What Is Unstructured Data Management? Tools, Databases, and Analysis

What Is Unstructured Data Management? Tools, Databases, and Analysis

Unstructured data management is the collection, storage, maintenance, monitoring, and processing of data that is not predefined and is not easily stored in database tables such as an Excel spreadsheet. 

What Is Unstructured Data, Exactly?

Much of today’s data—in fact, up to an estimated 90% of enterprise data according to experts—is unstructured, which means that it doesn’t conform to any traditional data model or schema, such as a typical relational database (think the organized columns and rows of an Excel spreadsheet). 

Unstructured data can be generated by human activities or by machines, and includes text in Word documents, email content, image and video files, social media content, PowerPoint presentations, satellite imagery, mobile phone data logs and recorded conversations, and so on. 

Unstructured vs. Structured Data

Structured data can be organized into neat and orderly spreadsheets and has historically been much easier to manage than unstructured data. It includes information such as customer files, inventory lists, accounting data, and travel reservations. 

Unstructured data differs from structured data in its format, as previously mentioned, but it also differs from structured data in the way it’s used. It is more qualitative than quantitative and tends to represent ideas, thoughts, and feelings more than simple relational numbers and values.

While it can be more difficult to manage than structured data, unstructured data holds a wealth of valuable insights locked within it. Imagine being able to look at unstructured data and pinpoint the best times of day to attract customers in retail shopping areas or analyzing real-time driving data and weather data together to determine how, when, and why city traffic gets backed up. Or what if you could look at social media content to see how your customers are responding to a recent product launch or how your brand reputation is fluctuating due to a product recall? That’s the power of unstructured data. 

Unstructured Data and Big Data Analytics

Unstructured data is the most common type of data that organizations want to analyze today. As in the examples above, analyzing unstructured data with data analysis systems that offer serious number-crunching power and AI and machine learning features can lead to incredible insights no human could have discovered as quickly—or at all. Data analysis applications can look at multiple streams of unconnected data, such as sales figures for the past year, weather data, social media activity, recent news events, and much more, to find patterns and correlations never before considered. With insight into these patterns, organizations can find more effective ways to customize consumer experiences, deliver better and more efficient services, create new revenue streams, respond more quickly to customer and market trends and evolving demands, and more.

Analysis and Management Tools and Databases for Unstructured Data

While unstructured data is more complicated to store, manage, analyze, and process than structured data, many tools and applications exist today to help organizations manage their unstructured data and extract the hidden value within it. Let’s take a closer look at the data analysis and management tools and databases that make unstructured data less complex. 

Popular Unstructured Data Analysis Tools

The best data analytics tools for unstructured data typically include AI and machine learning features. They’re also often equipped with natural language processing (NLP), which is a type of artificial intelligence that can analyze and parse unstructured information without a traditionally defined format. These tools can analyze content from emails, social media, customer support records, and much more to understand the data’s context and significance. Other features include text mining, forensic analysis of content, authorship analysis, and text stylometry.

Some of the most popular data analytics tools for unstructured data include: 

  • MongoDB Charts: Provides robust visualizations for real-time insights and embedded analytics
  • Power BI from Microsoft: Offers data integration and robust visualizations for greater insights
  • Apache Hadoop: Has a toolset that makes it simple to parse and analyze complex data sets
  • Apache Spark: Offers rapid processing for real-time analytics
  • Tableau: Provides powerful visualizations and is good for non-technical users
  • MonkeyLearn: Serves as a comprehensive, all-in-one tool for visualization and data analytics
  • RapidMiner: Offers a solid platform for creating predictive data models
  • KNIME: Is an open source offering that allows a high degree of advanced customization

Popular Unstructured Databases

As mentioned previously, unstructured data doesn’t conform to traditional relational databases, which typically use Structured Query Language (SQL). Therefore, most organizations use NoSQL databases for unstructured data. NoSQL means “not only SQL” and refers to a non-relational database. It doesn’t split data into separate tables like relational databases do, so it isn’t “tabular.” Instead, there are four different types of NoSQL databases, including document-based databases, key-value stores, wide column-oriented databases, and graph databases. 

Some of the top NoSQL databases for storing unstructured data are: 

  • MongoDB: This is the most commonly used document database and provides a single view of all the stored data.
  • Apache Cassandra: This is an open source, distributed wide column-based database system that is very scalable and fast.
  • ElasticSearch: Because this open source, distributed NoSQL database system can store and search massive volumes of data and uses fuzzy matching (or returns results that approximately match a search term), it’s ideal for full-text search.
  • Amazon DynamoDB: This highly scalable key-value-pair-based distributed database system can handle 10 trillion requests per day with ease.
  • Apache HBase: Another highly scalable, open source distributed database system, it operates best with huge volumes of data (at least petabytes) and provides random and real-time data access.
  • Neo4j: This graph-based database is suitable for big data analytics applications and is often the database of choice in use cases that include knowledge graphs, network management, fraud detection, personalization, and more.
  • Redis: This open source, in-memory data store can be used as a cache, message broker, and database, delivering fast performance.
  • OrientDB: This open source project combines documents and graphs into a single database and offers fast read/write operations.

Popular Unstructured Data Management Tools

When it comes to finding the best tools for managing unstructured data, there are a few things to keep in mind. You need tools that can help you do the following:

  • Store and organize data and make it accessible and searchable: Cloud providers such as AWS or Microsoft Azure offer scalable storage for unstructured data in the form of a database, data warehouse, or data lake. Organizations sometimes choose to store highly sensitive unstructured data in an on-premises storage solution.
  • Clean your unstructured data: This is an important step that entails unifying data structure, standardizing data sets, fixing data errors, resolving syntax errors, identifying and addressing gaps in your data, and more. There are several tools to choose from, including OpenRefine, Trifacta Wrangler, WinPure, TIBCO Clarity, Melissa Clean Suite, and Data Ladder.
  • Visualize your unstructured data: Gartner defines data visualization as “a way to represent information graphically, highlighting patterns and trends in data and helping the reader to achieve quick insights.” As it’s a part of data analytics, many of the analytics tools mentioned above can help you visualize your data. Other solutions include Microsoft Power BI, Looker, Domo, Klipfolio, and Qlik Sense.  

Structured vs. Unstructured Data Management—A Comparison

We’ve already mentioned how structured data differs from unstructured data in general, but now let’s take a closer look at how the management of them differs as well. 

The advantage of structured data is that it is easily parsed by machine learning applications. Its organized nature makes it simple to manipulate and query. Structured data is also more user-friendly for people who aren’t data scientists, and there are many mature, well-vetted solutions today for analyzing, searching, and processing it. 

However, while structured data fits neatly into relational databases, it can be complicated to set up and the organized configuration of data can make it difficult to change up later on. Because it conforms to a predefined structure, that information can usually only be used for its originally intended purpose. Plus, structured data is typically stored in data warehouses, which are rigid and highly defined. That makes it expensive in terms of time and effort when an organization wants to use that structured data differently. 

Unstructured data, on the other hand, is not stored in any predefined format. Because it’s stored in its native format, it can be used quite flexibly for a wide range of use cases and needs. Also, due to the fact that it’s not predefined, unstructured data collection is typically fast and easy. It’s stored most commonly in data lakes, as opposed to data warehouses, and these lakes are highly scalable and can accommodate massive volumes of data. 

The downside to unstructured data, however, is that it’s generally more complicated and complex to prepare and analyze. It requires trained data scientists who know how to clean and use the data—and also to understand how various data sets are related to others. Unstructured data also requires more specialized tools to parse and analyze. While solutions are maturing today, they’re still “younger” than tools for analyzing structured data and have a ways to go to match the capabilities the industry is accustomed to with structured data manipulation and analysis.

Why Managing Unstructured Data Is Harder

Unstructured data is harder to manage because—well, it’s unstructured. That leads to a whole slew of issues that we’ve already mentioned in previous sections. It’s harder to organize, analyze, process, store, and retrieve. Querying, or searching, the data is also harder than it is with structured data because of the lack of fixed or predefined formats and the wide variety of data types it encapsulates. 

Scalability can also be an issue with unstructured data, as traditional storage systems require organizations to add more disks or storage nodes to the system to scale out. That scale-out model isn’t infinite and can also get expensive over time. 

Unstructured data requires storage that can scale out efficiently and cost-effectively. Many storage solutions for unstructured data are object storage solutions because object storage includes detailed metadata and a unique ID to make data access and retrieval easier. Unstructured data storage should also be flexible to allow for a range of data types and simplify access to archived data. 

While unstructured data is still typically more difficult to manage and use than structured data, the extra effort is worth it. Unstructured data is rich with hidden patterns and insights that can give your organization new and innovative ways to compete and succeed in today’s increasingly fierce marketplace.

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