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Unstructured Data Analytics Tools

Unstructured data is data that does not have any particular format. Businesses often capture unstructured data—such as text files, CCTV footage, sensor data, or emails—to enhance their revenues and optimize their internal processes. To process and analyze such complex types of big data, however, requires modern tools and techniques, due to the raw formats and often multiple input sources.

Why unstructured data analytics tools?

To understand the amount of data facing any modern business and the need for specialized tools to process that data, we’ll look at a specific example.

Imagine that you run a burger shop. Your shop is the best in town, and people love your burgers, particularly cheeseburgers. A new burger shop opens in the town, and they offer many more unique varieties along with cheeseburgers, like cheese-pasta burger, or veggie-overload burger.

You notice that your customer base has gradually reduced, so you check the shop’s last three months of sales statistics. You also review what users have said about your shop in various forums like social media, your website, and so on (descriptive analytics)—people still love your burgers, but they are also attracted to this new shop.

You find out the reason (diagnostic analytics)—the new shop has more varieties of burgers that people, particularly kids, enjoy.

Based on this information, you predict and visualize the future revenue of your shop for the next three months, if you continue the same practices (predictive analytics).

You have to find a sustainable solution to make your shop the best again (prescriptive analytics).

Imagine analyzing a thousand customer reviews, survey data, and sales statistics collected over a period of a few months manually—that would be time-consuming and highly error-prone. Traditional tools require developers and analysts with expert IT skills, and don’t help with real-time data analysis. This is where unstructured data analytics tools and techniques come to the rescue.

Most businesses typically perform the following types of analytics to solve a business problem:

  • Descriptive analytics: Describes what happened in the past using summary and statistics
  • Diagnostic analytics: Uncovers why it happened using data discovery, drill-down, data mining
  • Predictive analytics: Predicts what might happen in the future using machine learning models and forecasting
  • Prescriptive analytics: Suggests a corrective course of action using predictive analytics, machine learning, and artificial intelligence (AI) for decision-making

While the first two types of analytics are retrospective, the last two are prospective. Over the past 30 years, unstructured data analytics tools have evolved from being retrospective to prospective. This enables more focus on informed decision-making for better business productivity.

The top unstructured data analytics tools are listed below. There are overwhelming choices on the market, but the below tools have powerful analytics features, a simple UI, a narrow learning curve, and can perform different types of analytics to solve a business problem:

  • MongoDB Charts: Offers powerful visualizations, real-time data insights, embedded analytics
  • Microsoft Excel: Simple to use, powerful visualizations, ideal for medium datasets
  • Apache Hadoop: Contains set of tools to perform data-intensive tasks
  • Apache Spark: Offers fast processing, suitable for real-time analytics
  • Tableau: Yields great visualizations, apt for non-technical users
  • Power BI: Integrates data, intuitive, provides rich visualizations and insights

The image shows the various types of analytics and the tools that support the different analytics

MongoDB Charts

MongoDB Charts is an easy way to analyze data stored in MongoDB, including real-time data. Business users can quickly create rich dashboards and view visualizations from various data sources to get useful insights from data. Most unstructured data analytics tools are suited to work only with relational databases or structured data, creating the need for an additional data preparation and integration step; MongoDB Charts can directly work on JSON data. MongoDB Charts provides powerful features that allow you to:

  • Create real-time dashboards and share them for collaboration.
  • Use built-in aggregation functionality to perform quick data calculations.
  • Work with data on-premise or integrate with MongoDB Atlas cluster data.
  • Use embedded objects and arrays—i.e., nested data—without any coding or querying for creating different visualizations.
  • Embed visualizations directly into applications or workflows using iFrames or JavaScript SDK.
  • Dynamically filter data based on end user selections to provide a rich UI experience.
  • Skip the ETL step as no connectors are required.
  • Collaborate easily using custom sharing permissions for specific data sources.

Use cases for MongoDB Charts:

  • Ad hoc data analysis and reporting
  • Self-service data analysis by business users without the help of an IT team
  • Data-driven decision-making
  • Direct integration into MongoDB Atlas, enabling real unstructured data processing without any additional transformation steps

Microsoft Excel

Most of us have used MS Excel at some point to store data, perform basic calculations, and run descriptive analytics. Excel has evolved over time and can now be used for advanced data analytics. Excel stores data as rows and columns; unstructured data doesn’t necessarily have this format. However, you can import unstructured data from NoSQL databases like MongoDB using BI connector to bring unstructured data into Excel. You can then use Excel’s features for big data analytics. These include:

  • Basic and advanced formulae for calculations.
  • Pivot tables for data insights like statistics, summary, and conditional formatting.
  • Rich sets of charts and graphs to visualize data. Excel also recommends the most suitable charts based on your dataset.
  • Easy-to-generate-and-print reports, with customizations.
  • The ability to write code in Visual Basic to automate repetitive tasks.
  • Excel Power Query (add-on), which can be used for data cleaning and transformation.

Excel cannot handle extremely large datasets (more than one million rows). For this, you can use NoSQL databases like MongoDB that can store large amounts of data.

Use cases for Excel:

  • Building targeted marketing campaigns with benchmarks and expected profits.
  • Managing employee records like tracking leave and assignments.

Apache Hadoop

The Apache Hadoop ecosystem is an entire set of modules working together to divide an application into smaller fractions that run on multiple nodes. This way, large datasets can be processed in parallel. Hadoop is scalable, resilient, and suitable for large-scale data analytics. Because of this, Hadoop:

  • Provides high computing power to deal with data-intensive operations.
  • Enables distributed parallel processing.
  • Can add any number of servers to a Hadoop cluster, to increase storage and processing power.
  • Can perform preprocessing tasks like cleaning, transformation, and feature extraction using MapReduce and Pig.
  • Provides tools like Mahout to perform advanced analytics.
  • Offers good fault-tolerance; if one node goes down, the jobs are redirected to other nodes for completion.

Hadoop handles heavy batch operations but is not suitable to deal with real-time data. To overcome this, you can:

  • Use Hadoop with Apache Spark: Spark is lightning fast for processing real-time data. Using both tools together ensures batch and real-time processing for applications.
  • Use Hadoop with MongoDB to support real-time expressive ad hoc queries and aggregations against the data.

Use cases for Hadoop:

  • Evaluating public health trends, thereby reducing medical costs and helping to predict new diseases
  • Providing real-time access to customer call detail records and billing information
  • Predicting demand for specific products, informing dynamic product pricing, and aiding supply chain management

Apache Spark

Spark supports different data analytics tasks, like data loading and transformation, machine learning, graph processing, and streaming computation. Spark performs in-memory (RAM) computations, which is why it is lightning fast. Some features that make Spark a suitable tool for unstructured data analysis are:

  • Several components are available to perform data analytics tasks—like GraphX for graph processing and MLlib for data processing and machine learning.
  • Streaming data can be ingested from multiple sources like Hadoop Distributed File System (HDFS), Flume, CSV files, MySql, and SaaS applications.
  • Developers can write their applications in multiple languages like R, Python, and Java.
  • It supports deep learning workflows using the deep learning pipelines.
  • It can run on top of clusters managed by Hadoop, or as a standalone application.
  • It works seamlessly with NoSQL databases; for example, you can connect to MongoDB using MongoDB Spark connector.

Spark has been adopted by companies like Amazon and Yahoo!, among others. Some use cases for Spark are:

  • Identifying suspicious logins or fraudulent bank transactions.
  • Improving product recommendations based on a user’s browsing patterns and providing customized discounts or limited-time deals.
  • Personalizing news items and showing other news items in line with a user’s interests.


Tableau is an end-to-end data analytics and self-service business intelligence tool that helps businesses to integrate data, analyze, visualize, and share data insights. Tableau takes in data from multiple sources like NoSQL databases, spreadsheets, and CSV files, and integrates the data into a single structured view.

Although Tableau cannot by itself process unstructured data for analytics, it can consume data from NoSQL databases that store unstructured data in a flexible format. For example, you can connect Tableau with MongoDB using the BI connector. This makes it easy for non-technical users to create dashboards and use drag-and-drop features to get different views of data.

Key features of Tableau include:

  • Real-time data analysis using auto-refresh.
  • Intelligent blending of data collected from multiple sources into one view.
  • Interactive visualization widgets like maps, charts, and graphs to help users spot patterns quickly.
  • Collective insights using join recommendations, smart tables, trend lines, and forecasting.
  • Easy integration with R and Python.
  • Collaboration features like real-time updates on web, and file downloads in different formats.

Tableau’s use cases include but are not limited to:

Power BI

Power BI is a powerful self-service BI tool that can perform unstructured data analytics. It is well-suited for both analysts and business audiences due to intuitive visualization and dashboard features.

PowerBI can transform unstructured data for analytics into a more usable format using Power Query, R, or Python scripts. Non-technical users can also use NoSQL databases to avoid the transformation step and speed up the analytics process. For instance, MongoDB stores unstructured data and using the BI connector, you can get the usable data into PowerBI. True to its name, it has powerful features:

  • Allows you to clean and drill down just about any dataset with just a few clicks using Power Query. You can even use the M query language to customize the Power Queries.
  • Gives granular control to users for dashboard customization.
  • Supports Data Analysis eXpressions (DAX), a library of functions and operators that can be combined to build expressions and formulae and generate easy-to-understand reports.
  • Integrates with R to clean and shape data.
  • Allows access to real-time data streams using Azure Stream Analytics.
  • Provides an intuitive Q & A box, where users can ask questions about data available in the Power BI system in natural language.
  • Offers an easy learning curve; no prior knowledge is required to use the tool.
  • Great tool for ad-hoc reporting and ad-hoc data analysis.

Use cases for Power BI:

  • Creating custom business intelligence dashboards for resource and task management
  • Conducting prescriptive analytics and inventory management using granular reports on inventory in multiple warehouses
  • Creating sales scorecards to easily view sales performance zone-wise, product-wise, team-wise, and by many other criteria


Unstructured data analytics tools collect data from various data sources, integrate it, and then clean and analyze the data to produce business insights. They can largely reduce the manual efforts of data storage, integration, and analysis. Traditional relational databases are no longer suitable to process unstructured data because these databases require a proper data format.

This has led to the growth of NoSQL databases like MongoDB, which store data in a flexible schema. MongoDB can also perform analytics on data, using rich query expressions, charts, and aggregation framework. MongoDB’s suite of tools can help in preprocessing data before it is fed into the tools and speed up the analysis process. MongoDB provides connectors for all the major unstructured data analysis tools.


Which tools can be used for analysis of unstructured data?

Some of the best tools for unstructured data analysis are:

  • MongoDB Charts | Powerful visualizations, real-time data insights, embedded analytics.
  • Microsoft Excel | Simple to use, powerful visualizations, ideal for medium datasets.
  • Apache Hadoop | Contains a set of tools to perform data-intensive tasks.
  • Apache Spark | Fast processing, suitable for real-time analytics.
  • Tableau | Great visualizations, apt for non-technical users.
  • Power BI | Integrates data, intuitive, provides rich visualizations and insights.

What is unstructured data analytics?

Unstructured data analytics is the process of using data analytics tools and techniques to clean, process, structure, transform, analyze, and visualize unstructured data to get business insights and make strategic business decisions.
Unstructured data is complex to store and process. Hence, we need sophisticated tools to handle it. Some popular tools for storing unstructured data are Apache Hadoop, NoSQL databases like MongoDB, Apache Hive, and Excel. Popular tools for unstructured data processing and analysis are Power BI, Tableau, RapidMiner, Python, and R.

How do you analyze unstructured data?

To analyze unstructured data, we need a robust storage and integration mechanism. Unstructured data is usually huge in volume and comes in varied formats. Some popular unstructured data analysis tools are Hadoop, RapidMiner, PowerBI, Spark, R, and Tableau. These tools use various unstructured data analysis techniques to analyze and get insights from data.

How do you manage unstructured data?

To manage unstructured data:

  • Integrate and store the collected data on a secure and scalable platform.
  • Keep data searchable and accessible.
  • Clean and transform the unstructured data to make it suitable for analysis.
  • Use unstructured data analysis tools and techniques to gain insights.
  • Visualize the insights for reporting and decision-making.
  • Keep the latest data in storage systems at all times.

What does unstructured data look like?

Unstructured data comes in varied formats. Unstructured data can include a huge amount of text, including social media posts and engagements, reviews, survey results, questionnaires, chats, multimedia files like audio, videos, images, and so on. Unstructured data is raw and needs specialized tools for analysis.

Is XML unstructured data?

XML is semi-structured data. XML attributes are grouped together and have a schema. Although XML does not conform to the standard relational database structure, it is still easier to analyze when compared to unstructured data like multimedia files and documents. In addition, XML can have a flexible format, unlike structured data.

Is NoSQL unstructured data?

NoSQL is a popular way to store unstructured data. Unstructured data is more complex, as it doesn’t have a predefined format. Some examples are sensor data, multimedia, and text files. NoSQL databases provide a flexible data model to store and retrieve data. For example, MongoDB, a NoSQL database, stores data as documents, which are easy to traverse and allow multiple nesting levels.