Canalyst vs Daloopa: A Comparative Analysis

Canalyst and Daloopa share similarities in our commitment to delivering high-quality fundamental and historical data on publicly traded companies.

Canalyst and its parent company Tegus is focused on providing in-depth expert interviews, datasets, and ready-to-use financial models, whereas Daloopa emphasizes on automating the data extraction and delivery process and allowing greater flexibility in how that data is used in financial modeling with AI. Both serve a purpose, therefore it is essential to understand the specific needs of your unique approach to financial analysis and the data infrastructure and platform to achieve your objectives.

This article aims to help equity investment and research analysts understand the differences between the two and how to use these tools to make more informed investment decisions on your coverage.

Key Takeaways

• Canalyst specializes in providing simple, ready-to-use models for analysts looking to ramp fast with basic fundamental data
• Daloopa specializes in using AI to increase the depth, accuracy, speed and flexibility for equity research analysts and investors looking to ramp and update multiple models ahead of others
• Both platforms offer distinct advantages based on specific financial data needs

How Canalyst and Daloopa are Similar

Both Canalyst and Daloopa offer robust financial data solutions that streamline processes and enhance productivity for equity investment and research professionals.

Both claim to have accurate data that is customizable and reduce time when it comes to building models and updating models.

Let’s take a deeper look into how they are similar below.

1. Financial Data Automation

• ‍Canalyst: Specializes in providing pre-built financial models that are updated with the latest company filings each quarter. These models are consistently formatted and delivered to users via their web browser.‍
• Daloopa: Automates the process of extracting financial data from company SEC filings and other sources, allowing users to create and update financial models with minimal manual input. Daloopa’s automation is designed to reduce the time spent on data entry and increase accuracy by delivering data through on online marketplace or via an Excel Add-in.

2. Accuracy and Consistency

• ‍Canalyst: Emphasizes the accuracy and consistency of their financial models, which are meticulously constructed and maintained by a team of human analysts to ensure reliability for analysts.
• ‍Daloopa: Also prioritizes accuracy through advanced machine learning algorithms that cross-check data points and AI that extracts and verifies the data, ensuring that users have reliable and consistent information for their analysis. Daloopa also has hyperlinks on every single data point to access the original source file in one click, helping analysts verify accuracy.

3. Customization and Flexibility

• ‍Canalyst: Offers financial models in Excel that allow analysts and users to tailor assumptions and forecasts according to their specific needs.
• ‍Daloopa: Provides unformatted data sheets and built-in Excel tools that allow users to easily update and customize their own financial models, with the ability to pull data from multiple sources, while maintaining the format and integrity of their own models.

4. Time Efficiency

• ‍Canalyst: Saves analysts time by providing ready-to-use models, reducing the need for manual data entry and model building.
• Daloopa: Aims to maximize efficiency by automating the data extraction process, enabling analysts to focus more on strategic decision-making and less on data gathering.

5. Target Audience

• ‍Canalyst: Primarily geared towards buy-side and sell-side analysts, asset managers, and other finance professionals who require pre-built financial models.
• Daloopa: Similarly, serves finance professionals on the buy-side and sell-side who need reliable and fast fundamental and historical data and streamlined processes for financial analysis, including those in investment management and equity research.

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Differences between Canalyst and Daloopa

Canalyst and Daloopa offer unique approaches to financial modeling and data automation, marked by differences in core offerings, technology use, and customization capabilities.

Let’s take a deeper look into how the two offerings differ below.

1. Core Offering and Focus

• ‍Canalyst: Primarily focuses on delivering pre-built financial models that cover a wide range of publicly listed companies. These models are designed to be plug-and-play, providing users with a basic starting point that includes historical financials, forecasts, and key performance metrics.
• Daloopa: Focuses more on pure data delivery and automating the data extraction and processing aspect of financial modeling. Daloopa’s strength lies in the use of AI and machine learning to pull hard-to-get financial data from various sources, including company filings and reports, and then integrate this data directly into financial models. The emphasis is on reducing the time spent on manual data entry.

2. Technology and Automation

• ‍Canalyst: While Canalyst employs automation in updating its models with the latest financial data, the core product is the ready-made financial models themselves. These models are built and maintained by a team of analysts to ensure they meet a high standard of accuracy.
• Daloopa: Leverages advanced financial analysis AI and machine learning technologies to extract, process, and update fundamental data from diverse and often unstructured sources. The technology is designed to handle large volumes of data and transform it into structured, usable formats that can be easily incorporated into financial models, emphasizing completeness, accuracy and speed delivered in a flexible format. Hyperlink technology is added to every data point for instant verification and a deeper dive into the original source document.

3. User Interaction and Customization

• ‍Canalyst: Provides users with pre-built models that can be customized, but the starting point is a complete model provided by Canalyst. The user experience is designed to be intuitive for those already familiar with financial modeling. Users can tweak assumptions, add scenarios, and modify the model as needed. ‍
• Daloopa: Offers more flexibility in terms of how data is integrated with an analyst’s model. Users can create financial models from scratch from Daloopa data sheets or update existing models that they’ve created from sell-side models or from scratch by automatically pulling in data using Daloopa’s Excel Add-in. The data service is designed for a more automated experience, with less need for manual adjustments.

4. Coverage and Breadth

• ‍Canalyst: Before Canalyst was sold to Tegus in August 2022, their website stated it covered 4,000+ public companies which is the same number Tegus still states today indicating the growth of coverage has plateaued. They have a strong emphasis on providing only the data that they determine “matters” on each company. The platform is designed to meet the needs of institutional investors and analysts who require ready-made models across many companies.
• Daloopa: Daloopa covers more than 3,600 companies globally and is adding an average of 50 per month. Daloopa also focuses on the efficiency of data extraction across multiple data sources in order to offer the most complete set of data, including hard-to-get data from multiple, unstructured sources. In 2023, Daloopa signed a deal with a large investment bank’s equity research team solidifying a coverage roadmap of 6,500 companies by 2026.

5. Primary Use Cases

• ‍Canalyst: Best suited for analysts who need ready-to-use financial models with minimal setup time. It’s ideal for users who want to start their analysis from a simplified model and then make adjustments as needed.
• Daloopa: Geared towards professionals who need to streamline the data gathering process, especially when covering multiple companies during busy earnings periods. It’s particularly useful for those who need to rapidly generate and update financial models or pull data from varied sources into a consistent format without extensive manual input. This allows users to build or update models with data that might not be as pre-packaged as Canalyst’s, but is highly customizable according to specific needs and unique workflows.

6. Human and AI Input

• ‍Canalyst: Depends on human expertise to build and maintain its financial models. The models are curated and regularly updated by a team of analysts to ensure they are accurate and relevant.
• Daloopa: Relies heavily on AI and machine learning for data extraction and processing. While this enhances speed and efficiency, it also reduces the need for manual intervention in updating or creating models, focusing more on the accuracy of data extraction and model automation. There is a human analyst team involved in QA before the data is delivered to users.

7. Integration and Ecosystem

• ‍Canalyst: Primarily focuses on delivering its models within its platform, though it does offer integration capabilities for users who wish to incorporate the data into their own systems.
• Daloopa: Daloopa is built with hundreds of AI algorithms specializing in data extraction, data organization, data delivery, as well as directly integrating into an analyst’s workflow whether that’s directly into their model or as a downloadable data sheet. 

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In summary: Canalyst and Daloopa’s Unique Value Propositions

Canalyst and Daloopa both provide robust and reliable financial data solutions but cater to slightly different needs.  

Canalyst focuses on delivering pre-built financial models for analysts. Their tools allow analysts to quickly access standardized financials, thus saving time. The platform’s data updates and intuitive interface are particularly beneficial for generalist hedge funds and asset managers that don’t prioritize speed and a broad range of data from harder-to-get sources.

In contrast, Daloopa streamlines the data extraction and delivery process for institutional investors with AI. The data service specializes in automating data collection from financial documents, enabling users to easily gather hard-to-find data points accurately in real-time. This helps reduce manual data entry and improves data reliability. Daloopa’s emphasis on automated data extraction makes it highly attractive to long/short hedge funds, asset managers and mutual funds, as well as equity research teams that require unique data sets and speed. Daloopa also has tiers of offerings for analysts that don’t prioritize speed with Daloopa Standard and the Free Account.

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Want to learn more?

If you tired of spending countless hours on manual data collection and validation, you can try Daloopa out for free.

Daloopa’s Free Account allows you to access the marketplace and download five datasheets on your own.

‍Create your free Daloopa account today and explore how you can stay ahead in the fast-paced equity markets.

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And, if you want to talk to an actual person, book a demo here.