Understanding Structured and Unstructured Data

We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive.

In our modern digital world, proper use of data can play a huge role in a business’s success. Datasets are exploding at an ever-accelerating rate, so collecting and analyzing data to maximum effect is crucial. Companies and businesses focus a lot on data collection in order to make sure they can get valuable insights out of it. Understanding data structure is a key to unlocking its value.

A data’s “structure” refers to a particular way of organizing and storing it in a database or warehouse so that it can be accessed and analyzed. Different types of information are more suited to being stored in a structured or unstructured format. Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both.

Structured vs unstructured data

Structured data is far easier for programs to understand, while unstructured data poses a greater challenge. However, both types of data play an important role in data analysis.

Structured data

As the word “structured” suggests, this is data which is highly organized and neatly formatted. Structured data is organized in tabular format (ie. rows and columns) and there is a relationship between different rows and columns. As such, it’s highly organized and formatted and easy to store, process, and access. It can work easily with most standard analytical models. Most BI tools also know how to work with it, allowing users to optimize resources from a technical perspective. It also requires less storage space. Some examples of structured data are Excel files, Google Sheets, and traditional DataBase Management Systems (DBMS).

Unstructured data

Unstructured data is data which is not organized in any predefined manner. It can be textual, numbers, dates, or BLOBs (Binary Large OBjects). Irregularities and disorganization within unstructured data make it difficult to handle and understand.

Some key points to know about unstructured data:

• Approximately 80% of the worldwide data is unstructured
• Can be difficult to process and organize
• Tends to be complex in nature
• Gives more freedom for analysis
• Requires more storage
• Rich media types (images, videos, audio) can also be analyzed with advanced technology
• Some examples include text data, social media comments, documents, phone call transcriptions, various log files like server logs, sensor logs, image, audio, video, etc.

Semi-structured data

Semi-structured data is a hybrid of both structured and unstructured data. It has some organizational framework but does not have the complete structure that is required to fit in a relational database. Semi-structured data has a self-describing structure that contains tags or attributes to separate various entities within data.

Key points to keep in mind about semi-structured data:

• Falls under the heading of unstructured data, but it has some lower-degree organization (still falls short of relational databases)
• Can be coerced into useful and easy-to-leverage table formats 
• Examples of semi-structured data include XML, JSON, Emails, NoSQL DBs, event tracking, and web pages

To analyze structured vs unstructured data, a new generation of BI tools has emerged that use advanced coding languages, as well as Machine Learning (ML) and Artificial Intelligence (AI) to help humans make sense of these huge datasets. Both types of data potentially hold great value and these tools are pivotal to help aggregate, query, analyze, and create business value from them.

Differences between structured and unstructured data

Here’s a quick table showing the differences between structured and unstructured data for easy reference.

Cloud data warehouses: The new era of data storage

Cloud data warehouses aggregate data from different sources into a central, consistent data store to support various business, analytics, visualization, AI, and ML purposes. A data warehouse enables an organization to run powerful analytics on huge volumes of data in ways that a standard database cannot.

This new generation of data warehouses are built to run entirely in the cloud rather than requiring a company to own on-premises server machines. They are offered as a managed service to customers, with the physical infrastructure being managed by the cloud company. Customers don’t have to make an upfront investment in hardware or software and don’t need to worry about server maintenance or related concerns.

Cloud-based data warehouses have grown more popular in recent years as more companies use cloud services and seek to reduce or eliminate their on-premises investments. They have numerous advantages over on-premises systems, which explains why everyone is moving to the cloud.

• Scalability: Cloud data warehouses empower organizations to quickly scale to meet changing business requirements. Administrators can scale processing and storage resources up or down as needed, easily.
• Speed: Setup is fast and simple. Queries can also run much more quickly.
• Cost savings: The cost-effective subscription-based model is a major driver of cloud data warehouse adoption. There’s zero initial cost for hardware, server room, IT staff, and maintenance. User cost is based on storage and computing usage.
• Security: Cloud data warehouses include data security coverage, end-to-end data encryption, and built-in protection against data loss.
• Availability: Cloud data warehouses are built for high availability. They also support multiple locations anywhere in the world.

How cloud data warehouses work

In general, data warehouses have a three-tiered architecture. First is the bottom tier, which is an extraction level that collects, cleanses, transforms, and loads the data from multiple sources by using a process known as Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT). The middle tier is typically a relational data store with schemas that support analytical processing. The top tier is an analytics tier that includes everything from standard querying tools to analytics, data mining, AI or ML capabilities, reporting, and presentation visualization tools.

Making life better for data professionals

Nowadays, data stores are huge and complex. This makes cloud data warehouses a boon for data professionals because they’re designed for analytics on very large datasets. They can help deliver consistently high performance at lower costs. Users also enjoy faster analytics queries and processing speeds. These warehouses, when combined with an analytics and BI tool, also summarize information very effectively across the business to help users in all departments better understand their data. In-warehouse data prep and materialized views can also help users glean deeper insight through advanced analytics, including the use of more sophisticated coding languages and ML.

Turning data into money with analytics tools

Today, a business with a mature data environment will have a huge amount of structured and unstructured data from various sources collected in a cloud data warehouse (or maybe more than one). They can use this data to make better decisions and launch new projects. The variety and complexity of this data drives the need for efficient, cost-effective ways of analyzing it. Analytics and BI tools are the solution.

One way that analytics and BI tools can help businesses transform and thrive in the changing world we live in is data mining. Data mining is the practice of looking for patterns in the data to identify trends and insights that can reveal market trends, increase sales, reduce churn, fuel new business initiatives, and more. The right analytics and BI tool can even help embed analytics into a company’s software product and put insights from their vast stores of structured and unstructured data into the hands of users, increasing stickiness and mindshare and even opening the door for increased revenues.

Data engineers are tasked with connecting data warehouses to business intelligence tools and use software engineering skills like advanced coding languages to prepare that data for analysis. Once the data is prepared and connected to a business intelligence tool like Sisense, users of all technical skills levels can perform analyses and glean insights from it.

Every bit of data helps

No matter what your business specifics are, if you have access to massive amounts of structured and unstructured data, it’s up to you to make the most of it. If your goal is to derive business value and opportunities from the stores in your cloud data warehouse,then you need to understand what kind of data you have, make sure it’s properly prepared, and pipe it into a BI tool. BI tools provide efficient data analytics, visualizations, embedding opportunities, and insights that users across your organization will use to make smarter decisions, drive new revenue opportunities, and help your company digitally transform and stay competitive in the ever-changing business world. Whatever you’re using your data to build, be bold.

Carmen DeCouto developed a passion for data as a customer before joining Sisense. With over seven years of experience in a variety of technologies, Carmen is dedicated to empowering advanced data teams as they tackle the next wave of industry-redefining challenges.