How Real-World Enterprises are Leveraging Generative AI
Generative AI (GenAI) is moving incredibly fast. So much so, that in less than two years, GenAI has emerged as one of the...
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.
With this solution, we achieved:
- Direct connectivity, eliminating the use of third-party tools
- Real-time updates, leading to faster turnaround times in the business
- Flexibility and scalability
- User interface customized to the needs of our users
- 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.
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