Atlan > Case Studies > Data Governance and Active Metadata Management: A Case Study on Elastic

Data Governance and Active Metadata Management: A Case Study on Elastic

Technology Category

Infrastructure as a Service (IaaS) - Cloud Middleware & Microservices
Sensors - Autonomous Driving Sensors

Applicable Industries

Buildings
Cement

Applicable Functions

Quality Assurance
Sales & Marketing

Use Cases

Inventory Management
Time Sensitive Networking

Services

System Integration
Training

About The Customer

Elastic is a legendary technology company that started as a search engine built by Shay Banon in 2000. It has since grown into a company that powers search solutions, helps applications run smoothly, and protects against cyber threats for the world’s most respected organizations. With a rich history in open source, Elastic's offerings are built on a foundation of simplicity, speed, scale, and relevance, and powered by data. The company is distributed by nature, and its team comes from diverse backgrounds, with vast differences in geography and culture. This makes it crucial for any solution to be relevant and personalized to a spectrum of needs, expectations, and skill sets.

The Challenge

Elastic, a technology company that powers search solutions and protects against cyber threats, faced several challenges in its data practice. Takashi Ueki, Director of Enterprise Data & Analytics at Elastic, identified multiple sources of truth leading to disconnected reporting, a BI platform strategy that was misaligned with organizational needs, and varying definitions making it difficult to accurately and consistently report. Elastic's distributed and remote nature, along with its diverse team, made it crucial for any solution to be relevant and personalized to a spectrum of needs, expectations, and skill sets. The company's data governance strategy needed to drive transparency, accountability, and engagement, and be embedded seamlessly into the day-to-day experience of its distributed workforce.

The Solution

To address these challenges, Elastic's data team considered technology that could deliver fit-for-purpose experiences to their diverse stakeholders, make clear available data and capabilities, and serve as a collaboration layer. They moved forward with proofs of concept with four vendors, ultimately choosing Atlan for its clean UI, ease of adoption, and integration with their current stack. Atlan became a crucial part of Elastic’s data architecture toolkit, helping to make more informed decisions, better onboard users, and drive efficiency. The team spent roughly 4 weeks to integrate key data sources and invite a core set of users, enriching data assets, defining terms, and utilizing automated lineage. They integrated key systems like BigQuery, dbt, Tableau, and Fivetran, enabled seamless login with Okta, and used Slack to share data assets and track data issues.

Operational Impact

The adoption of Atlan has led to several operational benefits for Elastic. It has helped the company address pipeline breakages and errors proactively, advancing Elastic’s principle of transparency and building trust with stakeholders. Atlan's integration with Jira and dbt has allowed the team to automate alerts, further improving efficiency. The tool has also helped Elastic identify the most and least used assets in their data warehouse, and the cost to execute queries, enabling them to optimize the performance and cost of their data estate. Looking forward, Elastic plans to drive ownership and accountability, drive adoption through personalization, and maximize the return on investment on critical data technology.

Quantitative Benefit

Elastic spent roughly 4 weeks to integrate key data sources and invite a core set of users.
Elastic has been on the Atlan journey for 9 or 10 months, making significant strides across enriching data assets, defining terms, and utilizing automated lineage.
Elastic has had 572 Chrome Extension views, indicating growing engagement from end users.