“What should you and your business focus on when trying to create better customer journeys and beat competition?” That was the question we asked Countly data captains (also known as Countly customers) when trying to determine how well they collect customer experience metrics and how well they were using that data to make data-driven decisions.
Domino’s Pizza, one of the world’s top restaurant brands, already knows how to translate data into great customer experiences and stronger sales with Talend. Over the past five years, the company has used Talend to integrate 100’s data sources into a single source of customer information — and has harnessed that data to improve everything from personalized promotions to logistics to financial forecasting. The latest ingredient to their success?
In 2022, digital natives and traditional enterprises find themselves with a better understanding of data warehousing, protection, and governance. But machine learning and the ethical application of artificial intelligence and machine learning (AI/ML) remain open questions, promising to drive better results if only their power can be safely harnessed.
Are you efficiently unifying, modeling, analyzing, and activating all the data you need to drive impactful marketing campaigns and customer experiences? For years, marketing teams have struggled to operate from a single view of the customer and their business, essential to powering personalized experiences and measuring impact on key KPIs such as sales, growth, and profitability. Today, only half of all marketers have a unified view of the customer.
Chief Data & Analytics Officer UK (CDAO UK) is the United Kingdom’s premier event for senior data and analytics executives. The three-day event, with more than 200 attendees and 50+ industry-leading speakers, was packed with case studies, thought leadership, and practical advice around data culture, data quality and governance, building a data workforce, data strategy, metadata management, AI/MLOps, self-service strategies, and more.
Data teams have the impossible task of delivering everything (data and workloads) everywhere (on premise and in all clouds) all at once (with little to no latency). They are being bombarded with literature about seemingly independent new trends like data mesh and data fabric while dealing with the reality of having to work with hybrid architectures. Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem.
