Databases for maintaining unstructured data and analysis techniques to get mind-blowing business results have been lately developed. Some of the data created is in structured type while some are in unstructured type. Storage and analysis of structured data have been ongoing for quite a long time, but unstructured data has recently emerged on a huge scale. Big data constitutes both structured data and unstructured data. Now, we will look at what forms the structured vs unstructured data.
Structured vs unstructured data types are used broadly in data analysis but function in a different way. Let’s take a deeper look at these structured vs unstructured data types to comprehend just how diverse they are.
Different types of data
- Structured data
- Unstructured data
Structured data is easily visible via search because it is very organized information. It is uploaded precisely into a relational database (remember traditional row and columnar database structures) and resides in prefixed fields. Structured data types are used to analyze mostly quantitative problems—think “how many goods have been sold this year, month or week” or “how many consumers have subscribed to the newsletter,” for example.
Examples of structured data include:
- Dates
- Phone numbers
- ZIP codes
- Customer names
Now let’s look at what is unstructured data-
Unstructured data may have its own inner arrangement, but it does not arrange neatly to a database or spreadsheet. It consists of everything outside the boundaries of structured data. It may be gathered from a machine or a human; it can be images or text.
While unruly, it is also precious – unstructured data has the capacity to illustrate a complicated web of information that brings solid indication about future results. Unstructured data analysis is an essential part of the data analytics procedure -for example, customer webchats, a platform where customers frequently speak out their troubleshooting questions and complaints. If analyzed thoroughly, this data will surely help to guide organizations to prioritize solving queries and which feature of the product is driving the most queries. Social media data can predict customer buying habits and trends even before they start looking for a product if structured data is historically considered to be the company’s core strength or backbone. Unstructured data can help to have a competitive rim over its peers.
Some examples of unstructured data:
- Weblogs
- Text files
- Multimedia content
Now that you understand structured vs unstructured data, let’s move on to semi-structured data
Semi-structured data: neither structured nor unstructured
Semi-structured data is left out often in structured vs unstructured data conversation, but it’s worth discussing. At first look, semi-structured data seems much cluttered, which might make you ask, what is the difference between semi-structured data and unstructured data?
Actually, semi-structured data has features of both structured and unstructured data—it doesn’t match the structure associated with classic relational databases as structured data does. Still, it also has some structure in semantic mark-up, which enforces hierarchies of fields and records within the data.
Some examples of semi-structured data are:
Structured data vs unstructured data
The way data is searchable is often used to differentiate between structured vs unstructured data. In structured data vs unstructured data, the Structured data are combined in a way to make them simple to look for in their data set. On the other hand, unstructured data makes a searching capability much more complex and difficult.
Structured data is relatively simple to store, enter, query, and analyze. Still, it should be firmly defined in terms of field type and name (e.g. currency, alpha, date, numeric). Consequently, it is often limited by numbers, characters, or precise terminology.
In contrast, improvements to prepare and analyze unstructured data are very recent. Like data lakes, the latest data storage methods have authorized organizations to record and store unstructured data because it can be stored in its raw format. On the other hand, the primary problem of unstructured data sources is they are tricky for non-technical business analysts to understand, unbox, and prepare unstructured data for analytical usage.
The future of data structures
As data moves to big data, machine learning, and cloud computing, the prospect of data structures will grow as well. Jigsaw Academy offers online data science course that can help you move ahead in your career.
By the end of this decade, 80% of all the available data will be unstructured, and many organizations have attained that percentage already. There is undeniably a mammoth opportunity ahead with unstructured data, yet it creates the biggest challenge to the industry for the accessibility and analysis of that data.
What’s more, organizations won’t only use unstructured data but some amalgamation of structured, unstructured, and semi-structured data. For example, the case we mentioned earlier about the data of web chat. It’s sensible to analyze customer web chat, but the analysis won’t make any sense till it is combined with structured customer information stored efficiently in a CRM.
Conclusion
The challenge is preparing, accessing, and combining this data to make some sense out of it. Analysts can merge their current structured data with unstructured data, such as mapping social media data with consumer and sales data. You can enrol in a data science online course, to get wider knowledge about data structures. Jigsaw Academy provides online courses for data science that can earn you a certification.
