Mural Cuts Data Access Time to Minutes with Databricks Integration
With the implementation of Genie Agents through Databricks, Mural has transformed its data access, enabling employees to obtain answers in minutes and streamlining decision-making processes across the organization.
Key Facts
- Mural reduced data request wait times from weeks to minutes, enhancing operational efficiency.
- Transition to Databricks improved data governance, enabling self-service analytics across teams.
- Genie Agents empowered employees to ask exploratory questions, shifting from routine requests.
- Enhanced reporting capabilities increased customer self-service, improving satisfaction and retention.
- Mural's analytics now centralized in Databricks, streamlining decision-making and competitive positioning.
Summary
Summary
Mural, a company providing shared visual workspaces, faced delays in data access due to a small analytics team and a complex data environment. By implementing Genie Agents on the Databricks Data + AI Platform, Mural enabled employees to obtain answers to data questions in minutes, significantly reducing the reliance on the analytics team. This transformation led to faster decision-making and improved data accessibility across the organization.
Background
Mural operates in the technology sector, offering collaborative visual workspaces to companies like IBM, SAP, and Deloitte. Prior to deploying Genie Agents, Mural's analytics team, consisting of five to seven members, struggled to keep up with data requests from various departments. Requests often took one to two weeks to fulfill, leading to frustration among employees who needed timely insights.
Challenge
The primary challenge was the slow response time for data inquiries, which stemmed from a limited analytics team and an overwhelming number of tables in Mural's Amazon Redshift warehouse. Employees were unsure which tables to trust due to inconsistent data quality and a lack of clear standards, making it difficult for anyone to get reliable answers quickly.
Solution
Mural transitioned to the Databricks Data + AI Platform to establish a governed data foundation. They adopted Unity Catalog, migrating their data over the course of a year and restructuring it using a medallion architecture. This involved organizing data into bronze, silver, and gold tiers, retiring duplicate tables, and ensuring that permissions were correctly set for Genie Agents. The first Genie Agent was deployed in customer support, allowing employees to access data independently, with subsequent adoption across various teams.
Results
The implementation of Genie Agents drastically improved data access. Employees can now retrieve answers in minutes, reducing the number of routine requests to the analytics team. For example, Jeremy Moskowitz, VP of Marketing, was able to obtain critical account-level figures in about ten minutes without needing to contact the data team. The analytics team now receives fewer questions about data location, allowing them to focus on enhancing the platform.
Key Insights
Mural's experience highlights the importance of establishing a governed data foundation to enable self-service analytics. By empowering employees with tools like Genie Agents, organizations can reduce bottlenecks, foster curiosity, and encourage exploratory data analysis, moving away from reliance on static dashboards.
Customer Testimonial
"I got the numbers I needed without having to bug anyone on the data team. It got me out of a really tight jam." — Jeremy Moskowitz, VP of Marketing at Mural.
Entities Mentioned
Companies
Products
Technologies
People
Key Concepts
Definitions
- Genie Agents
- AI-powered tools that allow employees to ask questions about company data in plain language and receive answers quickly.
- Unity Catalog
- A feature in Databricks that provides data governance and access management for data assets.
- medallion architecture
- A data architecture approach that organizes data into bronze, silver, and gold tiers for better management and access.
- self-service analytics
- A method that enables users to access and analyze data independently without needing assistance from data teams.
- MLflow
- An open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment.
Use Cases
- →Customer support using Genie Agents for quick data retrieval
- →Sales teams researching accounts with Genie Agents
- →Generating metric calculations for the gold layer
- →Building internal apps with Genie Code
- →Creating AI/BI Dashboards for product managers
- →Providing enterprise customers with in-product ROI reports
Frequently Asked Questions
What are Genie Agents?
Genie Agents are AI tools integrated into the Databricks Platform that allow employees to ask data-related questions in natural language. They provide quick answers without the need for coding or waiting for data analysts.
How does Mural ensure data quality?
Mural ensures data quality by implementing a governed data foundation with the help of Databricks. This includes using Unity Catalog for data governance and organizing data into a medallion architecture to maintain consistency.
What benefits do employees gain from using Genie Agents?
Employees benefit from faster access to data, often receiving answers in minutes instead of waiting for analysts. This empowers them to explore data independently and ask more insightful questions.
How does Mural's data team support Genie Agents?
Initially, Mural's data team reviewed the questions posed to Genie Agents to ensure accuracy. As usage increased, they focused on improving the platform and enhancing the capabilities of Genie Agents.
What is the significance of the medallion architecture?
The medallion architecture is significant because it organizes data into structured tiers—bronze, silver, and gold—allowing for better data management, quality control, and accessibility for various teams within Mural.