A recent article suggests 80% of global data will be unstructured data by 2025, and around 90% of that data has been created in the past four years alone.  

Knowing how to tackle this massive part of your organization will be crucial. Especially when you consider that out of that huge number, a mere 0.5% is organized and used in any meaningful way.  

When organizations think or talk about data, they tend to speak in absolutes.   

After all, data is binary, it is absolute by definition. When an organization chooses a data governance partner and works through the process of collecting and using data, they almost always come up against the data structure problem. 

In reality, data isn’t absolute. Two sets of data exist within an organization, structured and unstructured. Data comes in two forms, and the forms, although inextricably linked, are diametrically opposed in nature.  

Data is a paradox. It is both binary and organic. Binary by nature, organic by implementation. Both structured and unstructured data is sourced, collated, and used in different ways with different databases at the core. The outcome is always the same: to see what story hides beneath the ones and zeros. It’s there, we just have to look at it the right way. Think of data as a story within a story, a plot within a movie. We only need to look, to see it for what it truly is; a wide brush stroke that paints a picture of who we are, what we do, and why we do it.

Structured Data: 

So first, there’s structured data, also categorized as quantitative data. Rigid, readable, and capable of being searched with relative ease through relational SQL (Structured Query Language) commands. Users can rapidly search and manipulate the data through the framework of the database.   

It has many pros, including: 

• (ML) machine learning- and algorithm-friendly, quickly searched using machine learning technology.
• Highly accessible for average users. Structured data doesn’t require in-depth knowledge of data sets and data theory. A basic grasp of the topic relative to the data set is all that’s required.
• Tool accessibility is high since structured data predates unstructured, and the tools available to search the data sets are more commonplace.   

Cons include:  

• Highly limited usage since the rigid pre-defined structure is key to how easy this data is to search through and manipulate. 
• Any update to one part of the system usually requires an entire reorganization of the data set.   

Best uses of structured data:  

• Collection and retention of facts and figures like names and dates, addresses and stock count information, and even geolocation. 
• The online booking model fits the “rows and columns” format usually seen in the organized, pre-defined dataset model.
• CRM (customer relationship management) fits this, too.
• Bookkeeping or accounting.  

Structured data, therefore, is what we would recognize in an Excel spreadsheet. It’s probably familiar to even the most basic user of a dataset.   

Unstructured Data: 

Also referred to as qualitative data, this type of data is raw. It can’t be processed using conventional means and has to be given its own special non-relational database, also known as NoSQL. Or, conversely, it can be stored in data lakes to preserve the raw format until such a time when the data can be analyzed.   

As we mentioned before, 80% of data by 2025 is going to be unstructured. And 95% of enterprises are prioritizing it from now on.   

Some of the basic forms we come to recognize as unstructured data are: 

• Basic text  
• Mobile activity  
• Sensor data from (IoT) technology  
• Log files  
• Social media posts  
• Video/audio files.

Pros of unstructured data:  

• Rapid data accumulation. Since the data isn’t pre-defined, it can be collected in its native format as rapidly as required 
• Accumulation in its native format means it is highly adaptable if used correctly 
• The ability to store data at scale in data lakes allows rapid storage scalability.

Cons: 

• The unstructured nature of the data requires specialized tools to fully make use of it, like NOW Privacy  
• Because of the non-formatted nature, it takes a little more work to make it useful. This may alienate some users who aren’t fully committed to a data governance policy that covers all forms of data. 

Best uses of unstructured data: 

• Predictive analytics. The nature of unstructured data, allows for predictive data analytics if used correctly. We can prepare for future events and get market-ready ahead of time. 
• Deep data mining. Consumer behavior and purchasing patterns can all be hidden inside gathered unstructured data.
• AI driven chatbots can perform text analysis and divert customers to the answer when they ask your organization a question. 

Key differentiators: 

• Quantitative (structured) data gives us an intricate view of customers/clients. Qualitative (unstructured) provides a top-down view to the quantitative data. It’s the holistic overview that allows us to see what can’t be found on a simple spreadsheet.
• Structured data is gathered from online forms, network logs, server logs, OLTP (online transaction processing) and systems with similar set parameters. 
• Unstructured data looks more like emails, word-processing documentation, PDF files and video formats like AVI or MP4.
• Structured data comprises numbers and values. Unstructured is text, audio, video and sensor data.
• Structured data has a data model assigned to it before creation (schema-on-write), unstructured is raw and stored in its native format until needed (schema-on-read). 
• Structured data has tabular formats when stored, like Excel sheets or SQL databases, these require less storage space. Unstructured data is stored as media or a NoSQL database. They usually require more cloud storage.
• Structured data can be used in machine learning, it can drive algorithms, whereas unstructured data can be used for Natural Language Processing applications (NLP). 

What About The Third Type Of Data? 

There’s a third type of data known as semi-structured data (XML, JSON) and is commonly referred to as the “bridge”. It doesn’t have a predefined structure like structured data, but it is easier to store than unstructured data. It makes use of metadata like tags and semantic markers to single out specific characteristics and assign them to preset fields. Metadata enables semi-structured data to be cataloged, analyzed and structured far better than plain, unstructured data.  

• For example, a written blog article has a headline tag, an image, a snippet, and image alt-text. This metadata helps differentiate the blog post from others of a similar type. 
• A database containing CRM data, versus tab-delimited files containing customer data, is a prime example of the difference between structured and semi-structured data. 
• Or, a tab-delimited file versus a text file containing LinkedIn comments on a business post is an example of semi-structured versus unstructured.  

Where Does NOW Privacy Come Into This? 

Powerful technology like NOW Privacy, artificial intelligence (AI) machine learning (ML) and other advanced technologies are driving toward a singularity in data science. A point where all data, no matter what the structure, can be called upon, analyzed and used effectively.  

NOW Privacy is already a world-class tool in this progression toward ultimate control over data within an organization. NOW Privacy can handle structured and unstructured data with the same simplicity and user friendly UI/UX.

The future of data governance across data types is the ability to: 

• Execute upon market intelligence findings by creating more advanced data governance modalities. Create machine learning capabilities that rapidly cover datasets and analyze customer behavior based on pre-set definitions. 
• Deep scan analysis of organizational communications for compliance errors in real-time to mitigate compliance faults. 
• Scan social media conversations to predict customer behavior based upon sentiment. 

In closing, the different data your organization is going to encounter is going to differ as time goes by. But as with all things, there’s a trend, and the trend is toward unstructured data.  

Why is this? 

Perhaps it’s the slow crawl away from the corporatization of organizations who are used to handling spreadsheets, toward a more fluid structure that embraces social media and unstructured media in general.  

Maybe the technology is leading the charge, as machine learning and tools become more adept at analyzing and governing unstructured data (especially using metadata), this type of data naturally becomes more prevalent.  

No matter the actual reason, what we know is that it’s happening right now in your organization. The unwanted snowball effect of putting it off now, to deal with a much bigger issue later, is truer now than ever before. If predictions are correct, then by 2025 most organizations could face a perfect storm of massive data sets, most of it unstructured, and the top-level executives pushing to implement data findings of which there will be few without the correct tools to utilize said data. 

The qualitative and quantitative world can complement each other beautifully if funneled through a tool like NOW Privacy. You can tap into that fully extractable value hidden deep within the unstructured world of your data, and we are here to walk you through that process.