Structured vs unstructured data management

Big Data is big business – if you have the skills to manage it


Data is everywhere and constantly growing in both value and complexity for businesses. As such, an effective strategy to extract, analyse, and use it has become more and more of a priority, particularly as it can offer a vital commercial edge over competition.

Related Resource

Six things a developer should know about Postgres

Why enterprises are choosing PostgreSQL

Six things a developer should know about Postgres - whitepaper from EDBDownload now

That's easier said than done, however, as proactively carrying out mass analytical processes can be a bit of a minefield, not to mention expensive. This is in part due to the sheer diversity of data sources. It is also far to easy to think of IT on a granular level with databases such as SQL, NoSQL, Excel, or Oracle.

Instead, it could be more beneficial for businesses to think of the bigger picture and reflect on whether the data they want to use is structure or unstructured. This will have a far bigger effect on how it is ultimately managed and analysed.

However, even that can lead to more complications as data can be structured, unstructured, or some combination of both.

What is structured data and how is it managed?

A depiction of unstructured data

Structured data is often what first comes to mind when you think of both data in general and Big Data analytics.

This is the type of information that can be stored in traditional databases composed of columns and rows, and is also known as relational data. A customer database comprising names, addresses, telephone numbers, order frequency and type, and so on is an illustration of structured data. Likewise, a database for clinical trials encompassing demographic data, whether a patient is on a placebo or the real treatment, dosage and impact would also be structured data.

To an extent, by its very nature, structured data is already "managed" it's kept in an orderly fashion in a single location. Another layer of management can be added to this, however, in the form of a relational database management system (RDBMS).

These systems allow users to create, update and administer relational i.e. structured databases. The majority are written in the open source SQL language, or a variant thereof like MySQL. A notable exception is Oracle's database system, Oracle DB, which is proprietary software that's particularly popular for managing large datasets and as such is often found being used by the financial services sector.

While we won't be discussing it in depth here, it's also worth noting that an RDBMS is often embedded in products that also offer far more bells and whistles than just managing data and making it available to queries. For example, Salesforce, the cloud-based customer relationship management (CRM) platform, manages the structured data put into it, but also offers tools like chat, access to the development platform, analytics and so on. So depending on your needs, it may be worth looking for more than a bare RDBMS.

What is unstructured data and how is it managed?

Unstructured data is anything that can't be organised into a structured database. Common examples are free-flowing text-based interactions, such as email conversations or chat logs, word processing documents, slideshow presentations, image libraries, or videos.

While this may not look how you would imagine data to at first, it makes up over 80% of data in existence and often offers a wealth of useful information. Together with structured data, it's also one of the three Vs of Big Data variety (the other two being velocity and volume).

Unstructured data is more difficult to manage than unstructured data as it doesn't have a uniform format, even if the data source is the same. Indeed, managing it in the way structured data is managed is something of a novel idea, as it's only been feasible to mine it for information since big data analytics and AI have taken off.

Unstructured data management (UDM) is essential for successfully making use of all this data. Rather than there being a handful of tools to point to for UDM, there are instead some basic tenets to be followed.


This term is sometimes known as "discovering" as well as other related terms, it means compiling your data to really see what's there, how frequently it's accessed, for how long it has existed and more. The objective of indexing is to find out whether this information will potentially bring future value to the organisation and see if it is worth putting in an UDM system and archiving it.

This, however, can be a long process and take many weeks to sift and scan all this data. Be ready to dedicate a lot of effort and time to this process in the initial stage. This is also the section where you should add metatags so that the data is easy to search later on in the process.

Storage and availability

Now that the data has been organised, it now requires storing in a suitable location with the correct attributes that make it automatically and easily accessible.

There are a number of storage location to choose from which includes general cloud storage like Microsoft Azure or AWS S3 or on-premise data lakes. When the information resides here, it is able to be stored in its "natural" state, which means there is no need to store it in a database format, but also allows it to be available for automated querying through APIs.

When thinking about which type of storage to utilise, it's worth considering how frequently the data that is being stored is accessed. For example, if it's relatively frequent, it might need to be put in "cold" storage, which is usually much cheaper than if it is kept in storage that makes the information accessible at all times. However, in this "cold" storage it will be slower to access initially when you do need to sift through it and query it.

Semi-structured data

Usually, semi-structured data isn't generally presented in the form of columns and tables that are usually associated with relational databases or other database types. Despite this, it does still contain tags and other markers that separate specific elements and forms a hierarchy of records in the dataset. In a number of cases, semi-structured data can be an assortment of various differing classifications and attributes that are grouped together. In this case, it is not very important in which order the attributes are ranked.

Featured Resources

B2B under quarantine

Key B2C e-commerce features B2B need to adopt to survive

Download now

The top three IT pains of the new reality and how to solve them

Driving more resiliency with unified operations and service management

Download now

The five essentials from your endpoint security partner

Empower your MSP business to operate efficiently

Download now

How fashion retailers are redesigning their digital future

Fashion retail guide

Download now

Most Popular

RMIT to be first Australian university to implement AWS supercomputing facility
high-performance computing (HPC)

RMIT to be first Australian university to implement AWS supercomputing facility

28 Jul 2021
Samsung Galaxy S21 5G review: A rose-tinted experience
Mobile Phones

Samsung Galaxy S21 5G review: A rose-tinted experience

14 Jul 2021
Zyxel USG Flex 200 review: A timely and effective solution

Zyxel USG Flex 200 review: A timely and effective solution

28 Jul 2021