Although trickier to analyze than structured data, unstructured data is becoming more common with the rise of IoT. To stay competitive, organizations need software that can manage both.
Not all data is created equal. Data can exist in structured or unstructured formats (including semi-structured types), and although structured data is much more straightforward for machine analysis, unstructured data is actually far more common. In fact, International Data Corporation (IDC) has projected that 80% of worldwide data will be unstructured by 2025. That presents a challenge for businesses, especially those hoping to leverage the Internet of Things (IoT) as a primary data source.
Data structure matters because the point of data is to extract insights and value — in a cost-effective way. If you don’t have a functional solution for using and analyzing data, you aren’t making the most of your investments. If you are rethinking your data strategy, understanding the difference between structured and unstructured types can make it clear why certain software tools are invaluable for extracting the insights your organization needs.
What is Structured Data?
You can think of structured data as quantitative or “objective” data, consisting largely of names and numbers. When data is structured, that essentially means it can be organized in a logical database of discrete fields, where it can be easily referenced and analyzed to reveal insights. Because structured data is clearly defined, it is well-suited for storage in relational databases like SQL.
Most legacy technology for analyzing data is built to integrate with relational databases. When data is arranged and labeled in tables, it is typically easy for an automated machine process to manipulate and analyze that data. Queries or algorithms can pull data from tables in various combinations, quickly correlating various field names and data types to reveal patterns, which translate into usable insights.
Every programmer should be familiar with structured data, which is commonly used for everything from web page development to business analytics. An array of straightforward business processes generate or rely on structured data, from transactions and reservations to inventory and GPS. For instance, the following items could easily be organized as interrelated items within a relational database:
- Social Security Numbers
- ZIP Codes
- Phone Numbers
- Addresses
- Names
- Transactions
- GPS Coordinates
At a technical level, you can recognize structured (and some semi-structured) data not just as names and numbers, but as particular data types. If you’re dealing with events, time series, relational data, or columnar data, these are typically structured types. Relational data is the most common business data format, as it’s used with SQL and Excel. Columnar data is often used with Cassandra. (You could also add JSON/XML to the list — this format, used with MongoDB, is semi-structured and can sometimes follow a rigorous database schema.) While structured data is clearly widespread and useful, it is in fact not the most common type of data today. That distinction belongs to a more unwieldy category — “unstructured” data.
What is Unstructured Data?
Unstructured data does not have a pre-defined format and is not generated in a way that allows for traditional relational organization. It can’t logically reside in a tabular, row-column database. You can think of it as “subjective” information, in the sense that traditional queries or algorithms cannot easily search through, read, or analyze this type of data. It can be generated either by machines or by humans — but unlike structured data, it tends to be more easily read and understood by humans.
Unstructured data isn’t suited to relational databases and is thus typically found within object-oriented databases like NoSQL. Patterns and valuable insights do exist within unstructured data, but it can take a bit more work to uncover that information. It is especially difficult to build automated tools that can analyze this data since doing so tends to require artificial intelligence.
Unstructured data formats are increasingly widespread and include the following:
- Audio, video, and photos: While common, these formats obviously cannot fit within the constraints of a typical database schema. But it’s important to be able to analyze these data types — in fact, one of the most common forms of unstructured data is video and video-like content.
- Text files/PDFs: Word documents, PDFs, and even plain text within otherwise structured data aren’t easy for machines to read. Emails and websites could be considered semi-structured, as they have some metadata — but again, the content and message fields cannot be easily parsed.
- Communications: Chat logs, IMs, transcripts, and even social media posts are all unstructured, given that the “content” (the text) evades analytic machine reading.
- IoT sensor data: Heterogeneous sensors generate a range of data types, including NoSQL data. IoT data is not necessarily unstructured, but even potentially structured data is often transmitted in a file format, making it unsuitable for relational database storage. Of course, combining IoT data sources for cross-referential analysis is a challenge if it can’t be standardized in a relational format.
The line between structured and unstructured data is not always as clear as it seems. Sometimes data that appears unstructured can be defined and processed in a structured way. For instance, log data could be defined in CSV format, using fields to define a metadata-based relational schema. Or metadata can help bring some structure even within NoSQL databases like MongoDB. On the other hand, simply having schema-defined data doesn’t necessarily mean it is structured correctly (if it contains plain text, for instance). Actually extracting value from that data would then require an unstructured approach.
Handling Structured and Unstructured Data in One Unified Solution
As unstructured data continues to proliferate, businesses are beginning to take advantage of machine learning tools that can organize and manage this data. These tools include everything from big data programs, business intelligence tools, data integration software, document management systems, pattern sensing programs, and search and indexing tools.
One common example of a useful machine learning tool for unstructured data is an AI-based natural language processing tool that can “read” large amounts of text and extract fairly meaningful insights. A tool like this could, for instance, help capture whether users feel “positively” or “negatively” about a subject.
While a wide range of useful tools does exist, this piecemeal approach for handling unstructured data isn’t as streamlined as it could be. In an era in which IoT device and sensor use is growing, businesses that can leverage unstructured data quickly and accurately will be one step ahead of the competition. But doing so requires more than an ad hoc or disjointed approach to analyzing unstructured data.
Rather than juggling an array of tools, companies should consider a purpose-built solution that can handle both structured and unstructured data. It should also be capable of integrating machine-learning tools or other functions — all in one centralized location. Businesses need solutions built with a variety of tools to interface with all developers and all types of data. It’s for this reason that Coolfire was built with a combination of tools to enable situational awareness — from the Socket.IO Javascript Library to the non-relational database MongoDB, to the query language GraphQL and the REST API architecture.
All of these tools work together to support Coolfire in processing both structured and unstructured inputs into useful real-time information. Whether your team is looking for insights in the form of visualizations, alerts, or other critical operational functions, the right comprehensive tool can optimize your data investments in the future. As IoT sensor data and other unstructured data like video footage continues to multiply, companies that can handle this increasingly common data type will have a competitive advantage.
