TDWI research has found that organizations are increasingly modernizing their data warehouse environments. Often the current environment is not sufficient to support new analytics initiatives or they need to support new data types for analytics. Many enterprises are moving to the cloud as part of this journey. In fact, cloud data warehouses and cloud data lakes are already mainstream. The popularity of automated tools is growing as environments become more complex.
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With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.
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A Data Strategy is a plan for moving an organization towards a more data-driven culture. A Data Strategy is often viewed as a technical exercise. A modern and comprehensive Data Strategy addresses more than just the data; it is a roadmap that defines people, process, and technology. The people aspect includes governance, the execution and enforcement of authority, and formalization of accountability over the management of the data.
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To preview your data, you must first have access to it. This operation can be difficult when distributed into different systems and silos which is why many companies choose to bring them together in a Data Lake. However, without governance or stewardship, these data lakes are rapidly turning into swamps, veritable brakes on innovation.
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