When it comes to data, files can come in many different forms. There are two main types of data—structured and unstructured. Each is sourced and collected in different ways, living on different types of databases, so their differences are important for data professionals. Show
This article will guide you through structured and unstructured data and their differences. Structured vs. unstructured dataThe main difference is that structured data is defined and searchable. This includes data like dates, phone numbers, and product SKUs. Unstructured data is everything else, which is more difficult to categorize or search, like photos, videos, podcasts, social media posts, and emails. Most of the data in the world is unstructured data. Structured dataUnstructured dataMain characteristicsSearchable What is structured data?Structured data is typically quantitative data that is organized and easily searchable. The programming language Structured Query Language (SQL) is used in a relational database to “query” to input and search within structured data. Examples of structured data include names, addresses, credit card numbers, telephone numbers, star ratings from customers, bank information, and other data that can be easily searched using SQL. This video from Google's Data Analytics Professional Certificate will give you a quick introduction to structured data: Understanding Structured Data In the real world, structured data could be used for things like:
Pros and cons of structured dataThree main benefits of structured data are:
Some drawbacks include:
What is semi-structured data?So, what’s in between? Semi-structured data is a mix of both types of data. A photo taken on your iPhone is unstructured, but it might be accompanied by a timestamp and a geotagged location. Some phones will tag photos based on faces or objects, adding another element of structured data. With these classifiers, this photo is considered semi-structured data. What is unstructured data?Unstructured data is every other type of data that is not structured. Approximately 80-90% of data is unstructured, meaning it has huge potential for competitive advantage if companies find ways to leverage it [1]. Unstructured data includes content such as emails, images, videos, audio files, social media posts, PDFs, and much more. Unstructured data is typically stored in data lakes, NoSQL databases, data warehouses, and applications. Today, this information can be processed by artificial intelligence algorithms and delivers huge value for organizations. Read more: Data Lake vs. Data Warehouse: What’s the Difference? Examples of unstructured dataIn the real world, unstructured data could be used for things like:
Pros and cons of unstructured dataThese are some benefits of unstructured data:
These are the drawbacks of unstructured data:
Structured data is typically stored and used with relational databases and data warehouses supported by SQL, which includes OLAP, MySQL, PostgreSQL, Oracle Database, and more. Unstructured data is typically supported by flexible NoSQL-friendly data lakes and non-relational databases, such as MongoDB, Hadoop, Azure, and more. Read more: NoSQL vs. SQL Databases: Understand the Differences and When to Use Related careersJobs that would typically work with either structured or unstructured data include most types of data-related careers. Here are a few common roles that work with data:.
Build your skills in data analyticsData analytics can help you in nearly every career field, but it can take you far in data science. Enroll in Google’s Data Analytics Professional Certificate and learn how to process and analyze data, use key analysis tools, and create visualizations that can inform key business decisions. professional certificate Google Data AnalyticsThis is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 4.8 (96,138 ratings) 0 already enrolled BEGINNER level Average time: 6 month(s) Learn at your own pace Skills you'll build: Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study What is structured data and unstructured data give examples?Common examples of applications that rely on structured data include customer relationship management (CRM), invoicing systems, product databases, and contact lists. Unstructured data includes various content such as documents, videos, audio files, posts on social media, and emails.
What is an example of structured data?Common examples of structured data are Excel files or SQL databases. Each of these have structured rows and columns that can be sorted. Structured data depends on the existence of a data model – a model of how data can be stored, processed and accessed.
What is meant by structured data?Structured data is when data is in a standardized format, has a well-defined structure, complies to a data model, follows a persistent order, and is easily accessed by humans and programs. This data type is generally stored in a database.
What is the difference between a structured and unstructured group?In a structured group, each member is assigned a particular role to play or task to perform in achieving the overall group goal. In unstructured groups, the group is simply given the task to be performed and each person in the group is free to contribute as he or she wishes.
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