If data is the new bacon, data stewardship supplies its nutrition label!
This is the first part of a two-part blog introducing Data Steward Studio (DSS) and discusses the problems that DSS addresses in the enterprise data landscape. Part 2 of this blog will cover a detailed capability walkthrough.
Data lakes, which promise to provide business agility coupled with rapid time to insights, have become the backbone of a scalable modern data architectures across enterprises today. With the ability to store all types of data for longer periods of time and immense flexibility to query and access data using multiple methods, data lakes aim to provide an elastic converged platform with data management services focused on business use cases that can deliver financial value through relevant insights. However, the adoption of data lakes across enterprises is plagued with anti-patterns such as:
With the convergence of cloud, IoT, and big data technologies, data lakes provide the critical fuel for enterprise-wide digital transformations. As businesses are required to store their data simultaneously in multiple data lakes or ponds separated across many geographies (for example, due to regulatory and compliance mandates that limit cross-border data transfer) and across multiple cloud platforms, the importance of effective centralized data management of such hybrid environments becomes paramount. Such data management needs a nuanced balance of governance and democratization of data. Providing adequate stewardship with the right set of rules and policies around data security and privacy as well as rational policy enforcement across the information supply chain is critical to adoption and value creation. Enterprises now require a “global insight fabric” that can find a happy medium between adequate rules and policies of data governance while providing a trusted environment for users to collaborate and share data responsibly to create value from such modern data architectures.
At Hortonworks, our strong belief is that the journey to value creation with data lakes is at an inflection point: a global insight fabric that provides a common way to manage, secure, govern this rapidly evolving, dynamic, multi-location, virtualized, multi-cloud hybrid environment is critical for collaborative value creation in most enterprises. Our approach is to provide robust capabilities via the Hortonworks DataPlane Service (DPS), an enterprise-grade global insight fabric, which focuses on enabling customers to accelerate capturing of insights, to distill knowledge from the underlying data via collaboration faster, and to realize an adequate return on their data lake investments quickly. In order to comprehensively address such challenges with understanding your hybrid and multi-cloud data lake environments, in April, we unveiled Data Steward Studio (DSS) at DataWorks Summit in Berlin. DSS is the second service to be generally available on the DPS platform. The launch of DSS is a recognition of many key challenges faced by enterprises today:
All of these challenges create a significant chasm between initial data capture and subsequent data insights to drive value creation. DSS, as an enabler of this global insight fabric, is designed to address this gap.
With DSS, businesses will be able to understand their data in their hybrid data lake environments from a 360-degree perspective with our robust ‘DISCOVER’ approach:
DSS empowers enterprises to precisely identify and evaluate trust levels of their data, to securely collaborate, and to confidently democratize data across the enterprise in order to derive value from the data in their data lakes – whether these data lakes are located in on-premise data centers or in the cloud or across multiple cloud provider environments.
DSS enables various personas across enterprises, such as information stewards, data scientists, business analysts, and data engineers, with robust capabilities to find, curate, collaborate, secure, and report on data and its context through an intuitive user experience. The context can include key characteristics of the data such as its structure, metadata, classifications, lineage, sources, governance and security rules and policies, and usage audits across data lakes.
With DSS, data stewards, engineers, and analysts are easily able to:
In summary, DSS enables enterprises to contextualize knowledge about data located across hybrid data lake platforms which in turn allows them to take meaningful actions or generate actionable insights about their business operations, reducing the lag between insight discovery and value creation.
To learn more visit https://hortonworks.com/products/data-services/data-steward-studio/