Webinar: Extending BI With Big Data Analytics

CockroachDB has already defined what it means to be a cloud database and its unique architecture delivers some key innovations. Now with CockroachDB Serverless we use CockroachDB’s architecture to remove all barriers between developers and the promise of a massively scalable, familiar SQL database that can power applications of any size.

From AI and machine learning to data discovery and real-time analytics, a strong data architecture strategy is critical to supporting your organization's data-driven goals. Greater speed, flexibility, and scalability are common wish-list items, alongside smarter data governance and security capabilities. Many new technologies and approaches have come to the forefront of data architecture discussions, including data lakes, in-memory databases and engines like Spark and cloud services of all shapes and sizes.

Organizations that operate worldwide typically need to manage data both locally and globally. Local business units and subsidiaries must address region-specific data and accounting standards, regulations, customer requirements, and market drivers. At the same time, corporate headquarters must share data broadly and maintain a complete view of performance for the entire enterprise. For many global firms, data is the business. They need state-of-the-art data management just to remain innovative and competitive. Hence, multinational businesses face a long list of new business and technical requirements for modern data management.

Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.