Providing an enterprise-wide data store has been one aim of the enterprise data warehouse since the 1990s. One of the key lessons learned was that resolving issues of meaning and context — metadata — was central to any successful implementation. The challenges remain: very few data warehouse teams have claimed anywhere near complete success. It is also interesting to note that these issues have, finally, been recognized by data lake proponents. Tools offering big data governance, data wrangling, and similar function have begun to emerge over the last year or so. Unfortunately, once again, the tools precede an understanding of the true extent of the problem: how to traverse from data and information to knowledge and finally meaning and vice versa?
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