Unstructured Data Vs. Structured Data: A 3-Minute Rundown
Most marketers think being data-driven means using web metrics to inform every decision they make. But that’s not actually being data-driven. That’s being Google Analytics-driven. To truly be data-driven, we must remind ourselves of the actual definition of data — all types of information.
One of the most insightful types of information is qualitative data or unstructured data. It can reveal your customers’ true opinions and feelings toward your brand, which is challenging to extract from quantitative data or structured data.
Even Jeff Bezos, the CEO of Amazon, is a passionate proponent of using qualitative data to drive strategy. “The thing I have noticed is when the anecdotes and the data disagree, the anecdotes are usually right. And there’s something wrong with the way you are measuring (your data),” he explained during an onstage interview at George Bush Presidential Center last April.
Bezos’ love for customer feedback shouldn’t compel you to supplant quantitative data with qualitative data when strategizing your next marketing campaign, though. It should compel you to inform your strategy with both sources of data. By combining the insights pulled from web metrics and customer feedback, you can get a full understanding of your marketing program’s effectiveness.
If you want a deeper explanation of what qualitative or unstructured data and quantitative or structured data is, check out this quick rundown of what both data sources exactly are and which tools you can use to store and analyze them.
Most often referred to as qualitative data, unstructured data is usually subjective opinions and judgments of your brand in the form of text, which most analytics software can’t collect. This makes unstructured data difficult to gather, store, and organize in typical databases like Excel and SQL. It’s also difficult to examine unstructured data with standard data analysis methods and tools like regression analysis and pivot tables.
Since you can’t store and organize unstructured data in typical databases, you need to store them in Word documents or non-relational (NoSQL) databases, like Elasticsearch or Solr, which can perform search queries for words and phrases.
Additionally, since you can’t use standard data analysis methods and tools to pull insights from unstructured data, you can either manually analyze or use the analysis tools in a NoSQL database to examine unstructured data. However, to use these tools effectively, you need a high level of technical expertise.
If you can successfully extract insights from unstructured data, though, you can develop a deep understanding of your customer’s preferences and their sentiment toward your brand.
Unstructured Data Examples
The most common examples of unstructured data are survey responses, social media comments, blog comments, email responses, and phone call transcriptions
Most often referred to as quantitative data, structured data is objective facts and numbers that most analytics software can collect, making the data easier to export, store, and organize in typical databases like Excel and SQL. Even though structured data is just numbers or words packed in a database, you can easily extract insights from structured data by running it through data analysis methods and tools like regression analysis and pivot tables. This is the most valuable aspect of structured data.
Structured Data Examples
The most common examples of structured data are numbers, names, dates, addresses, and transactional information .
Structured vs. Unstructured Data
The difference between structured and unstructured data is that structured data is objective facts and numbers that most analytics software can collect, making it easy to export, store, and organize in typical databases like Excel, Google Sheets, and SQL. You can also easily examine structured data with standard data analysis methods and tools like regression analysis and pivot tables.
On the contrary, unstructured data is usually subjective opinions and judgments of your brand in the form of text, which most analytics software can’t collect, making it difficult to export, store, and organize in typical databases. You also can’t examine unstructured data analysis methods and tools. Most of the time, you must store unstructured data in Word documents or NoSQL databases and manually analyze it or use the analysis tools in a NoSQL database to examine this type of data.
Be Data-Driven, Not Just Google-Analytics Driven
In a world where Google Analytics can spit out every metric under the sun, you must remember that qualitative data, like customer feedback, is just as crucial for informing your marketing strategy as web metrics. Without unstructured data, you won’t have a clear understanding of how your customers actually feel about your brand. And that’s crucial for every marketer to know.