6 Open-Source Data Profiling Tools (Benefits And Importance)
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Data profiling monitors an organisation's data and ensures accuracy, quality and completeness. Usually, companies use open-source data profiling tools to improve data quality and determine patterns and data relationships for better consolidation. If you work in the business intelligence or data science field, learning about data profiling tools can help you improve your understanding of the gathered data and make intelligent business decisions. In this article, we discuss why companies use data profiling tools and provide a list of six profiling tools to help you choose one for the organisation.
Why do companies use open-source data profiling tools?
Open-source data profiling tools are useful for data scientists and business analysts. Some reasons why companies are using these tools include:
understanding and organising data
discover potential issues with data quality
ensure data meets the organisational standards and help in statistical analysis
determine data that requires correction and analysis
understand the source of data quality issues
identify potential data relationships and patterns for data consolidation
improve user's understanding of the collected data
help identify and address problems before they arise
Benefits of using data profiling tools
Here are a few benefits of using data profiling tools:
Enhances data quality: Most of these tools reveal issues with the data before transfer and storage. After running this tool, it becomes easier to manage the data.
Prevent unprecedented crisis: With a data profiling tool, companies can find issues with the data before they create havoc in the company. Often, erroneous calculations and business forecasts can cause business loss and using a data profiling tool can reduce these losses.
Enhances decision-making: As it becomes easier to manage data, companies are in a better position to make intelligent and data-backed decisions.
Stay organised: Using these tools, analysts and scientists show the relationship between each data value and access data in an organised manner.
6 Open-source data profiling tools
Here are six common data profiling tools to consider:
Quadient DataCleaner is a plug-and-play and cost-effective data profiling tool that transforms, analyses and improves the data quality. It helps users run a comprehensive check on the entire dataset to help a business drive better business decisions. This intuitive tool can find missing parameters, values and other characteristics present in the data set. Interestingly, the tool allows data enrichment and ensures data quality. Apart from ensuring data quality, this tool visualises the results through the dashboard.
Using Quadient DataCleaner, users build their cleansing rules and use various scenarios to ensure optimum data profiling. For users looking for an easy to use and cost-effective data profiling, DataCleaner might be ideal.
Data Ladder is an open-source data quality tool that helps analysts use quality data through deduplication and profiling. The tool provides a comprehensive view of data quality and identifies recurring patterns, blank values and field data types. This helps analysts find the exact number of non-filled or blank spaces present in the data set. Due to the Data Ladder's design, analysts can even count the number of distinct values.
With Data Ladder, organisations conduct frequency analysis, statistical analysis, pattern recognition and data validation. From allowing users to review the presence of duplicate values to highlighting how many columns have letters, numbers, leading spaces and non-printable characters, it is a useful tool in data profiling.
Another intuitive, user-friendly and powerful profiling tool is the Apache Griffin. Unlike other tools, Apache Griffin provides a unified process for measuring data quality, considering different perspectives. This helps in building trusted data assets that ensure an organisation's growth. Using this tool, users define their data quality requirements, such as completeness, accuracy, profiling or duplication. The tool processes the data and generates insightful reports based on the requirements. These reports help an analyst make critical business decisions.
For analysts not wanting to define data quality requirements, Apache Griffin offers a well-defined domain model comprising commonly encountered data quality problems. This simplifies the entire process of data quality and profiling.
Another powerful data profiling tool is OpenRefine, which primarily handles messy data and focuses on transforming and cleaning data. OpenRefine is a Java-based tool allowing analysts and users to clean, load, reconcile and understand data. This tool makes it easier to import a large dataset, clean it and prepare it for analysis. It is ideal for organisations that want to clean a large dataset without manual intervention.
Apart from cleaning and managing the data, the tool identifies errors and outliers that are otherwise hard to find. Though the tool resembles a spreadsheet, it works like a relational database, allowing users to understand, clean and reconcile data and even augment it with data available on the internet.
Another data profiling tool widely used is Ataccama. The tool allows analysts to convert data into meaningful insights. What differentiates Ataccama from others is its ability to profile data directly from the browser and perform transformations on any data. Ataccama uses the power of artificial intelligence or AI to identify anomalies during data loading and notify the anomalies to analysts or users. The tool's self-learning engine detects data domains and assigns data quality rules from the rule library.
This tool applies data quality everywhere, including data lineage and business domains. Another area where it outperforms others is detecting changes automatically, taking action and improving data quality.
Talend Open Studio is famous data quality and profiling tool. Using Talend Open Studio, analysts can access and examine the data and gain valuable insights. The best part of Talend is its refreshing UI and interface. Another exciting feature is the large community that helps users with any issues. The tool uses data integration tasks in real-time and even in batch. Some common features include cleaning data, integrating data from various sources and analysing characteristics of text fields.
One unique value proposition of Open Studio is its ability to match time-series data. When delivering reports, the tool presents information in tables and graphs and displays the result of profiling each data element. Without adding any code, users can analyse the data ranging from simple data profiling to profiling based on different fields. It helps validate custom or standard patterns. It even allows users to apply custom rules to the data and determine data that fails to conform to internal organisational standards.
Paid data profiling tools
Here are some paid data profiling tools to consider:
Another excellent data profiling tool to consider is the InfoSphere Information Analyser, which understands data quality, structure and content for consistency and quality. Using this tool, analysts improve data quality and accuracy by identifying abnormalities and making inferences. With InfoShere, analysts reduce the risk of increasing incorrect information with data quality and monitoring. Making this tool a part of the data management toolkit reduces downstream costs. It helps in correcting data structure or validity issues before they affect the success of any project.
Interestingly, the tool offers excellent integration with other data governance and integration of the products offered by the organisation. Unlike many data profiling tools with a steeper learning curve, this one is easy to use.
The Enterprise Data Quality tool is a comprehensive data profiling tool widely used for operation-based organisations' data quality processes. The tool allows users to govern data quality while ensuring data improvement and protection. Due to the application's interface and capabilities, the tool allows governance, master data management, business intelligence and data integration. One unique value proposition of the tool is standardising the created fields, poorly structured data, incorrectly filed data and even notes field. It provides an ideal data quality management environment that helps understand, improve, protect and govern quality.
It even offers integrated data quality in customer relationships and management. What differentiates this tool from others is its ability to handle every type of data, including asset, customer, product and operational. Due to the design, users can process a large volume of data, which empowers them to validate and transform data quality rules.
Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.
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