Posted in

在生命科学领域构建一个健壮的数据管理工具_AI阅读总结 — 包阅AI

包阅导读总结

1.

– 数据管理、数据治理、数据托管、Databricks、生命科学

2.

本文探讨了生命科学领域中数据治理工具的构建,指出数据托管是组织数据策略的关键,现有工具存在诸多问题,团队开发了基于 Databricks 的新工具,它具有高效、简便、经济等特点,能优化数据托管流程。

3.

– 数据治理的重要性

– 主数据管理系统是组织的重要支柱

– 数据托管依靠人工干预解决边缘案例

– 现有数据托管工具的挑战

– 选项众多但适配性有限

– 操作复杂,需大量培训和资源投入

– 解决方案

– 开发基于 Databricks 环境的新工具

– 结合效率、简便和经济性

– 具有直接连接、实时更新等优势

– 定制用户界面,集成 AI 和 ML 工具

– 总结

– Databricks 数据托管工具是数据管理进化的基石,能优化流程和提升数据质量

思维导图:

文章地址:https://www.databricks.com/blog/building-robust-data-stewardship-tool-life-sciences

文章来源:databricks.com

作者:Databricks

发布时间:2024/8/16 10:01

语言:英文

总字数:663字

预计阅读时间:3分钟

评分:90分

标签:数据管理,数据治理,生命科学,Databricks,Python


以下为原文内容

本内容来源于用户推荐转载,旨在分享知识与观点,如有侵权请联系删除 联系邮箱 media@ilingban.com

This blog was written in collaboration with Gordon Strodel, Director, Data Strategy & Analytics Capability, in addition to Abhinav Batra, Associate Principal, Enterprise Data Management Practice Lead, Nitin Jindal, Enterprise Architect, and Abhimanyu Jain, Business Technology Solutions Manager at ZS

Data stewardship: a key component of an organization’s data strategy

Master data management (MDM) systems have long stood as an essential pillar within any well-structured organization. Over time, the advancements in MDM frameworks have greatly amplified their ability to automate, standardize and cleanse an organization’s customer data. Despite these enhancements, there remains a persistent challenge: the unsolved edge cases that require the direct intervention of a data steward.

Data stewardship, a critical element of an organization’s data management strategy, relies on manual intervention to address these edge cases. These data stewards demand intuitive tools to navigate, manipulate and manage customer profiles effectively.

The challenge with data stewardship tooling today: many options, limited fit

There are thousands of market solutions tools for data stewardship, but many of these options don’t fit the selective use case each business unit has. It’s operationally inefficient to manage business unit-level complexities at an enterprise level, as existing tools are heavy, complicated to use and require extensive training. Furthermore, they demand considerable investment, both financially and in terms of time spent on the configuration setup, therefore it becomes a substantial drain on resources for the organization. Moreover, these tools are best suited for businesses with a high influx of data for mastering and stewardship.

How did we address this problem?

Considering these challenges, our team recognized the need for a solution that combines efficiency, simplicity and affordability. Our response is the development of a new tool within the Databricks environment leveraging Databricks widgets and Python hypertext markup language (HTML) tags, which is a last-mile business unit-centric data stewardship tool that is lightweight yet robust for customer bridging use cases.

This innovative tool has been designed to streamline the data stewardship process within a business unit. Not only does it eliminate the complexity often associated with other market solutions, but it also provides an intuitive user interface fine-tuned to solve specific challenges and opportunities and significantly ease the job of a data steward.

The lightweight yet powerful stewardship tool was developed using a business with an average influx rate of around 250 records per week and doesn’t demand a full-fledged data stewardship tool, such as Reltio.

How Databricks helps with data stewardship

In the complex landscape of data management, the need for robust, flexible and efficient tools is more pressing than ever. Data stewardship, a critical component of this process, requires a platform that can adapt to complex challenges and scale with a business’ growing needs.

But why should a business choose Databricks for this important role? The answer lies in a unique combination of attributes that offer unparalleled advantages in terms of managing and leveraging data. The case for using Databricks as a platform for light data stewardship is compelling from the point of view of flexibility and scalability powered by Python to modern features such as Databricks widgets.

Key system components
Key system components

With this solution, we achieved:

  1. Direct connectivity, eliminating the use of third-party tools
  2. Real-time updates, leading to faster turnaround times in the business
  3. Flexibility and scalability
  4. User interface customized to the needs of our users
  5. Integration with AI and ML tools to foster predictive analytics

Learn more about our approach

The Databricks UI-based data stewardship tool stands as a cornerstone in the evolution of data management processes. Through its seamless integration with the Databricks ecosystem, it not only streamlines data stewardship within business units but also significantly enhances the overall quality and accuracy of merged results. The intuitive user interface, coupled with advanced algorithms, transforms the data stewardship experience from reactive to proactive, promoting a more agile and efficient approach.

Learn more about how we approached this project, its architecture, features and the step-by-step framework we used to drive stronger data stewardship in our organization.

Read more