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What’s the Difference Between Structured and Unstructured Data?

August 24, 2016

Searchability is often used to differentiate between structured data and unstructured data. Structured data typically contains data types that are combined in a way to make them easy to search for in their data set. Unstructured data, on the other hand, makes a searching capability much more difficult. Companies most often work with unstructured data. 

Structured and unstructured data are both used extensively in big data analysis. Historically, because of limited processing capability, inadequate memory, and high data-storage costs, utilizing structured data was the only means to manage data effectively. More recently, unstructured data analytics sources have skyrocketed in use due to the increased availability of storage and the sheer number of complex data sources. Let’s look at these two data formats to understand just how different structured data and unstructured data are.

Structured Data: If Only Everything Were This Easy

Structured data is highly organized information that uploads neatly into a relational database (think traditional row database structures), lives in fixed fields, and is easily detectable via search operations or algorithms.  Structured data is relatively simple to enter, store, query, and analyze, but it must be strictly defined in terms of field name and type (e.g. alpha, numeric, date, currency), and as a result is often restricted by character numbers or specific terminology. Analysts typically use simple or more complex VLOOKUP queries in Excel spreadsheets or Structured Query Language (SQL) to perform queries on structured data within relational databases.

Structured data leaves out immense amounts of material that do not fit simply into a firm’s organization of information. Until recently, structured data was supplemented by this additional information in the form of paper or microfiche. With the improvement of processing by computers, lowered cost of data storage, and the spread of new formats of data, the age of unstructured data began. Now, structured data and unstructured data must both be consulted, queried, assimilated and leveraged to make the best business decisions.

There are already well-established ways to prepare and analyze structured data, whereas developments in unstructured data are fairly recent. Structured Query Language (SQL) programs take and read structured data from commonplace data stores like flight logs, ATM transactions, credit card transactions, and more.

Unstructured Data: Everything You Didn’t Know You Wanted

Unstructured data may have its own internal structure but does not conform neatly into a spreadsheet or database. While unruly in nature, it is also incredibly valuable and increasingly available in the form of complex data sources, such as web logs, multimedia content, email, customer service interactions, sales automation, and social media data. Most business interactions, in fact, are unstructured in nature.

The fundamental challenge of unstructured data sources is that they are difficult for nontechnical business users and data analysts alike to unbox, understand, and prepare for analytic use.  Beyond issues of structure, is the sheer volume of this type of data. Because of this, current data mining techniques often leave out valuable information and make analyzing unstructured data laborious and expensive. It’s important to note that, while perhaps more difficult to format than structured data, unstructured data has become increasingly important in recent years, and even vital for many organizations.

Structured and Unstructured Data Together At Last

Through our “data wrangling” techniques, Trifacta Wrangler enables both structured data and unstructured data preparation, analysis, and visualization. Trifacta’s intuitive interface empowers everyone—even the most non-technical of users—to interactively explore and prepare simple and complex data sources in order to execute data analytics.

Analysts can easily combine their current likely structured data with unstructured data, such as mapping social media with customer and sales automation data, for example. No matter the complexity and variance, Trifacta Wrangler permits users to leverage the data they need early on in order to generate the right outputs for better decision-making.  If your next data analysis project involves putting structured data together with unstructured data, consider using Trifacta.

To learn more about wrangling data for data onboarding, read our brief, Data Onboarding: A Survivor’s Guide To Combining Unfamiliar, Disparate Data; or download the free Principles of Data Wrangling eBook here.

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