Looker vs Qrvey

Looker is a forerunner in the business intelligence field for a reason; it generates reports that include easy sharing via link, automatic scheduling and a level of granular detail that allows for deeper analysis below the surface. It excels in its filter and drill-down features and creates unique URLs when users make changes to data, leading to enhanced sharing. However, one of its biggest strengths could also be considered one of its biggest weaknesses: its proprietary programming language, LookML which is used to construct SQL queries in the platform. While a flexible and powerful data querying language, of course, LookML isn’t the most accessible to non-technical users, which means that Looker requires an IT or data team to access its full capabilities and has a steep learning curve. Users also note that its data visualizations, while simple and easy to understand, are quite basic and lacking in customization options, particularly in comparison to competitors. Some users say that it may be more appropriate for internal reporting than presentation to shareholders and end-users because of its bare-bones visualization options. However, Looker truly shines when used by enterprises, with its scalability and data accessibility making it a stellar solution that can align departments and provide thousands of users access to data insights. Its price point reflects this, with its pricing being prohibitive to startups as about 88% of users who comment on its cost remark. Overall, Looker is a solid pick for larger businesses that have a team of power users who can maximize its functionality and set it up to deliver to employees across an entire organization.

User reviews from the past year paint a mixed picture of Qrvey. While many appreciate its intuitive drag-and-drop interface, robust data visualization capabilities, and seamless integration with existing applications, some users have expressed concerns about its pricing model and occasional performance hiccups. Qrvey shines when it comes to its no-code approach, allowing users to build complex analytics dashboards without writing a single line of code. This is particularly valuable for businesses that lack dedicated data science teams, as it empowers non-technical users to generate insights independently. However, some users have noted that Qrvey's pricing, which is based on data volume, can become prohibitive for organizations dealing with large datasets. Additionally, while generally praised for its performance, some users have reported occasional slowdowns, particularly when handling complex queries or large datasets. Qrvey's strength lies in its ability to democratize data analytics, making it accessible to a wider audience within an organization. Its no-code platform and intuitive design make it ideal for businesses looking to empower non-technical users to explore data and generate insights. However, organizations dealing with massive datasets might find its pricing structure less appealing, and occasional performance issues might require some patience.