Managing Unstructured Data
Data is everything in today’s business, from the structured SQL databases to the recent LinkedIn update. All the data in the world can be divided into two mega categories: structured and unstructured data. Most of the data generated today is unstructured — as much as 90% of data is defined as unstructured and this number is growing by 50% to 65% per year.
Let us delve into what unstructured data is and how to manage it in the world of ever-growing information.
What is unstructured data?
Unstructured data is any type of data which is not contained in a traditional file system or a database. We can also define unstructured data as human-generated content, and structured data as machine-generated (SQL databases, names, geolocation, credit card numbers, etc.).
Unstructured data can come both in form of text and non-text content. It is stored in its native format — the format defined by the application used to create the document.
The main criterium is that any unstructured data is not conformed into an existing template, and the types of available data are inconsistent.
It is harder and more challenging to analyze and manage unstructured data, contrary to structured.
Examples of unstructured data
Let us look closer at most common real-life examples of unstructured data.
- Media and entertainment content — video files, audio files, photos, etc.
- Social media generated data
- Customer feedback
- Rich media — weather data or spatial analysis data in form of video, audio and image formats
- Internet of Things (IoT) data — sensor data, ticker info, etc.
- Open-ended survey responses. Unlike close-ended responses (which fall more under the structured data category), open-ended responses give a better understanding of user desires and challenges.
- Voice transcriptions and chat recordings
- Documents — business documentation, reports, presentation, legal data, etc.
- Webpage content
- Emails. Even though emails can be structured based on the sender, date and email subject, the email body is unstructured.
The unstructured data types are not limited to the ones mentioned above — they grow, as the amount of data goes up. The best way to extract value from this data and organize it is through unstructured data management.
What is unstructured data management?
Unstructured data management is aimed at collecting, organizing, storing, and adding structure to the data which comes with no structure in the first place. As a result, you can gain serious benefits from having your unstructured data managed.
Why is managing unstructured data important?
The storage solution — with cloud storage options for data, there is no such thing as too much data as there are endless possibilities to store it. However, if you can’t use this data by analyzing it and applying the results to your business, there is no real use in it. To extract real value from unstructured data, you need to manage it.
Unstructured data generates priceless business value. The lack of structure makes it hard to analyze and retrieve the information. Proper analysis and management of unstructured data can allow to determine the efficiency rate of marketing campaigns, trends on social media, etc.
Unstructured data management allows you to:
- Stay ahead of the competition. You can gain a large competitive advantage by extracting valuable information from your unstructured data, getting insights into customer behavior, feedback, etc.
- Shape data-drive strategies. Enhance your future marketing and ad campaigns by retrieving the relevant information from your existing unstructured data.
- Optimize your workflow. With better data management, you and your coworkers will make less steps in the decision-making process.
- Perfect the customer experience. Feedback is gold, and a lot of data from customer feedback is brilliant. By gathering feedback from your unstructured data, you can improve the non-working techniques and enhance the working ones.
What are the challenges of managing unstructured data?
Unstructured data is more complicated to deal with than structured, and it comes with a set of drawbacks and challenges.
Challenge #1: Low data quality
With the huge amount of unstructured data, it can make it uneven in terms of quality. For example, tweets or certain social media shares are classified as unstructured data but it doesn’t bring relevant, high-quality information in terms of data analysis and eventual conclusions.
This type of data is not necessarily reliable because users can tend to exaggerate what they are sharing.
Challenge #2: Scalability. Permanent growth and eventual cost
You’ve seen the numbers — the amount of unstructured data is huge and keeps growing. It also means that the unstructured will be growing within your business as your business grows. We have a solution for this challenge below, stay tuned!
Challenge #3: New types of unstructured data
Certain conventional systems can’t analyze new types of unstructured data. The analytics need to come up with new ways to analyze, retrieve, extract and store the data. These ways also need to suit all the possible unstructured data formats, which makes it way more challenging than the fixed nature of structured data.
Challenge #4: Time-consuming process
Analyzing all the unstructured data, with different levels of quality, formats and disjoined data pieces takes a long time.
How can you overcome the challenges of managing unstructured data?
How to handle unstructured data when it never stops growing? One of the best technologies in working with unstructured data is object storage. Until the arrival of object-based storage, all the unstructured data was managed within file storage which came with scalability and hierarchy issues.
It immediately solves the scalability issue, since object storage allows almost unlimited scalability. For example, the object storage solution offered by Scaleway allows you to add unlimited amount of data and you pay only as you use — no need to purchase unneeded data storage in advance.
Among other unstructured data solutions, we have machine learning and artificial intelligence. The quality of data remains uneven. However, with natural language processing and machine learning you can retrieve more sophisticated data and get a detailed, high-quality analysis.
How do I manage my unstructured data?
There are certain questions you need to answer before you decide on the next step with your data management.
Question #1: Where are you now and where do you see your business data in terms of growth?
The answer will define what you can invest now and your future budget, according to your data needs.
Question #2: What type of security level does your business data require?
Finance companies will require a different level of security than, for example, NGOs.
Question #3: Why do you want to keep your data structured?
Is it to analyze feedback? Customer behavior? Social media trends? Only you can know the answer. Then, the right data storage provider will help you pick the right option
What is next for unstructured data?
According to Dataversity, the amount of unstructured data in the world is predicted to increase by 175 billion zettabytes by 2025. The way we manage it is crucial. With the growing amount of unstructured data, more and more businesses will depend on it, and the bigger the number — the more challenges it will bring.
The best way to handle the upcoming questions that come with unstructured data is to be smart and strategic about data storage and management. One sure option is to choose the best data storage provider to help you solve any potential data problem.
