What is unstructured data and why is it important?
Unstructured data is information that is not organised in a predefined structure. This means that unstructured data is difficult to search, analyse and manage. The majority of data is unstructured, and Unstructured Data examples include:
- Documents and text files
- Audio files
- Web content
- Social media posts
- Chat Logs
- Sensor Data (e.g., from devices that measure temperature, air pressure, motion, or location)
Unstructured data is often an untapped resource and can provide valuable insights not offered by structured data alone.
What is the difference between structured, unstructured and semi-structured data?
Structured data is information that is highly organised and formatted in a specific way. Examples of structured data include statistics or information stored in spreadsheets or databases, such as customer information, sales transactions, inventory records, financial data, and other organised datasets that fit into a well-defined structure. Structured data is easy to search and analyse quickly due to the clear structure it follows.
By contrast, unstructured data doesn’t fit into a pre-set structure. Unstructured data is processed and analysed in a more specialised way. This often involves analysts, language processing or machine learning.
Semi-structured data is largely unstructured, but it incorporates internal tags, markings or metadata that separate and differentiate various data elements. This allows data to be organised into groups and categories of data elements, but the data within these groups is itself unstructured.
Email is a common example. The metadata used in an email enables searching by Inbox, Sent, and Drafts, but the email text within each category has no pre-set structure.
Is Excel structured or unstructured data?
The data in Excel spreadsheets is generally considered structured data because it provides a clearly-defined structure for data entry, and data can be formatted or filtered in particular ways. However, Excel data can also be considered unstructured when there is freeform text in cells or embedded images and charts, and the data doesn’t conform to the tabular structure Excel uses.
Is SQL unstructured data?
Structured Query Language (SQL) is not a form of unstructured data. In fact, it is a programming language used to manage structured data within a relational database. It is used to provide the structure or organisation needed to ensure specific data or information can be accessed when using particular search methods within a database.
What are the challenges with unstructured data?
Because unstructured data isn’t organised in a predefined format, relevant information isn’t easily accessible through standard search or retrieval methods. For example, a particular category of information can be found quickly in a spreadsheet using a simple search query, but information embedded within a video isn’t available in the same way.
The lack of standardisation of unstructured data also makes integration and analysis more complex. Because unstructured data is not stored in a relational database – i.e., linked with other databases – it isn’t readily retrievable through a common identifier, such as a customer number.
Additionally, unstructured data is often generated at a high volume and speed, which poses challenges for storage and processing, as well as filtering for inconsistencies and inaccuracies.
What is unstructured data used for?
Unstructured data can enhance analysis, knowledge and understanding, particularly when reviewed in conjunction with structured data. This is because unstructured data can deliver a greater volume and array of information that provides insights not attainable from structured data. It is valuable for business intelligence and analytics and can be used in a number of ways. For example, analysing unstructured data can help businesses better understand customer preferences and behavioural trends.
In healthcare, unstructured data, such as physician notes, radiology reports, and pathology reports, can detect patterns and improve diagnosis accuracy and treatment decisions.
In the field of finance, unstructured data can help detect fraud. By examining unstructured data within transaction logs and other free-form textual records, useful clues and indicators of fraudulent activities can be identified.
How do you make unstructured data structured?
To convert unstructured data into structured data, the data must undergo a process called data structuring or data normalisation. This involves transforming the unstructured data into an organised format that can be easily stored, processed, and analysed. The process begins with extracting the relevant information from the unstructured data sources, such as text documents, images, or audio files.
The data is parsed into smaller components or entities, like sentences, paragraphs, or data fields, and then standardised using consistent formatting, normalisation, and cleaning techniques to ensure uniformity and remove any inconsistencies or noise from the data.
The next step is categorisation and classification based on the data’s attributes, identifying, and extracting specific entities, such as names, dates, locations, or products, using techniques like named entity recognition or pattern matching.
The data can then be entered into a structure or schema, allowing for it to be accessed using filters, formulae, or basic search functions.
How do you analyse unstructured data/
A number of advanced data analysis tools have been developed specifically to process and store unstructured data, particularly text data. These tools can be trained for specific industries and needs.
Using machine learning and natural language processing techniques, the technology uncovers hidden patterns within unstructured data that would not be easily detectable through traditional methods used for structured data analysis.
In the field of threat intelligence, the ability to analyse unstructured data is critical. Analysis of unstructured data can provide diverse and real-time information, context, early threat detection and threat actor attribution.
Silobreaker specialises in taking this text-heavy and conversational unstructured data from millions of sources in different languages and normalising the data set. At the click of a button, we provide insight from that data and how it relates to your Priority Intelligence Requirements (PIRs). This is extremely difficult to do manually and cannot be achieved with lists of Indicators of Compromise.
Find out how Silobreaker can help you make sense of unstructured data –delivering key insights and helping you to meet your PIRs, reduce risk and response times and provide decision-makers with actionable intelligence faster.