Insights for your entire team

  1. Data Leader
  2. Data Engineer
  3. Data Analyst
  4. Analytics Engineer
Use Cases
  • Data Leader

    Free up time to focus on growth

    You are helping the company grow by providing data to other teams, via BI tools, raw feeds, or custom data products. Your team is constantly being pulled into SQL support or warehouse administration. You are dealing with complexity: model sprawl, waste, etc. and this slows down efforts to help the company grow. You lack visibility into whether their data is actually being used / how it converts. You lack tools to budget your costs, or understand if you are exceeding your budget and why.

    • Free up your team to work on data projects instead of data warehouse administration or fighting fires
    • Prove how the data you are preparing is being used by the business
    • Justify cost and attribute it back to departments and users
  • Data Engineer

    Scale-up without compromising performance

    Your data platform must support to manage ever more data and workflows. You are exhausted by the many reactive problems you need to fix. You are being asked to reduce waste and complexity, and find ways to optimize for cost. You are constantly being pulled into fielding support tickets for SQL optimization.

    • Measure and troubleshoot performance of models and queries
    • Budget and find ways to be more efficient and cut costs
    • Scale the data platform without sacrificing performance and cost
  • Data Analyst

    Measure user engagement

    You are constantly performing ad-hoc analyses and managing BI tools, and writing some ELT jobs. You want to understand which dashboards are the most popular and by which people and teams. You wish you had more visibility into performance of queries.

    • Unmask end-users of BI tools like Looker, Chartio and Periscope Data
    • Rank your power users across any dimension
    • See the performance of dashboards from the perspective of the end-user
  • Analytics Engineer

    End-to-end view of models and table dependencies

    There are so many models and tables - it's very hard to understand the dependencies. Model sprawl makes it harder to make changes, and you want to know which models are being used and which can be deleted. You want to understand user intent, how new prepared tables are converting.

    • Measure how users and tools engagement with tables
    • See which models can be removed, and which are being used
    • Find downstream table dependencies on models and queries