Structured Data vs. Unstructured Data

In 2022, McKinsey predicted that by 2025, businesses would have embedded data in every decision, interaction and process. Fast forward to today, and the facts prove them right.

A robust data management architecture has become essential for businesses aiming to maintain a competitive advantage. It lays the foundations for efficient data handling at scale, which fosters key insights. It improves your bottom line and enables people across the company to access reliable data.

However, to harness the power of both structured and unstructured data assets, businesses must address a series of complexities:

• Data integration and retrieval
• Quality assurance
• Data security

Imagine being able to harness all your organisation’s data, regardless of its format, source, or complexity. That’s what successful businesses do. They leverage the massive amount of information they generate to enhance competitiveness, facilitate actionable insights, and make effective data-driven decisions.

But the value of data, whether structured or unstructured, isn’t in its volume. It’s in how well businesses understand, manage, and, ultimately, use it.

Structured vs. Unstructured Data – What’s the Difference and Why It Matters

Structured data and unstructured data are the two crucial types of information companies must master to help achieve different strategic goals. The terms classify data based on format type.

The Order: Structured Data

Structured data is information organised in a rigid format, making it ideal for reporting and quick analysis. It fits neatly into tables but lacks flexibility, and it’s typically stored in traditional databases.

The most significant benefit of structured data is that it works out-of-the-box with machine learning (ML). Furthermore, it can be easily used by non-technical users. Common examples of structured data are employee records, spreadsheets, and credit card numbers.

Businesses can utilise structured data to enhance their supply chain, inventory management process, generate fresh customer preferences and buy behaviour insights.

The Chaos: Unstructured Data

Unstructured data (i.e., big data) is descriptive information that lacks a predefined schema and comprises up to 90% of an organisation’s data. It comes in all shapes and sizes, including email, social media content, video files, and sensor data. It’s best for understanding user sentiments, predicting trends, and personalisation.

It shines for its flexibility, fast accumulation rate, and scalability, but it requires strong governance and specific data expertise.

Moreover, data is collected quickly and easily. It can also be stored in cloud-based, scalable data lakes (more on that momentarily) that can accommodate massive amounts of data and allow pay-per-use pricing.

The Middle Ground: Semi-Structured Data

Semi-structured data is a mix between the two. For instance, it doesn’t follow the strict rules of structured data. It’s better searchable than unstructured data because of tags and metadata (e.g., date and location) that provide some basic order and hierarchy. Organisations generally use it for web scraping and data integration.

Building a Unified Data Strategy – Using a Data Lake to Seamlessly Integrate Data at Scale

Effective data management is crucial in a world where, according to Statista, the amount of data created in 2024 reached a whopping 149 zettabytes – 30 times the amount of global data generated in 2020.

With this ever-growing volume of structured and unstructured data being generated fast from a variety of data sources, organisations struggle to keep up. Traditional data warehouses that accept only processed, structured data are no longer enough.

When you feed your AI models with high-quality structured and unstructured data, you derive accurate insights and impactful predictions that enable you to innovate, adapt, and remain competitive. Here is where data lakes come in.

Data lakes seamlessly integrate any structured and unstructured data at scale. They provide a holistic view of your business, empowering you to unlock your full potential. Information is stored in real-time and indefinitely. That explains why, according to S&P Global, over half of the organisations polled have implemented a data lake and an additional 22% aim to do so soon. A data lake enables businesses to:

• Centralise all data in a single source of truth. Provides users with a single, trustworthy, self-service platform for all data. Data duplication, multiple platforms and security policies become things of the past.
• Leverage more data from different sources, at a fraction of the time. You can collect any data type (structured, unstructured, semi-structured) from any source, and you can do it fast. Furthermore, you can swiftly transform raw data into structured data to prepare it for analysis.
• Uncover insights using any data analytics tool. With data lakes, you aren’t hindered by a limited set of tools. Depending on your needs, you can select any tool that suits you.
• Foster collaboration and accurate and fast decisions. When all data is available to all employees, effective collaboration across departments is a doddle. A data lake breaks data silos, leading to more accurate and faster decision-making, reduced errors, and increased productivity.

Structured and Unstructured Data: Use Cases

Here are three real-life examples of how Acora utilised both data types to help organisations boost business performance while reducing costs.

From Fragmented Data Systems to a Unified Data Platform

Fragmented data across different systems is a common issue among organisations of all sizes. We supported a long-standing customer in the Financial Services Industry to overcome this problem by implementing a Data Lakehouse.The new data architecture, with a centralised system acting as a unity catalogue for governance, streamlined operations and informed decision making.

Outcome

• Faster and more informed decisions,
• Lower operational costs,
• Boosted data accuracy and compliance with industry regulations.

Identify Customers at Risk to Mitigate Customer Churn

In today’s highly competitive market, customer retention is a real challenge. Our solutions helped a telecom firm leverage both structured sales data and unstructured customer behaviour information to forecast churn and identify customers at risk. These actionable insights enabled the organisation to engage those customers at risk with targeted offers.

Outcome

• Improved retention rates, and
• Maximised customer lifetime value.

Improve Customer Journey to Reduce Cart Abandonment Rate

With nearly 40% of consumers surveyed by Five9 confirming they’ll stop buying from a company after a single negative experience, it’s clear that nowadays, customers’ expectations are high. The objective was clear: simplify the journey, increase satisfaction and boost revenue. By combining structured and unstructured customer data, we leveraged AI automation to improve and simplify the customer journey. through personalised recommendations and real-time support using chatbots and automated responses.

Outcome

• Decreasing basket abandonment rates.
• Higher revenue.
• Enhances customer satisfaction and experience.

Structured and Unstructured Data: Data Quality Is a Must-Have for Any Business

According to One Advanced, inaccurate or incorrect data is the number one cause of data-related issues for 37% of financial professionals, such as untrustworthy analytics, operational issues, and incorrect business decisions that can have catastrophic consequences. Organisations may end up shipping products to the wrong address or being fined for incorrect financial reporting.

Moreover, as AI initiatives gain traction, data quality becomes paramount. Companies are investing millions into AI to unlock efficiency and innovation. However, without a proper data quality strategy and controls in place, all these investments and the benefits of AI-based analytics and ML are undermined.

AI directly depends on the quality of the data ingested. That means that if you train your models on structured and unstructured data riddled with conflicting, outdated, or duplicated information, you’ll get flawed outputs. The outcome will affect everything, from risk modelling and predictions to customer insights and business decisions.

Darshna Shah, Chief AI Officer at Elastacloud (part of the Acora Group), explained the importance of data quality stating, “Usually it’s been through some kind of quality process, which is fundamental to actually getting good results.”

More accurate information translates into reduced costs, fewer errors, and better decisions. So, ensure the quality of your structured and unstructured data by implementing robust data quality processes.
Automatically cleanse and validate your information so that it’s ready for consumption.
Ensure the data is up-to-date, unbiased, and consistent.
Get rid of duplicates and errors.

In a flash, your structured and unstructured data will become your organisation’s centralised source of truth. From there, you’ll be finally able to extract meaningful and valuable information that drives strategic decisions and enhances operational efficiency.

The Acora Approach – Navigating the Structured/Unstructured Data Challenge

At Acora, we start by understanding your business’s current stance with data, your business objectives, and your use cases. We then apply our experience and expertise to help you pinpoint areas where AI can make the most impact and accelerate your data-driven journey.

For instance, we facilitate data quality improvements by guiding you to set up a data strategy and proper data management that includes standardised data collection, storage, cleansing and access. As experienced service partners, we help you establish centralised data repositories to streamline accessibility for AI applications while addressing other challenges like integration of legacy systems, scalability, and leadership buy-in.

Unearthing growth opportunities starts with data. AI bridges the gap between unstructured and structured data by introducing new ways to process information at scale with incredible accuracy. It excels at pattern recognition, contextual understanding, and adaptive learning. It gives structure to unstructured data without changing the meaning and value of the information. In short, it’s the perfect ally for bringing order to chaos.

That is why Acora helps businesses address the challenges of structured and unstructured data through Art of the Possible Sessions, which encompass maturity assessments. Our experienced Data and AI experts are there to support you in building the foundational systems and knowledge necessary to generate effective solutions tailored to your needs quickly.

Let Acora Help You Navigate The Structured and Unstructured Data Challenge

Understanding the differences between structured vs. unstructured data, managing information effectively, and embracing a unified strategy is crucial to harnessing the true power of data. Acora can help you do just that. Talk to an Acora data and AI expert. Let us guide your organisation through this journey. We’ll enable you to unlock value and drive rapid progress while ensuring that the implemented technology aligns with your broader business priorities.