Big Data and Your Data Warehouse
Just a few years ago, big data was a problem in terms of scaling up IT systems and discovering the business value. Thanks to advances in vendor platforms and user practices, most enterprises today consider big data an opportunity—not a problem—because they can mine and analyze it for valuable business insight.
However, getting full business intelligence (BI) value out of big data requires some adjustments to best practices and tools for enterprise data warehouses (EDWs). Oddly enough, scaling up an EDW to large data volumes is a challenge, but not the only one or the most pressing one. For example, big data tends to get big because much of it is coming from new sources, such as websites, social media, robotics, mobile devices, and sensors. Deciding which of these are useful for BI/DW purposes pushes BI professionals to think in new terms.
Big data from these sources ranges from structured to semi-structured to unstructured, and most EDWs are not designed by their users to store and manage the full range. Likewise, some of the new big data sources feed data relentlessly in real time, whereas the average EDW is not designed for such feeds. Most of the business value coming from big data is derived from advanced analytics, based on the combination of both traditional enterprise data and new data sources. So you will need to modify your tried-and-true best practices for EDW data integration, data quality, and data modeling in order to take advantage of big data.
Big data and your data warehouse can be a powerful team, providing many new analytic applications that enterprises need to stay competitive. But you will need to make some changes to your existing infrastructure, tools, and processes to integrate big data into your current environment.
You will learn:
- The kind of business value that big data yields through analytics
- Analytic applications and business use cases—both old and new—that benefit from big data
- Recommendations for how best to integrate big data into your existing IT environment
Philip Russom, Ph.D.