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Tableau is the world’s leading AI-powered analytics platform. Whether you are a business user or an analyst, Tableau turns trusted data into actionable insights. With our flexible, interoperable platf
Tableau is a data visualization tool that allows users to create charts and dashboards through a drag-and-drop interface, even with large datasets. Users like Tableau's simplicity in creating visuals, its ability to load data from multiple sources, and its quick time refresh feature. Reviewers mentioned that Tableau can be expensive, especially for small teams and individual users, and advanced calculations and customization sometimes require a steep learning curve.
SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and e
SAS Viya is a cloud-native platform that provides detailed keyword and sentiment analysis, and allows users to customize categories for analysis. Reviewers appreciate SAS Viya's scalability, seamless integration of data preparation, advanced analytics, and machine learning within a single platform, and its user-friendly UI combined with powerful statistical capabilities. Users mentioned that SAS Viya has a steep learning curve for new users, especially when transitioning from open-source ecosystems like Python, and its cost structure could be improved.
Domo's AI and Data Products Platform empowers organizations to turn data into actionable insights and solutions. It allows users to seamlessly connect diverse data sources, prepare data for use, and g
Domo is a business intelligence tool used to aggregate data from various sources and display it in a unified manner, with features such as custom visualization and app creation. Reviewers appreciate Domo's ease of use, its ability to cater to non-technical users, and its Magic ETL feature which simplifies data transformation and visualization. Users experienced issues with Domo's performance during product launches, difficulties in data cleaning and sorting, and complexities in the pricing model and licensing.
Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier perf
Alteryx is a data science and analytics platform that allows users to prepare, clean, manipulate, and analyze data through a drag and drop interface. Reviewers frequently mention the platform's ability to automate repetitive tasks, handle large datasets, and streamline data processes, allowing teams to focus more on insights rather than data wrangling. Reviewers noted that the pricing is on the higher side, performance can slow down with very large workflows, and there are issues with data type mismatches and software crashes.
Operations Hub connects, cleanses, and automates customer data across the HubSpot CRM, providing operations teams with tools to maintain data quality, ensure system integration, and streamline busines
dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documenta
DemandTools is the secure data quality platform that ensures your data remains your most valuable asset. With DemandTools, you manage your CRM data in minutes, not months, so you always have accura
Incorta is the first and only open data delivery platform that enables real-time analysis of live, detailed data across all systems of record—without the need for complex ETL processes. By enabling di
Qlik Sense empowers people to make better data-driven decisions and take action. The solution provides augmented analytics for every business need from visualization and dashboards to natural language
What is Coalesce? Coalesce is the only data transformation platform built for scale, governance, and the AI-driven future. The platform provides data teams with an intuitive yet powerful interface
DataGroomr is a modern, AI-powered platform purpose-built to ensure exceptional data quality in Salesforce. For organizations that rely on Salesforce to drive sales, marketing, customer support, op
AWS Glue is a serverless data integration service that makes it easier for analytics users to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and app
OWOX Data Marts is a Free Forever Self-Service Analytics Platform that brings live, trusted data to spreadsheets and dashboards of decision-makers. Ask in plain English or click to build – your truste
Visier gives organizations a Workforce AI Edge: a set of AI-powered capabilities that help leaders understand the relationship between people and work, elevate employee productivity, and win by adapti
Visier is an analytics tool designed to centralize and analyze People data, offering various types of analysis and filtering options. Users frequently mention the ease of integrating multiple data sources into the system and the ability to create graphs and data from different sources, making data-driven decisions more accessible and quicker. Reviewers mentioned the lack of strong customer support, challenges in customizing graphs and layouts, and the complexity of the tool, especially when working with Visier Extend or trying to understand data functions behind the scenes.
Gathr.ai powers AI with complete data context for higher quality intelligence. With day-zero, high-fidelity data discourse, users can get data-backed answers to the ‘why’, ‘what-if’, and ‘how do I’ qu
Gathr.ai is a data warehouse intelligence tool that allows users to ask sales-related questions in natural language and receive data-driven answers. Reviewers frequently mention the ease of building pipelines and configuring workflows, the no-code/low-code approach for analytics, and the ability to generate data-driven insights from various platforms. Reviewers noted that while the product is generally solid, it would be beneficial to have more advanced resources or walkthroughs focused on real-time analytics patterns, and more frequent updates to the documentation.
The amount of data companies collect is staggering. Even a mid-sized business can quickly generate millions of raw data points about its customers, business, and technology performance. As a company’s analytics multiply, proper data management can become insurmountable for even the most seasoned data prep expert — not to mention companies without a specialist on hand. Data prep tools are designed to rummage through this pile of data and aggregate relevant insights for users. These tools are increasingly valuable and necessary for businesses with an endless influx of large data sets. These tools help draw valuable conclusions about important data points through the noise of excess information.
A popular term for this process is called data wrangling. Data wrangling evokes the full capabilities of these tools. They can mine useful, relevant analytics from an overwhelming stream of different data sources. Modern businesses must make timely, critical decisions in response to the diverse insights generated by these data wrangling tools. These tools compile real-time analytics about product users, sales numbers, system performance, and more. The tools in this emerging space help streamline the data preparation process, gleaning precise information from large data sets. As a business’s data piles up, data prep tools enable users to find important data points with the push of a button. This way, companies can leverage actionable insights immediately without sorting through hours of data.
In the early days of analytics, a small team would be responsible for manually preparing data — managing quality assurance for an entire company’s database, and pulling together actionable insights. This is still the case for thousands of organizations across multiple industries. As technology advances, the volume of unstructured data has grown immensely. People generate more data than businesses know what to do with, creating a unique and unprecedented challenge for data science experts and executives trying to make sense of the analytics. Data prep technology was created out of this growing necessity, with the ability to pick through massive amounts of unstructured data and present only the data points that matter for a given scenario. This relieves IT specialists of this strenuous task and makes an impossible amount of data more digestible.
In addition to finding, profiling, and combining data based on user specifications, certain solutions in this category assist with data transformation or converting data types into different forms or structures for analysis purposes. This creates a unified view of the most relevant analytics for convenient analysis and eventual exporting into external systems. Just as the amount of data has increased in recent years, so has the variety of data types, formats, and sources. Data preparation platforms work to identify or profile the most valuable data across these various types and deliver it in the most useful way for each new scenario. These advanced tools can save employees time while creating opportunities with previously unattainable data, especially if a business has an extensive portfolio of data sources.
The solutions in this category benefit companies with a substantial pool of data and a complex network of data sources. For smaller companies in certain industries, data prep may still be a manual process that does not require new technology. However, since many organizations utilize various types of software and third-party partnerships, they generate mountains of data on a daily basis. As a result, more and more businesses are eligible for these tools.
The following teams or individuals will most likely use these solutions in a given organization.
IT specialists — If a company has an IT department, these employees are the most logical choice for general data and test data preparation. IT specialists already have a comprehensive view of the computer systems and software platforms used across an organization. They may already be the primary owners of analytics tasks such as data enrichment and data cleaning. The analytics platforms featured in this category empower IT specialists to expedite the quality assurance process and create clean data sets for internal use or to be shared across their organization.
Data analysts and engineers — As the data realm has swelled in size, tech-forward companies have started to seek designated employees to collect and draw conclusions from company analytics. These data analyst roles are typical in organizational structures and third-party agency settings, such as data governance services providers. Whether employed with one of these firms or on a company’s full-time staff, data specialists benefit from one of the tools in this space. In some cases, data prep will be a daily responsibility in this line of work. Pulling various data sets for additional analysis or tests and using the results to influence business outcomes emphasizes the impact this technology can have on a given organization. The correct data prep solution can be an indispensable asset for data engineers, analytics executives, and others with a strong focus on data work.
The robust tools in this software category offer a diverse range of functionalities related to the process of data preparation. The following are some prominent features of these unique offerings.
Workflow scheduling and monitoring — Depending on the intended use of these tools, employees may want to map out an automated query to prepare certain groupings of data regularly. This might involve a custom data flow builder or a similar user interface for customization. Using these tools, administrators can adjust the specific details of each workflow, including analytics filters, which sources to pull from, and the schedule for executing the query. A company may be able to adjust other components of the process, such as validation details and the destination for exporting finished data sets. Dashboards on some tools can help display analytics related to data prep workflows, including general efficiency and results summaries.
As a company creates data prep queries, whether for one-off events or routine workflows, a company may be able to configure the data blending and joining process as it relates to each function. Data blending is another common term used to describe the merging of analytics from separate sets into a cohesive group to draw conclusions and continued analysis. When configuring the intelligent algorithms on these platforms, companies can specify how they want the data joined together and presented, for instance, which data type they prefer and how the data should be ordered. Whether called data preparation, data wrangling, or data blending, the solutions in this category can assist with this increasingly popular business strategy to help bring divergent analytics together for a unified purpose.
Data profiling — Once the intended analytics are pulled and organized using these tools, certain platforms can assess the data and help determine the additional purposes it can be used for. This is also known as data profiling. Some tools in this category offer more powerful profiling features than others, allowing for rich analytics and summaries about prepared data sets as they are constructed. If data profiling features are not present, a company might assign certain data analysts or other specialists to profile the finished data sets and determine the best course of action to take as the results are delivered.
When selecting a data preparation tool, consider a few key factors to ensure it aligns with your unique data needs and organizational resources.
First, assess your data's complexity and your team's technical skill level. Some tools are better suited for advanced technical users with programming knowledge, while others are designed for ease of use, making them accessible to non-technical team members. Look for a tool that strikes the right balance between functionality and usability for your team.
Next, think about performance and scalability. As your data grows, your tool should be able to handle increased volumes without a dip in efficiency. Make sure the tool integrates smoothly with your existing infrastructure, such as cloud storage, data lakes, or on-premises systems, to avoid compatibility issues down the line.
Don’t overlook the specific needs of your data workflows. Consider how often your data is updated and whether you need real-time processing capabilities. Advanced features like data profiling, which helps uncover patterns and quality issues, or specialized data transformation options might be essential for more complex datasets. Evaluate these aspects carefully to ensure the tool meets your immediate and long-term data preparation needs.
By evaluating these factors, you’ll be well on your way to choosing a data preparation tool that meets your current requirements and can scale as your organization grows.