How to Operationalize Data Mapping for Engineering

As organizations seek to deliver new applications and services, they often also need highly scalable and lightning-fast data management platforms to support these innovations. For instance, some new applications work against millions or billions of rows of data and require a response time in milliseconds. These might include financial services applications that need to process transactions with sub-millisecond response rates and be globally available.

How important are data and analytics to a banker’s bottom-line objectives – growing deposits, loans and the customer base? In the latest Digital Banking Report, AI for Improved Customer Experience, almost 90 percent of respondents indicated that advanced analytics is extremely or very important to capabilities like cross-channel contextual communications, proactive advice and audience targeting.

Fanatics, a popular sports apparel website and fan gear merchandiser, needed to ingest terabytes of data from multiple historical and streaming sources transactional, e-commerce, and back-office systems to a data lake on Amazon S3. Once ingested, the data would be analyzed to better identify, predict, and fulfill customer needs related to the products Fanatics offers in over 300 online and offline stores.

To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don’t know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents. In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.