Nov 9 ADV Webinar: Data Quality: The ROI of Adding Intelligence to Data

Modern businesses rely on transformative insights into front-end customer channels, back-end business operations, and end-to-end supply chains. Data-driven insights keep modern industrial enterprises operating at peak efficiency. Nothing delivers these insights and associated business results better than modern cloud data platforms, artificial intelligence, and advanced analytics. To accelerate digital transformation, enterprises everywhere are integrating data-driven intelligence into their manufacturing, logistics, and distributed supply chains, and leveraging investments in knowledge graphs, simulation and modeling, edge computing, digital twins, the Internet of Things, and other pillars of what many are calling “Industry 4.0.”

Analytics is a core decision support tool in modern organizations. It can provide tangible benefits such as deeper insights, increased productivity, improved customer service, improved operational efficiencies that drive cost savings, and top-line growth that delivers higher profits. For innovative companies, analytics can drive new products and services. It can also provide an important competitive advantage. To maximize the return from their investments in analytics, organizations are evolving beyond traditional approaches such as spreadsheets, query tools, and reporting applications. Many are moving to deploy machine learning, natural language processing, generative AI, and other advanced analytics applications. Many are looking to data discovery approaches to find insights within their data. Many seek self-service analytics capabilities to democratize access to and use of this technology.

Leveraging enterprise data and third-party data to power strategic decision-making is essential to remain competitive and, as the new era of AI and ML unfolds, this trend is only accelerating. Yet challenges in data quality still loom large. Technical issues, like integration of siloed data sources and legacy architectures, can

Organizations who want to leverage their data lake for insights often struggle to deliver a consistent, accurate, high-quality view of their data to all of their data consumers. That challenge is often exacerbated by the need to make changes to data that impacts multiple tables. In this webinar, we’ll share how data teams can use Dremio Arctic, a data lakehouse management service, to simplify data management and operations. Using Git for Data capabilities like branching, tagging, and commits, we’ll show how Dremio Arctic makes it easier than ever to: