Unstructured Data Uncovered: Its Ascent, Challenges, and Potential Use Cases
We had only structured data in the good ol' days, and it was pretty straightforward to deal with. That's no more the case: We have semi-structured and unstructured data to worry about today. We will ignore semi-structured data for the purposes of this blog post, but the latter is about to become the data for us. IDC predicts that the total data will amount to 175 billion zettabytes by 2025, and 80 percent of data growth will come from unstructured data. We are being bombarded by unstructured data, people.
Think about how many photographs you receive on WhatsApp or how many files you share daily with friends at work. Your favorite culinary channel on Youtube must have uploaded a few videos since last week, right? The surveillance cameras scattered all over the city keep recording nonstop. Hundreds of satellites flying over the Earth take snapshots of certain locations at regular intervals, creating terabytes of imagery per day. These are all various forms of unstructured data, and our civilization generates a staggering amount of it every day.
Understanding the nature of unstructured data, the mind-boggling increase in its volume, and its implications can be key to understanding some of the problems the tech industry is trying to solve nowadays. Without further ado, let's dive in.
The best days of structured data seem to be behind us. Most of the data we generated before the rise of cloud technology and the explosion of social media was structured. Structured data is arranged in a predefined format, most of the time in rows and columns. A relational database is a good example of this type of data which is formatted into fields such as "customer name," "age," "phone number," etc. This type of data lends itself to SQL queries, can be stored in data warehouses and leveraged by machine learning tools, and has a large user base and a wide ecosystem built around it. However, its use cases and storage options are limited by its predefined purpose, rendering it rather inflexible.
Enterprise Resource Planning (ERP) systems were the stars of the corporate world in leveraging structured data and generating reports on business operations. These reports helped professionals keep an eye on the financials, sales performance, efficiency, and guided decision-making. It was a time when data was in fairly standardized formats and came from a limited number of sources. In today's world, where every person and every digital device has become a source of data in a plethora of different formats, on-premise ERPs can no longer be effective business intelligence tools.
At the heart of business operations today is the drive to understand the way customers think and their motivations. Gone are the days when you just looked at financials and reports of how your business was doing to decide what you should do next. You need to be proactive today, and understanding the nature of unstructured data can shed light on how data needs to be used today.
Unstructured data is information communicated in different forms (audio, video, satellite imagery, text, etc.) and stored in wildly different formats. Thanks to its ability to convey information in so many different forms, it expands the definition of data and allows for faster data accumulation. That it can be dumped into data lakes makes its storage easier. However, the real challenge begins once data is stored.
Two challenges: Storage cost and the data interpretation problem
Gartner reports that unstructured data is growing at a rate of 30 to 60 percent year over year. Storing that much data brings about a huge data management problem. As the data stored grows, costs associated with it increase at an even greater pace because of all the backup copies needed for data recovery. For organizations looking for efficiencies, optimizing data storage costs is paramount. Organizations should distinguish between actively-used data and data that is rarely accessed. Storing the latter in low-cost storage is a good first step in bringing down the cost of data storage.
Another challenge aggravated by the ever-increasing amount of data at hand involves analyzing the data and making sense of it. A simple content search conducted across unstructured data is not enough to unlock the potential of this precious source. Tapping into the wealth of information that can be gathered from unstructured data takes expertise. That's why data science has been one of the most popular fields for over a decade. You need data scientists and specialized tools to find out what kind of insights can be drawn from the terabytes of unstructured data your organization is sitting on.
Possible use cases for unstructured data
The biggest gain that could result from unlocking the potential of unstructured data is the social listening ability it affords organizations. The Internet is choke-full of social media accounts, forums, and e-commerce websites where people keep talking about your brand and product all the time. Listening in on that chatter can reveal valuable insights about how people are using your product, what they like or don't like about it, and how it compares to rival products. Armed with this kind of information, companies become more agile, correcting mistakes on the fly, pivoting and changing course when needed, or doubling down without having to wait for the quarterly financial reports.
Unstructured data can also be used to boost data mining practices, resulting in much better decisions. Tasks like credit risk assessment and insurance claims management would benefit from incorporating outputs from unstructured data into their business flow. An insurance company can employ ML to analyze a person's Facebook and LinkedIn pages to gain visibility into her travel and driving habits. This information can then be used to revise her car insurance premium for the upcoming year. Likewise, a bank can pick up on the high level of activity in a brand's social media accounts or capture insider gossip in an internet forum and learn about an impending product launch, which can significantly change the brand's credit standing.
Another wide-scale use of unstructured data involves analyses of digital communications to detect criminal activity or enforce content moderation against online mobbing and harassment. Signals suggesting such actions are not out in the open: They are buried in the millions of discrete interactions between people. Audio files, surveillance camera footage, satellite imagery, and social media are fertile grounds for the kind of unstructured data that some people would like to keep hidden from the eyes of authorities. Unstructured data plays a prominent role even in international affairs nowadays. As the recent Russian invasion of Ukraine proved, analysis of unstructured data offers an endless amount of open-source intelligence (OSINT) information regarding troop movements, logistics, and possible war crimes committed.
Harnessing the power of unstructured data has been one of the biggest drivers of innovations in the software industry lately. The challenges regarding storage cost and making sense of huge amounts of data in a short time still remain. The hope is that artificial intelligence and machine learning technologies will help with those challenges once they are mature enough. Only then will we unlock the full potential of unstructured data.