However, there may be cases where operational analytics doesn’t simply stop at updating data but requires teams to react to that event causing the update. They can do so even if they don’t 1) understand the raw data or 2) know how to model the raw data with SQL.ĭaso: Operational analytics, so far, has been oriented around event-driven changes in the data of a user in the data warehouse being updated in the end destination, the SaaS tool. Thus, business users who own downstream tools (like CRM, email, ads, etc.) can work with refined, modeled units of data. In other words, the “translation” from raw data to usable data has historically happened through esoteric, often repetitive work done by data experts-tools like dbt and Hightouch help to centralize and automate that translation layer. 2) The data team has the knowledge and skillsets to transform and model that data into something usable downstream. 1) The data team understands the underlying structure of the raw data. We think that the status quo of data management forces ownership of data into the hands of a small team, largely for two reasons. Data is still primarily accessible to developers, and that’s the problem. It’s that tooling wasn’t built for data-savvy people to use data in their workflows. It’s not that people don’t understand data. Most roles require the use and understanding spreadsheets, BI tools, or SQL, but these roles don’t have “data” in the title. Gupta, Manohar and Curl: We believe that all users of Hightouch will become data people and that most people in a company already are data people. ![]() Hightouch also supports version control through git and configure the syncs from their command-line interface (CLI).ĭaso: Hightouch’s view is that everyone in a company using the product will become a “data person.” What are the underlying assumptions and implications of the perspective? Our live debugger helps users understand the API requests that Hightouch makes on their behalf. Hightouch can now be triggered via API, dbt, or Airflow. While a core focus of Hightouch is to serve less technical team members, the majority of our users are data engineers, so we dedicate a lot of our roadmap to unique developer features. We’ve seen companies like Blend use Hightouch for automation and workflows, putting their data into action by automating Slack messages for their services team to help meet customer deadlines instead of relying on static reports. Hightouch also supports creating notification feeds in Slack when customers do certain app actions, such as putting down a credit card. Hightouch also supports workflow-like actions such as creating tasks and tickets in Asana, Jira, Zendesk, or Salesforce. We also allow data team members to select and use their existing data models from popular tools like dbt. Customers such as Autotrader use our Visual Audience Builder, which allows team members who may not know SQL to filter customer segments visually on top of existing models (for example, targeting users who visited your site in the past 30 days). ![]() Hightouch now supports multiple ways to access data past utilizing SQL queries and filtering customer segments visually on top of existing models. ![]() We continued to build out our core competency with technical users while expanding the feature set to make the product more accessible to everyone within an organization. As a result, the scope of the product expanded. Over time, as we began working with bigger and bigger companies, analysts and business users wanted to use Hightouch to power their own workflows. Gupta, Manohar and Curl: Hightouch started as a significantly more technical product since the product’s initial users were primarily data engineers. Hightouchĭaso: Since your last fundraise, have your customer’s needs for reverse ETL evolved or expanded? If so, how have you shaped your product development to ensure Hightouch can maintain product-market fit? A graphical overview of how Hightouch works.
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