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.
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:
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 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:
Use cases for MongoDB Charts:
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:
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:
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:
Hadoop handles heavy batch operations but is not suitable to deal with real-time data. To overcome this, you can:
Use cases for Hadoop:
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:
Spark has been adopted by companies like Amazon and Yahoo!, among others. Some use cases for Spark are:
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:
Tableau’s use cases include but are not limited to:
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:
Use cases for Power BI:
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.
Some of the best tools for unstructured data analysis are:
To manage 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.