructured data.
If you consider structured data as one end of a continuum and unstructured data the other end, then everything in between is semi-structured data. The amount of data characterized as semi-structured is growing based on new tools like machine learning and new formats like JSON. Much of the data that we once considered unstructured is better treated as semi-structured data. Unlike unstructured data, which is difficult to mine for business value, semi-structured data is easier to query and easier to collate, and therefore easier to analyze. In order to generate business value, we need to be able to identify the data sources and organize the data. We need good data to be able to support our ability to make good business decisions. Semi-structured data with a custom data model supports the decision-making process.
Business insights are hidden in semi-structured data
Many businesses are changing their perspective from a focus on a specific set of products or customers to a recognition that they are part of a network of products and services. This change in focus is driving a need for increased business intelligence that is more than what can be derived from internal data sources. The external data sources that explore the marketplace and a business’s position within that marketplace are often in the form of semi-structured data. Analyzing the semi-structured data tends to allow a business to transition from analyzing what was to providing foresight in what needs to be.
Analysis of semi-structured data can also provide significant input to business process management. Business processes are often constrained by limitations imposed by data collection and analysis. When combined with semi-structured data and goal-driven behavior, the business processes can be adapted to markets and even market segments to be more responsive to customer needs and conditions. The more semi-structured data that is available for analysis and more analysis that is done, the greater the refinement of the business processes is possible.
The improved insights gained from the analysis of new data sources like semi-structured data help business leaders to develop more efficient operations and improve the chance of success of strategic initiatives. This leads to new ways to find competitive advantages.
There are a number of factors impacting the need for additional data storage and processing. In the Consumer-to-Business world, there is an ever-increasing use of digital devices being used to connect to a business. This results in an increase in the amount of direct data that can be collected and stored and also provides an opportunity to collect secondary data. Various things, like feedback forms and surveys, give businesses additional focused information. All of this data tends to be semi-structured.
Structured data storage can be done with a relational database management system (RDBMS) for most data. A spreadsheet can be used for simple, one table data. Regardless of which one you use, they require being able to create data models that conform to the table format. As business data grows in size and fluidity, it is becoming increasingly difficult to fit the data into the relational mold.
Learn more About Semi-Structured Data
Using a hybrid cloud data warehouse like Actian Avalanche makes it easy to work with semi-structured data by natively ingesting JSON data and supporting it in a relational database. You can learn more about different applications for semi-structured data by Registering for Actian’s webinar on Gaining Insights from Semi-Structured Data with Bill Inmon on September 16th at 1:00 EST/10:00 PST.