The rise of big data has facilitated many new conversations among business professionals in the enterprise. One of these conversations revolves around the two main types of data that businesses collect. They are structured and unstructured data, and they make up the sum of an organization’s data collection.
Both types of data are vital in the modern digital enterprise, but they must be managed differently, and thus, the conversation that clearly defines the role of each data type in the enterprise needs to be had. Data analysts and business stakeholders rely on data to produce actionable insights, so learning the ins and outs of each type of data is paramount to understanding the best practices with which data collection, archiving, and data discovery can be done.
What is structured data?
Structured data is sometimes thought of as traditional data, consisting mainly of text files that include very well-organized information. Structured data is stored inside of a data warehouse where it can be pulled for analysis. Before the era of big data and new, emerging data sources, structured data was what organizations used to make business decisions.
Structured data is both highly-organized and easy to digest, making analytics possible through the use of legacy data mining solutions. More specifically, structured data is made up largely of basic customer data, which includes names, addresses, and contact information. In addition, businesses also collect transaction data as a structured data source, which can consist of financial information which needs to be stored appropriately to meet compliance standards.
Structured data is largely managed with legacy analytics solutions given its already-organized nature. Even with the rapid rise of new data sources, companies everywhere will continue to dip into their structured data stores as a means of producing insights that can show them new ways of doing business. While data-driven companies all over the globe have analyzed structured data for many decades, they are just now beginning to really take emerging data sources seriously, and this has created chaos in what was once a mature business sector.
What is unstructured data?
Unstructured data continues to grow in influence in the enterprise as organizations try to leverage new and emerging data sources. These new data sources are made up largely of streaming data coming from social media platforms, mobile applications, location services, and Internet of Things technologies. Since the diversity among unstructured data sources is so prevalent, businesses have much more trouble managing it than they do with old-school structured data. As a result, companies are being challenged in a way they weren’t before, and are having to get creative in order to pull relevant data for analytics.
The maturation and growth of data lakes and the Hadoop platform are a direct result of expanding unstructured data collection. Traditional data warehouse environments are no match for the different data types that companies now want to analyze. In this way, companies are having to pour additional resources into human talent and software programs to help them with this task.
The lack of an easily definable structure inside an unstructured data store presents a unique opportunity for an up-and-coming profession, the data scientist. Unstructured data cannot simply be recorded in an Excel spreadsheet or data table, and requires more specialized skills and tools to work with, but those who seek business insights are willing to make those upfront investments.
The bottom line
For overall success, organizations need to properly and effectively analyze all of their data, regardless of the source or type. Given the experience that the enterprise has with structured data, it’s no wonder all the buzz surrounds data collected from unstructured sources. New technologies are beginning to surface that help enterprises of all sizes analyze all of their data inside one single pane of glass, providing ease-of-use for end-users who need to generate swift answers to important business questions.
Latest posts by Timothy King (see all)
- The 4 Best Data Quality Software Tools to Consider - July 17, 2018
- The 28 Best Data Management Platforms for 2018 - July 13, 2018
- The 4 Best Master Data Management (MDM) Software Tools to Consider - July 11, 2018