Warehousing application and recent trends
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For this reason, businesses that have data warehouses are upgrading and augmenting them with technologies such as Hadoop and in-memory processing, which help with “big data” workloads that are 10 times or 100 times or 1,000 times bigger than before. Meanwhile, businesses that have relied on piecemeal data-analysis solutions in the past are now establishing data warehouses to get a more complete picture of the enterprise. For more on that, see my recent article, “Healthcare’s Next Innovation? The Answer Is In The Data.”
The “datafication” of the enterprise requires more capable data warehouses. Mobile devices, social media traffic, networked sensors (i.e. the Internet of Things), and other sources are generating a growing stream of data—some would say a fire hose of data. IT teams are responding by adding new capabilities to data warehouses so they can handle new types of data, more data, and do so faster than ever.Physical and logical consolidation help reduce costs. The answer to datafication isn’t to throw more money at these systems. Or put another way, ten times the data shouldn’t translate into ten times the cost. So burgeoning data warehouses must be consolidated, through a combination of virtualization, compression, multi-tenant databases, and servers that are engineered to handle much larger data volumes and workloads.Hadoop optimizes data warehouse environments. The open source Hadoop program, with its distributed file system (HDFS) and parallel MapReduce paradigm, excels at processing very large data sets. That makes Hadoop a great companion to “standard” data warehouses and explains why a growing number of data warehouse managers are now using Hadoop to shoulder some of the heaviest workloads.Customer experience (CX) strategies use real-time analytics to improve marketing campaigns. Data warehouses play a pivotal role in CX initiatives because they house the data used to establish a comprehensive, 360-degree view of your customer base. A data warehouse of customer information can be used for sentiment analysis, personalization, marketing automation, sales, and customer service.Engineered systems are becoming a preferred approach for large scale information management. These kinds of in-database analytics capabilities minimize the need to move data back and forth to other systems and applications for analysis, resulting in more streamlined and optimized data discovery.In-memory technologies supercharge performance. The emergence of in-memory database architecture brings race car-like performance to data warehouses. The term in memory is highly descriptive, of course. It refers to the ability to process large data sets in system RAM, accelerating number-crunching and reporting of actionable information.Data warehouses are more critical than ever to business operations. While it’s true that data warehouses have been around for years, their value keeps growing because they represent a company’s crown jewels—prized data on customers and business performance. And organizations are finding new applications for data warehouses, as described in the example above, where healthcare providers are using enterprise data warehouses to improve patient care and streamline operations.
Taken together, these Top 10 trends describe a new generation of data warehouses that are bigger, better, and faster than ever before, transforming data into information and information into actionable insights, enabling businesses to forge ahead with unprecedented speed and agility. For an in-depth report on all of this, download the Oracle white paper, “Top 10 Data Warehousing Trends and Opportunities for 2014.”
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