Data Quality Best Practices

Modern business requires agile decision-making with real-time data analytics. Innovation hinges on predictive insights generated from machine learning and AI. ETL and data warehouse infrastructures are simply not able to keep up with the growing volume, variety and velocity of data, or the need of business to gain advanced insights across enterprise data silos of disparate data marts and data warehouses. Innovation remains a pipedream.

Business-centric data models are key to gaining a clear view of the data that drives the business – from customers to products to invoices and more. They offer a clear, visual way for both business and technical stakeholders to communicate around the crucial business rules and definitions that drive both operational usage of data as well as analytics and reporting. This webinar will provide practical, concrete steps in creating valuable, business-centric data models that can show immediate value to the organization, while at the same time building towards a full-enterprise view.

Organizations are rapidly building modern data stacks to stay on the cutting edge of data-driven insights and analytics. Yet, as technology has become more advanced, the demand for data has grown just as quickly—which has led to increasingly complex data workflows and processes.

Success in the modern economy depends on an enterprise’s ability to deliver high-quality data analytics into production applications. Structured data engineering processes ensure that analytics are always accurate, relevant, and fit for purpose. Modern data engineering processes—also known as DataOps pipelines—continuously integrate, transform, and prepare data for production deployment. In order to keep pace with fast-changing enterprise requirements, enterprises need to prioritize such DataOps modernization initiatives as: