
Unravel Data
Radically simplifying the management and of big data applications and systems.
USD | 2018 | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|---|
Revenues | 0000 | 0000 | 0000 | 0000 | 0000 |
% growth | - | - | (48 %) | 67 % | 38 % |
EBITDA | 0000 | 0000 | 0000 | 0000 | 0000 |
Profit | 0000 | 0000 | 0000 | 0000 | 0000 |
EV | 0000 | 0000 | 0000 | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
R&D budget | 0000 | 0000 | 0000 | 0000 | 0000 |
Source: Dealroom estimates
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Unravel Data is a technology startup that operates in the data analytics and artificial intelligence (AI) industry. The company provides a platform that uses AI to optimize the efficiency, reliability, and performance of data analytics. Unravel's AI is designed to automate tasks for data teams, helping them to understand not just what happened with their data, but why it happened and how it can be fixed. This allows data teams to focus more on innovation rather than troubleshooting issues.
Unravel's clients are primarily businesses that rely heavily on data analytics and AI, such as tech companies, financial institutions, and product-based businesses. These businesses use Unravel's platform to ensure that their data pipelines and AI models run reliably, that their data platform is cost-effective and scales efficiently, and that their data applications generate accurate results.
Unravel's business model is likely based on a subscription or usage-based pricing model, where clients pay for the level of service they require. This could be based on the amount of data they need to manage, the number of users, or the complexity of their data analytics needs.
Unravel makes money by providing a valuable service that helps businesses to manage their data more effectively and efficiently. By automating tasks and providing insights into data performance, Unravel helps businesses to save time and money, and to make more informed decisions.
Keywords: Data Analytics, Artificial Intelligence, Automation, Data Management, Efficiency, Reliability, Performance Optimization, Data Pipelines, Cost-Effective, Scalability.