Unstructured data: what it is and tools

25 de July de 2023

The rise of digital technology and the data-driven approach in companies has led to an exponential growth in data generation. More than 80% of this data is unstructured, i.e. it is not organised in a predefined format such as tables or databases. Although they are valuable and rich in information, their analysis poses significant challenges. In this article we will discuss what unstructured data is and what challenges and solutions you will encounter when using it.

What is unstructured data?

Unstructured data is information that is not organised in a predetermined way or does not follow a specific model or structure. This type of data can be difficult to analyse and process using conventional methods.
Unstructured data can come in a variety of forms, such as:

  • free text: any text written in natural language, such as emails, documents, social media posts, product reviews….
  • Images
  • Video
  • Audio
  • Sensor data.
  • Web data: Web pages, click logs, location data, etc.

Because unstructured data can be more complex and varied than structured data, it may require more advanced analytical tools and techniques, such as machine learning and artificial intelligence, to extract useful information from it. However, unstructured data can often provide valuable information that is not available through structured data.

Unstructured data challenges and solutions


Diversity of formats

One of the main challenges of unstructured data is the diversity of formats. Data can come in the form of text, images, audio, video, PDF documents, social media posts, and so on. Each of these formats requires a different analysis approach and specific tools for processing.

Volume and speed

With the proliferation of internet-connected devices and social media platforms, unstructured data is being generated at a staggering rate and volume. This speed of data generation can be overwhelming for organisations trying to process and analyse this data in a timely manner.

Data quality and accuracy

Unstructured data often contains noise, inconsistencies and errors. In addition, interpretation of data such as text and images can be subjective and context-dependent, which can lead to ambiguities and difficulties in ensuring the accuracy of analysis.


Artificial intelligence and machine learning

Artificial intelligence (AI) and machine learning techniques are playing an increasingly important role in the analysis of unstructured data. Machine learning algorithms can be altered to recognise patterns in large unstructured data sets and make predictions based on these patterns.

Natural language processing

Natural language processing (NLP) is a branch of AI that is used to analyse unstructured text. It can help identify themes, sentiments, entities and relationships in text data, allowing for greater understanding and analysis.

Cloud storage and distributed computing

Cloud storage and distributed computing technologies can help handle the volume and velocity of unstructured data. They enable the storage and processing of large amounts of data on multiple servers, reducing the load on individual systems and improving efficiency.

Platforms to centralise data

Before using platforms that facilitate the centralisation of data in each area of the company, there is an earlier step in data management. In this phase, the MDM (or master data management) organises the data and distributes it to the corresponding tool (CRM, CDP, ERP…).

Master data management is a method that defines and manages the critical data of an organisation in order to provide a single source of reference or point of truth.

This method involves collecting data from various sources, identifying the most reliable master data (such as customer, product, employee, etc. data) and integrating it into a single centralised system.

Once master data is identified and consolidated through MDM, it can be distributed to different platforms and systems, enabling better use and exploitation of this data.

Here are some of the platforms to which the data is distributed:

Customer area

CRM or “Customer Relationship Management: is a system or set of tools that allows a company or business to manage, organise and track all interactions and relationships it has with its customers or potential customers. One of the main purposes of a CRM is to centralise customer data. This means that everything a company knows about a customer is stored and organised in one place.

One example of a CRM is Salesforce, which is one of the leaders in the CRM industry and offers robust solutions for managing unstructured data. Salesforce uses artificial intelligence to analyse and manage this data, providing companies with a deeper understanding of their customers’ needs and behaviours.

CDP (Customer Data Platform): is software that collects and unifies customer data from multiple sources to create a single, persistent customer profile. This tool allows companies to have a complete and real-time view of their customers, improving the personalisation of communications and offers, the customer experience and facilitating more effective marketing strategies. Unlike other data management platforms, it can handle both known and anonymous data in real time.

Algonomy uses artificial intelligence (AI) to provide an omnichannel personalisation platform and a real-time Customer Data Platform (CDP) that helps brands deliver individualised experiences across different channels.

Business Area

ERP or Enterprise Resource Planning: is a software system that helps companies centralise, integrate and automate business processes and information across the organisation. This system collects, stores, manages and analyses customer data from various sources within a company. These sources can include sales, marketing, customer service, finance, etc.

SAP ERP is an enterprise resource planning system used to centralise and integrate business operations in an organisation. It includes modules for managing finance, supply chain, customer relationships, human resources and quality. These functionalities enable companies to improve efficiency, facilitate decision-making and optimise the management of customer relationships and other business processes.

Financial area

BI (Business Intelligence) platforms: these are tools that allow centralising, organising, visualising and analysing an organisation’s data. These platforms can be particularly useful for facilitating financial analysis, data-driven decision making, and identifying financial trends and patterns.

Tableau: Known for its intuitive user interface and data visualisation capabilities. It offers predictive analytics capabilities and can integrate with a variety of data sources. It is a powerful tool for financial services firms because of its ability to handle large volumes of data, its focus on intuitive data visualisation and its ability to integrate with a wide range of data sources.

Enterprise Performance Management (EPM) platforms: This is a set of processes and tools that allow organisations to centralise their financial data. This system collects financial data from a variety of sources, provides detailed analysis and reporting, assists in planning and budgeting, and simplifies financial consolidation, especially in organisations operating across multiple geographies.

HR Area

Human Resource Management Systems (HRMS): is a human resource management system that collects and analyses diverse and non-traditional employee information, such as emails, text notes, recorded conversations, and social media posts. This centralisation facilitates access to information, optimises management decisions based on detailed data, improves operational efficiency and helps ensure compliance with privacy laws.

Workday is a cloud-based human resource management system that provides services for financial planning, human resource analytics, talent management, payroll administration, and time and attendance, among others. Its data-driven design enables organisations to make strategic decisions based on real-time data, and its intuitive user interface makes it easy for users to adopt.

Shall we talk?

At Cognodata we have a platform that unifies a large part of this data in a single repository through workflows for the management of purchases, suppliers and data monetisation involving sales, marketing, finance and logistics departments.

Would you like to learn more about our platform?