Structured vs unstructured data management
Big data is big business – if you have the skills to manage it
As more and more companies begin or accelerate their digital transformation plans, the value and complexity of data analysis grows. No matter where you are on your digitization journey, you need an effective strategy to extract, analyse and use data to give your business a vital commercial edge over competition - but that is easier said than done.
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Proactively carrying out mass analytical processes can be a minefield, to say the least - and also expensive. Part of the reason for this is the sheer diversity of data sources. It's also far too easy to think of IT on a granular scale with databases such SQL NoSQL, Excel or Oracle.
So for newer businesses, it is therefore more beneficial to think of the big picture and try to understand whether their data is structured or unstructured. This classification will have a significant effect on how it is ultimately managed and analysed.
What is structured data and how is it managed?
When it comes to big data analytics, structured data is often what first comes to mind.
It's often stored in traditional databases composed of columns and rows and is also known as relational data. An illustration of structured data can be a customer database comprising names, addresses, telephone numbers, order frequency and type. Similarly, a database for clinical trials with demographic data, whatever their treatment or dosage, would also be an example of 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 Force.com 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.
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.
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