Does your workflow run on unstructured data?
While unstructured data management is fairly easy at the early stages of running a business, it will grow to untamable volumes over time.
Sooner or later, you’ll find yourself free-falling into a data chaos trajectory that seems to be pulling down your resources with it.
As data lives in this perpetual darkness, you’ll find it hard to unearth crucial insights about your market. Customer engagement and customer support also probably won’t be the best as your turnaround times will drag on for hours.
But that’s not even the worst part.
Unstructured data makes it hard to find, moderate and protect customer PII, putting your business at risk of huge fines, among other severe compliance penalties.
For these reasons, it’s prudent to consider a content intelligence partner to turn all your dark and unstructured data into an easily navigable model.
In this article, we’ll be deep-diving into a complete guide on unstructured data definition, sources, use cases, and analysis.
1. What is Unstructured Data?
If you’re a business that handles a huge turnover of paperwork and electronic files, unstructured data is something you deal with daily.
But what is unstructured data exactly?
Unstructured data refers to all data your business collects within its various workflow processes, which doesn’t have a clear data model.
A data model refers to an organizational system that standardizes how various entities, data, in this case, relate to each other. That could be based on various aspects all boiling down to personal preferences. However, data model metrics in a typical business workflow are usually in the way of departmental elements.
In other words, you could also define unstructured data as being disorganized, and involving a combination of various formats including, but not limited to:
As these various file types mix, it becomes increasingly unclear what data lies where, making information retrieval difficult.
Consequently, data lakes can turn into unstructured dark data, which can be particularly vital for any business that relies on such a repository to make operational decisions.
That said, the definition of unstructured data and dark data is entirely different.
It’s often the case, though, that unstructured data isolates information across various departments and creates dark data or data silos.
2. Examples of Unstructured Data
So what are some popular types of unstructured data?
You see it in your workflow every day, and often it is right in front of your eyes but you may not even know it.
Common unstructured data examples include:
- Business Communications
- Feedback from surveys
- Business documents
- Medical records
- Social media content
Let’s take a closer look at each one to know what makes them up:
Businesses today use a variety of communication methods in their workflows.
Emails alone, for example, catalyze the rapid growth of unstructured data. With the average office worker receiving 122 emails per day, emails are massive sources of unstructured data.
Other communication channels that churn out unstructured data include video and audio calls, which are rife with conversations about operations, marketing, and financial agendas.
Moreover, if your company also has a call center to handle customer queries, recorded conversations between employees and consumers also constitute unstructured data.
2.2 Survey responses
How does your business go about market research?
If you rely on open-ended questionnaires to evaluate employee engagement and for market research, then that’s a huge source of unstructured text.
Before the modern advances of technology, researchers would use themes to classify similar responses.
The process wasn’t entirely accurate leading to ineffective market interpretations, but now we have natural language processing technologies for more efficient unstructured data analysis.
2.3 Business documents
Miscellaneous business documents are among the topmost unstructured data types.
Businesses documents include everything ranging from presentation reports siloed in PCs to contracts as well.
According to research by Small Business Trends, close to 6,000,000 small businesses in the US have a workforce of between 20 to 500 employees.
These findings are in line with a study by the Radicati Group.
Assuming a median workforce of about 250 per organization, you’re looking at a huge number of employee contracts that can be difficult to analyze.
All these contracts can become sources of unstructured data, especially if they are in a variety of formats, such as PDFs, physical documentation, images, etc.
2.4 Social media content
Often, your business receives consumer queries and offers customer support through social media.
It could also be that a great chunk of consumer engagement occurs within social media posts.
Private messages, and post comments containing customer PII and data needed for market research, are perfect examples of sources of unstructured data.
Other examples of unstructured data in social media include geolocation information and company videos and pictures.
2.5 Medical records
For healthcare institutions, medical records are a huge source of unstructured data.
This encompasses a variety of generators which can be classified into two basic categories.
The first includes unstructured data sources produced through human intervention, including doctors’ notes and examination documents.
In addition to human employees, there’s also machine generated unstructured data. This covers the data generated from:
- Surgery robots
- Emergency video cameras
Biosignal information from wearable equipment in intensive care units and patient monitoring systems at large also constitute other types of unstructured data in the healthcare sector.
3. Structured data vs Structured data
Many organizations today deal with both structured and unstructured data sources, and it’s likely you do too but you may not know the difference.
Crucially, is one better than the other?
We’ll be answering that with five points on unstructured data vs structured data:
3.1 Data model
Structured data is any data that follows a strict data model, and is usually stored according to some predefined elements.
Commonly, structured data is characterized by tables. It exists within the fixed fields of a document and is usually in the form of text and numbers.
As a result of this convenience of organization, structured data can be processed into a relational database.
Unstructured data, on the other hand, lacks a predetermined model of organization and encompasses a wide variety of formats that are sometimes often hard to measure.
3.2 Nature of storage
Unstructured data is often bundled up in a data lake, while structured data fits into a data warehouse.
This data lake is usually several times the size of its data warehouse equivalent, assuming a similar quantity of documents, as it contains typically bulky and raw file formats like images. For instance, a single image can be ten times the size of a word document.
Conversely, after ETL (extract, transform, and load) procedures, structured data ends up in a data warehouse. Processing is complete, and there’s no other destination for the data.
3.3 Quantitative vs Qualitative
The structured and unstructured data showdown often boils down to a matter of quantitative vs qualitative aspects.
Unstructured data is often viewed as typically qualitative data, covering social media interactions, interviews, and survey responses, all of which are hard to quantify but can be a measure of quality.
On the flip side, structured data is normally made up of countable components and hence is more quantitative in nature.
Consequently, structured data makes for easier analysis because establishing relationships across variables is easy, while unstructured data analysis cannot be carried out by the same traditional methods.
3.4 Format variety
Structured vs unstructured data examples and types differ wildly in terms of format.
Because data has been cleaned up and processed into a model, structured data tends to be available in certain varieties depending on the unstructured data processing tools used.
Whatever the format, the nature of the content is usually organized by meta tags and is easy to find because of this. Meanwhile, the typical state of unstructured data encompasses a huge range of formats, because it is stored in its native form.
This data could be in the way of audio files, video, and text, among other less mainstream varieties such as data from sensors.
3.5 Data manipulation
Consistency, transaction support, and atomicity are strenuous and difficult to achieve with unstructured data, marking a crucial chapter of this unstructured vs structured data debate.
That’s because processing work occurs across scattered systems, which are usually siloed from each other, preventing cross-system data visibility.
Data is bound to change with time, especially if it was tracking a fluctuating aspect.
With structured data residing in a relational database, it’s easier to carry out these changes. Additionally, data atomicity and transaction support are all possible because of the ease that a relational database affords.
4. Use Cases of Unstructured Data
While unstructured data has its challenges, it also comes in handy in many other areas from aviation to semantic analytics. From detecting bad motors to intelligent document processing, the possibilities are endless.
Without further ado, here are some interesting applications of unstructured data:
4.1 Image recognition
Video and audio files can be a huge source of unstructured data.
If your business deals with large volumes of audio and visual files, perhaps you offer videography services, then you understand just how hard these can be to find, group and process.
Through the power of deep learning, algorithms can be trained on previous files to establish precedence for the identification of various sounds or images. This ability comes in handy across various industries.
In the aviation sector, there are many interesting use cases of unstructured data. For example, models trained on motor sounds can recognize failing motors and point out the problem for repair due to changes in audio frequency.
For businesses that sell photos online, image recognition can help group or name photos appropriately. Additionally, product images can also be identified and labeled without needing to do it manually.
There are many other ways image recognition and unstructured data combine in the real world. For instance, apps like LeafSnap use computer vision to detect plant types and offer more information about the species.
Similar to how Google offers search results from text queries, LeafSnap uses images to search a database and present findings, thereby illustrating the importance of unstructured data.
4.2 Intelligent document processing
Business workflows are full of unstructured documents.
If you’re in the oil and gas industry, these come in the way of construction blueprints and CAD schematics that tend to get hidden in both electronic and physical storage media.
Case in point is a chemical manufacturer LydondellBasell, which had been toiling to access engineering drawings because they were mostly in non-searchable formats.
With renovations commonplace around the company, new CAD designs were generated regularly. While there was a conventional system of indexing, it involved manual navigation to find the needed files.
Turning unstructured data sources, namely non-searchable TIFFs, into high fidelity PDFs that could be looked up by text search, we were able to help the company create an enterprise-wide data pipeline with regards to its engineering schematics.
This was achieved by our AI-driven content intelligence software, which enabled the speedy conversion and classification of these files.
4.3 Semantic analytics
Do you have a call center to encourage upsells?
If so, unstructured data is key to fueling big data analytics in this case because it is perfect for semantic analytics.
Merely looking at the results of the sales process, and establishing a success rate doesn’t always present the full picture.
You could establish that 20% of calls were successful, but that wouldn’t explain why others failed. It would be hard to know what these agents are doing differently.
Through various speech analytics solutions, your business can leverage unstructured data advantages for deeper insight into customer behavior.
Often, it’s not just important to understand what your market base is saying but how they are saying it. The ability to use certain tonal or emotional cues to add more context to a conversation offers valuable data from unstructured data sources.
As an example of the uses of unstructured data, we’ll consider the case of a European wireless carrier that leveraged semantic analytics to improve its call center services.
A consumer sentiment score was set up based on the tone and emotion of conversations when they talked about the company’s services.
The company was therefore able to significantly reduce its churn rate, thanks to the strategic analysis of unstructured data.
5. Challenges of Managing Unstructured Data
Unstructured data, due to being hard to find and protect, can be quite challenging to manage as it often leads to the creation of dark data.
Here are some of the challenges your business will face if you heavily rely on unstructured data workflows:
5.1 Data compliance issues
How many documents does your business handle monthly?
I’m guessing way too much to properly find and regulate them all according to data compliance laws
Between July 2020 and July 2021 alone, there was an over 113% increase in GDPR violations resulting in millions of dollars worth of fines due to poor unstructured data management.
The above survey by Finbold further reinforces the importance of compliance.
Without proper structuring solutions in place, there’s a continual growth of unstructured data and non-searchable PII documents slipping through the cracks.
That’s why you need to promptly put into action this complete guide of unstructured data definition, sources, use cases, and analysis.
5.2 Storage cost inflation
How much does your business spend on data storage?
As information needs grow unchecked, so too does your storage cost of managing unstructured data.
That’s because your business keeps hanging on to information long after it has served its purpose. And due to poor data visibility across your networks, your business has no idea what files are needed, and which ones can be discarded to free up some space.
In addition to that, the scourge of redundant content adds to the data pile-up as well. Content similarity becomes harder to detect, and duplicate files are continually uploaded to your storage mechanism to further inflate the cost.
The situation gets especially expensive if you carry out your unstructured data management on-premises rather than on the cloud.
Unstructured storage challenges also include the inflation of utility and operation bills as well. The floor space, cooling requirements, and personnel required to man physical servers increase with data demands.
NetApp, for instance, estimates that it costs your business between $100-300 per GB of on-premise storage per month.
Consequently, for an additional 5GB of duplicate content that goes unnoticed due to unstructured data formats, you’ll be spending an extra $500 to $1500 a month.
5.3 RPA scaling issues
Many businesses today have implemented robotics process automation technology.
That could be anything from a simple price reconnaissance system for competitor analysis to more advanced solutions such as autonomous warehouse sorting units.
The implementation of these RPA projects unlocks many benefits by primarily taking over iterative processes in the workflow.
That said, many of these businesses are not able to realize their true RPA potential as unstructured data inhibits full-scale automation by causing data gaps.
Not knowing how to manage unstructured data limits the input that various automation software feed on to drive their target processes.
Let’s consider the case of an RPA-powered HR management system, which relies on employee information such as level of training and history.
If recent employee history, such as newly acquired academic qualifications, is buried within dark data arising from non-searchable, unstructured formats, the system can overlook said candidate for promotions.
HR staff would need to know how to handle unstructured data manually and update this information by hand, which could also take a while to uncover in the first place.
5.4 Poor turnaround times
Dark data is among the most crucial disadvantages of unstructured data.
Because of loose indexing metrics, and sometimes the complete absence of any organizational considerations, it can take an immense amount of time and effort for your business to find the data it needs.
The result is poor due diligence for mergers and acquisitions, disgruntled customer experiences, and even delayed opportunities to identify operational drains in your workflow.
If you’re a law firm, for instance, drudging through endless contracts for your clients, and having to access each individually, limits how much your firm can get done in a day.
Important renewal clauses can go unnoticed, and it may be challenging work to beat compliance deadlines.
In other scenarios where customer information is required to provide feedback, maybe due to a complaint or refund issue, unstructured data challenges make it harder to find.
5.5 Requires specialized expertise
Unstructured data isn’t easy for the typical business owner to analyze.
It consists of complex structures, and is often not defined because it is typically stored as is, making it difficult to interpret in comparison to structured data which offers rather straightforward insights.
Consequently, you might need to expand your workforce to make room for a data scientist.
Alternatively, you could turn to a content intelligence platform and transform all your unstructured data sources into structured data for easy analysis.
6. How to Analyze Unstructured Data
Managing unstructured data can be a tricky affair for your business, just as we’ve established. So you might want to convert unstructured data in your workflow to easily manageable structured data.
To do that, here is a step-by-step guide on how to analyze unstructured data.
6.1 Set your objectives
What goal do you hope to achieve?
Gartner estimates that upto 85% of all big data projects fail. Among the reasons behind this are unclear expectations or goals for the project.
It should therefore be clearly expressed right from the get-go what you want to gain from unstructured data processing.
Perhaps you’re looking to decrease your storage needs. In that case, you should have an exact figure in mind regarding the amount of storage you’d like to free up.
Alternatively, if the end goal is to achieve data compliance, you should have clear PII metrics as well guiding how to process unstructured data according to your needs.
Work out what type of information you’ll be looking up for deletion or protection. That could be identification numbers, places of residence, etc.
6.2 List down sources
How is unstructured generated in your organization?
Working out your sources is key to determining the scope of your project.
Let’s consider the objective of data compliance. Identifying sources of PII and the unstructured data extraction processes involved which influence where they end up, makes it easier for you to follow the trail and craft a road map.
In this case, your unstructured data sources would be any part of your workflow that customers interact with to submit sensitive personal data.
This could be anything from direct social media messages to form filling processes at your front-end desks.
6.3 Choose technology
A technology evaluation is necessary to determine what you have to get started, and what you’ll need to get across the finish line.
So what unstructured data processing technologies do you use?
Perhaps you channel it all into a legacy system of some sort, or different ones for that matter, which are accessed and filled by different parts of your organization.
In this case, you’ll need a content intelligence solution to bridge the gap first and break down those silos, making it easy for your business when analyzing unstructured data. .
Otherwise, you’ll be blindsided by hidden PII and an under-defined scope.
Additionally, how will you be storing this data afterward?
If you’ll be going back to your old legacy systems, content intelligence may yet again be the key to ensuring newly structured data doesn’t settle back to an unstructured data lake.
Alternatively, if you’re still committed to physical documentation strategies, you may need to create a strict data policy and put in place an enforcement team or data governance plan.
6.4 Collect the data
With scope and goals in mind, it’s now time to get to the data collection work.
Go through each source identified in step two, and begin the unstructured data extraction process. With a content intelligence platform, this entire part of the process, and the next part i.e. data cleaning, can be automated.
That said, there are primarily two methods of manual data extraction depending on what exact tool you use, namely logical and physical extraction.
Logical extraction entails direct extraction from source systems, with a data pipeline enabling direct transference to a destination.
In the case of obsolete source systems, you have to make do with physical extraction. This takes place outside the source system, and you may still need to structure the data afterward if the extraction tool doesn’t do that for you.
6.5 Clean the data
With your data now where you need it to be, it’s time to clean up.
Data cleaning entails data modification to remove dirty components of a dataset.
Dirty data here refers to information entered incorrectly, which may have either occurred in the old system or the new one if you relied on a data migration strategy fuelled solely by human effort.
Additionally, data could be dirty because it is now irrelevant due to being time-dependent, duplicate, or for some other reason altogether, hence the need for processing unstructured data.
6.6 Back up data
Once data is clean, all white space and duplicate content are now gone, it is time to create a backup plan and choose a means of storage as well.
A backup is necessary to ensure you have an unstructured data source at the ready for emergency recovery operations, and also because a Business Continuity report substantiates that 70% of businesses shut down after a major data loss incident.
Of course, you have two options for unstructured data storage including :
- Physical media
- Cloud storage
Cloud storage is ideal if your business has a large supply chain, and there’s a need for highly collaborative teamwork across networks.
If your enterprise is shackled by resource-hogging applications that slow down cloud access, then you might need to complement cloud with local storage.
6.7 Classify and analyze
Depending on what criteria you’re prioritizing, you’ll need to classify the data.
In this case, you’ll prioritize certain parts of it according to your needs. If it’s customer PII you’re looking for, you could sort this out by demographic patterns, among other segmentation strategies.
You may need to incorporate visual unstructured data analysis tools in case you’re searching for more actionable data insights from market research. That entails tools that can help you generate graphs and charts, among other effective media.
Good examples of popular visual data analysis tools include Google Data Studio and Tableau.
7. Tools for Analyzing Unstructured Data
If you’re like most businesses today, it’s likely the case you rely on conventional unstructured data management and processing tactics.
In that case, you’ll need to tap into some of the latest AI-powered unstructured data analysis tools:
7.1 Adlib Software
Does your business need to structure its data?
Unstructured data can be overwhelming so you might need to structure it first to enable easy analysis.
If so, then Adlib software is a great place to start your content migration and restructuring. Their AI-driven content intelligence platform lives in the cloud and can digitize your documents, through intelligent OCR technology.
Additionally, it scans your current systems for hidden documents and standardizes over 300 formats into an easily searchable format. Then, Adlib provides further classification support through the power of natural language processing and machine learning, which also enables it to detect duplicate content.
Our Adlib content intelligence software is perfect for all types and sizes of businesses whose workflows are drowning in manual unstructured data analysis techniques.
7.2 IBM Cloud
IBM Cloud is a pricier option that is tailored towards particularly large companies, with huge storage and unstructured data analytics needs.
It complements a preliminary system without needing restructuring and can find data locked up across your networks.
The service taps into machine learning to enable cognitive applications when it comes to data discovery and analysis.
However, IBM Cloud analytics is synonymous with long customer response times. This is due to the large volume of queries the corporation receives, ensuring a steep learning curve for your business.
7.3 Microsoft Azure
Cloud-based Microsoft Azure also targets multinationals with global subsidiaries and is among great tools for analyzing unstructured data.
Using virtualized hardware, Azure can extract and process data through data pipelines and make it available to various end-users across its network.
It offers text processing features in real-time, which allow for customization to enable seamless integration into a workflow. Generally, Azure can offer data lake analytics using specialized computing engines meant to handle large data volumes and complex structures or lack thereof.
Microsoft Azure also offers modeling and reporting features for better insight. Despite 63% of enterprises using Azure, as reported by Forbes, the software is not flawless.
On the downside, the complexity of the control panel and untimely technical support are common sources of user complaints.
7.4 Amazon AWS
Amazon AWS is among today’s versatile tools for unstructured data analysis for businesses looking for a wide range of software functionalities.
It enables the combination of a huge variety of data formats, thanks to AWS-powered data lakes with great flexibility and scalability. Additionally, it unifies data access for better monitoring and security, further enabling the definition of data governance policies based on locality metrics.
Amazon AWS also cleans data and offers excellent visual interpretation tools.
For all its strengths though, the service falls short in terms of resource restriction. AWS, despite being among the most popular big data tools for unstructured data, limits resources on a regional basis.
Is unstructured data a huge part of your workflow?
Then you might have to be on the lookout for the GDPR, and other data compliance enforcers who might be readying a case against your business.
Additionally, the growth of unstructured data means your workforce will be devoting a lot of time and effort to its processing.
To put into motion this complete guide on unstructured data definition, sources, use cases, and analysis, be sure to reach out to the right partner to set your digital transformation initiative on the right footing.
An AI-powered platform will take care of everything that goes into structuring data, including digitization, unstructured data extraction from legacy systems, and classification as well.