Guide to the Lakehouse: Unite your data teams in the cloud to bridge the information gap

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3 Guide to the Lakehouse From punch cards to semiconductors to the cloud The evolution of data storage and analytics has spanned decades. There have been tremendous advances in the technologies used to store and process data. But it's the increasing need for data-driven insights that propels people from both the business and technical side of the organization to try to take greater advantage of all the data that our systems, products, and services create. Although we're changing the way we store and compute data, a few challenges remain the same. The first is preparing data in a timely manner. The second is providing engineers, data scientists, and business users with quality data that they can use to derive insights and make transformative decisions for the business. The last is bringing different types of data workers together by providing a truly collaborative environment in which data culture can flourish. This last challenge, the collaborative environment, may get less attention, but is perhaps the most important foundation of modern analytics. Gaps in information–both data and communication gaps-can hinder progress. Building deeper customer relationships is one of the most transformative things a business can do. And for deeper relationships to happen, organizations need to bridge the gaps with a comprehensive understanding of buying and consumer behavior, as well as the business systems necessary to support this process. Data is critical, yes, and obtaining and leveraging the data generated across the entire business requires cross-functional effort and cooperation. However, according to NewVantage Partners' "Big Data and AI Executive Survey 2021," 92.2 percent of leading companies continue to identify culture, including people and processes, as the biggest impediment to becoming truly data-driven organizations. The good news is that the right technology can help drive the kind of cultural change that transforms business: Bringing together data engineers, data scientists, business analysts, and others whose jobs and performance depend on having quality data, to work together to reduce costs, drive innovation, and shorten time to market.

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