Processing Unstructured Data in 5 Easy Ways

According to Forrester research, the volume of unstructured data managed by enterprises at the beginning of 2024 is expected to double by the year’s end. In fact, many studies indicate data will grow to 175 zettabytes by 2025. For context, 35 zettabytes of data equals a trillion hours of movies – enough to binge-watch continuously for 115 million years.

Roughly 80% of this vast array of data is unstructured, and it carries vast potential for businesses when effectively managed and analyzed.

How can organizations harness this valuable resource? Let’s dive into key strategies to turn unstructured data into a strategic asset – starting with understanding the characteristics and differences between structured and unstructured data.

Structured vs. Unstructured Data

Structured data is well-organized, quantitative and stored in predefined formats within relational database management systems (RDBMS). Examples are Excel sheets, Google sheets and databases containing names, addresses and transaction details. This data is easily searchable using SQL, scalable and requires minimal storage space. Structured data is well-suited for transactional and reporting-based tasks.

Conversely, unorganized data is flexible and more free flowing – like videos, images, social media posts and customer feedback – and doesn’t adhere to a specific format. Although more difficult to process, it is qualitative and holds valuable insights for deeper analysis into customer behavior, sentiment analysis and predictive modeling.

To stay competitive, organizations must recognize the importance of both types of data – leveraging structured data for operational efficiency and unstructured data for strategic decision-making.

Turning Unstructured Data into a Strategic Asset

Businesses can transform raw data from a liability into an asset in five simple steps.

1. Data Collection

Identify and gather relevant information that aligns with your goals. Unformatted data comes from various sources like social media, news reports and customer feedback, and not all of it will be useful. For example, to evaluate a social media campaign, focus on analyzing relevant hashtags to gauge sentiment rather than processing all content. This data often includes subjective opinions in forms like text, images and audio, which traditional analytics tools struggle to capture. Use surveys, questionnaires, emails, or real-time AI tools to gather this data. For unstructured data stored in legacy systems, intelligent document processing software can extract it for analysis. Once collected, securely store the data in data lake repositories in its native format to preserve metadata for future analysis.

2. Preprocessing

Unprocessed data can contain emojis, email signatures and banner ads that are difficult to analyze. Preprocessing is the cleaning of such unstructured data to make it usable for analysis.

To start, create a copy of the original file and expand any informal or handwritten text to enhance readability. Next, perform basic text processing tasks, like removing duplicates, links and special characters. Review the content for appropriate word usage and run a spell check to catch errors. Use intelligent document processing solutions, powered by natural language processing (NLP), and image recognition to extract relevant text from large files and distinguish between factual information and subjective opinions.

This thorough preparation will set the stage for more accurate and effective data processing and analysis.

3. Structuring 

To simplify access and synchronization across your business, unstructured data should be organized into structured formats, such as rows and columns in databases. This is achieved by leveraging text analysis programs powered by machine learning and NLP algorithms.

For instance, content intelligence tools like tokenization, lemmatization and stemming transform messy text into structured, machine-readable formats. Utilize this to prioritize specific data points by tagging parts of speech to classify attributes like “location,” “gender,” or “age.”

4. Analysis

The goal of unstructured data analysis is to extract actionable insights. The analysis should align with your specific goals, such as tracking how website visits, social media activity and ad clicks translate into sales and boost ROI. Tools like AWS S3 with Athena for querying, ElasticSearch for handling indecipherable text and AWS Neptune for finding relationships within the data can be invaluable in this process. By mining unformatted data for actionable insights, your organization can enhance products, services and customer experiences, ultimately aligning more closely with business goals.

To achieve better results, leverage content intelligence tools like pattern recognition software to uncover trends in customer behavior. Incorporating an operational analytics engine can enhance real-time decision-making, helping you assess whether your marketing campaigns are targeting the right audience.

5. Visualization

Creating visual representations like charts and graphs simplifies complex data, making it easier to understand and compare insights than sifting through raw text and symbols.

Instead of manually creating visuals – which can be time-consuming and error-prone – use an integrated business intelligence platform. These unstructured data analysis tools leverage machine learning to automate analysis, perform calculations and generate visuals based on key topics, customer sentiment and frequently used keywords. This makes it easier to share insights, identify trends and make data-driven recommendations.

Unlock the Value of Your Data with Material

As a best practice, regularly prune unstructured data, converting only the valuable information into a structured, relational database format. This not only reduces storage issues but also ensures that your team focuses on data that truly matters. Establishing a routine for this task – and ensuring that all team members adhere to it – is critical. Additionally, maintain an up-to-date backup and recovery system to ensure necessary data is at hand, especially in times of crisis.

Material’s data science and analytics services are designed to help you achieve your data goals. Our solutions are customized to meet your data management needs and tackle your complex business challenges. To learn more, reach out and start the conversation with our experts today.