The complexity of data supply chains and the technical infrastructures to manage them are growing every minute. Businesses are forced to be at the top of their game to leverage data to better the business and offer solutions that deliver results based on their understanding of data. Over the past decade, we have mastered building the plumbing infrastructure,but have also realized that this is just not sufficient anymore to tackle today’s business problems. The hidden signals that are mined when seemingly unrelated data elements are brought together need to be orchestrated so that the business can make intelligent, data-driven decisions. However, understanding signals or correlations acrosstime series data and dimensions is not a simple job. In most organizations, neither IT nor business has the capabilities to search for these needles in the haystack, let alone discover the correlations. Technology, math and business skills need to be combined to solve this problem. Technical solutions that utilize data lakes solve the data access problem, but leave the crucial understanding of the issues to only a few advanced users and data scientists. In this article, we discuss a method of defining and using data assets as afoundation block for faster and flexible analytics solutions.