We re performing on average eight times better and in some cases as much as 30 times better, yet using only 12 percent of the power.

Transcription

1 Top 10 Data Warehousing Trends and Opportunities for 2014

2 Most enterprises are overwhelmed with data. Information is now being generated much more quickly than it can be consumed. Meanwhile, leveraging information to make complete business decisions is becoming ever more critical to enterprises. All these factors increase the demand for comprehensive information management solutions. Which information management opportunities are CIOs putting at the top of their corporate agendas? What are the most compelling developments in data warehousing that motivate IT leaders to undertake new initiatives? This brief highlights the most noteworthy trends, problems, and breakthroughs in data warehouse technology. It also discusses best practices that will help your organization deal with this data deluge. Whether implementing in-memory analytics to boost performance or compressing information to create more-efficient data centers, forward-thinking companies depend on the following innovations to take the lead in their respective markets. 1. The datafication of the enterprise spawns more-capable data warehouses. We re performing on average eight times better and in some cases as much as 30 times better, yet using only 12 percent of the power. Chief Operating Officer, Online Marketing Firm Historically, data warehouses were populated with structured business data from enterprise applications. Today, however, data is pouring in from human-generated, cloud-generated, and machine-generated sources more than 90 percent of which is unstructured. This data can be collected not only from computers, but also from billions of mobile phones, tens of billions of social media posts, and an everexpanding array of networked sensors from cars, utility meters, shipping containers, shop floor equipment, point-of-sale terminals, and many other sources. With the help of new big data technologies, data warehouses are expanding in variety and scope. This improves the quality and speed of business decision-making as people learn how to acquire, organize, and analyze this massive influx of information. For example, manufacturing companies commonly embed sensors in their machinery to monitor usage patterns, predict maintenance problems, and enhance production quality. Studying the data streaming from these sensors allows them to improve their products and devise more-accurate service cycles. CIOs are responding to an increased use of data in the enterprise by deploying newer, faster, and more-capable information management systems. 2

3 2. Physical and logical consolidation reduces costs. By consolidating multiple databases onto a single platform, we ve saved tens of millions of dollars. Director of Infrastructure and Security, Satellite Navigation Company Consolidation allows organizations to maximize IT resources and minimize idle compute cycles. This in turn lowers costs because fewer resources are required to achieve the same outcomes. Data warehouse applications that experience peak loads at different times of the day can share the same hardware, which means fewer management tasks and less IT overhead. Many organizations are achieving tremendous levels of consolidation with engineered systems. These purpose-driven systems combine hardware and software to upend the long-standing industry practice of cobbling together information systems from scratch. Database, storage, and networking gear are engineered together to ensure a simpler and more flexible approach to database consolidation. Today s data warehouses also streamline how the data is structured, stored, and compressed. For example, software breakthroughs such as multitenancy let database administrators manage many databases as one, yet retain the isolation and control of separate databases. By tackling complexity and simplifying IT practices, organizations can focus on creating value for the business. 3. Hadoop optimizes data warehousing environments by accelerating data transformation. Data transformation is the process of converting data from one format to another format. For example, analysts might want to convert a stream of machinegenerated shop floor data or social media data into a semistructured form suited for pattern recognition and analysis in a data warehouse. Organizations that must contend with big data are increasingly using Hadoop an open source software that enables organizations to process huge amounts of data across inexpensive servers and storage devices to augment their traditional data warehouse assets. Hadoop clusters present a cost-effective platform for storing and transforming any kind of data before pushing it into a data warehouse for analysis. Hadoop is also flexible enough to accommodate machine data, documents, and social media. Hadoop clusters perform these high-volume operations by offloading resourceintensive activities to a dedicated, low-cost transformation platform. 3

4 Our data warehouse provides our hotel managers and corporate executives with near real-time information to make optimal business decisions and provide ideal amenities for our guests. Business Relationship Manager, Major Hotel Chain 4. Customer experience (CX) strategies gain real-time insight to improve marketing campaigns. The explosion of big data presents a unique opportunity to segment and target customers. Analysts are studying real-time, human-generated big data streams from social media, GPS, and mobile devices. Astute companies are enhancing their marketing campaigns and promotions by identifying meaningful patterns in this information. For example, marketing professionals tap into these streams of big data to observe how their products, services, and brands are perceived online. Measuring, analyzing, and ultimately influencing customer sentiment allows marketing professionals to formulate more-effective promotions so they can improve customer loyalty and generate more revenue. Today s data warehouses help people study more than customer information from sales orders. They can consider all the ways that customers interact and tailor the customer experience accordingly. Marketers, sales reps, and customer service personnel can better understand what customers need from moment to moment. For example, retailers can combine social media feeds, customer demographic information, purchase data, and network usage data to paint a complete picture of each customer s behavior. Harnessing this information in a data warehouse and analyzing it on-demand helps these professionals present personalized, tailored offerings to their customers. With engineered systems, we save space in the data center, and as an added benefit, we get 10x performance improvement. Manager of Database Administration, European Communications Company 5. Engineered systems become the de facto standard for large-scale information management activities. Fast-moving organizations embrace engineered systems for their data warehouse initiatives because they provide a simpler, cheaper, more flexible way to get the job done. Instead of ordering, installing, and integrating servers, storage arrays, networking gear, peripherals, and management software, these organizations work with one vendor to order complete, preconfigured systems. Engineered systems ensure optimum performance, with software and hardware working in harmony to create a cohesive database platform. They are especially popular for data warehouse solutions because they perform better and are fully integrated and simpler to maintain. Engineered systems accelerate time to market since customers can immediately start loading data into the database. They are optimized at every layer of the technology stack, and they feature technical advancements such as columnar compression, Flash memory, and intelligent caching to improve performance. 4

5 6. On-demand sandbox analytics environments meet rising demand for rapid prototyping and information discovery. Business intelligence and analytics are resource-intensive activities that lend themselves to on-demand computing due to their iterative nature and fluctuating workloads. Forward-looking organizations are establishing analytics as a service (AaaS) environments within public and private clouds. These versatile sandbox environments can flex with a shifting volume and velocity of data, making them ideal for analyzing energy usage, monitoring shop floor operations, gauging consumer sentiment, and undertaking many other large-scale analytics challenges. Cutting-edge technologies, such as multitenant databases, enable organizations to set up one cloud environment with dozens or even hundreds of pluggable databases for people to use. Some leading companies are even monetizing their AaaS environments by setting up subscriber networks for other companies in their supply chains. On-demand provisioning makes it easy for business users both inside and outside the enterprise to utilize these environments as they respond to high-velocity demands of big data analysis. 7. Data compression enables high-value analytics. Hybrid columnar compression is giving us 10x to 15x the compression, and the performance improvement is astounding. Director of Database Development, Marketing Solutions Company Even though the cost of storage has been declining dramatically, the enormous growth in the volume of data makes storage one of the biggest cost elements of most IT budgets. For many fields of inquiry, more data means higher-quality analysis. There is no shortage of information and there is a growing knowledgebase about how to use it. But there is also a pressing need for hardware and software systems that can accommodate this crushing volume of data, necessitating larger warehouses and a need for scalability. Today s compression technologies utilize a combination of row and columnar methods for storing data to save space and improve performance. Volume is proportional to value: more data means more-valuable analytics. But that doesn t mean the IT budget needs to expand with the data. Compression technologies can generate 10x the value without having to spend 10x the cost. 5

6 8. In-database analytics simplifies data-driven analysis. We are an analytics-driven company and we need an environment that is going to keep up with our analysts train of thought. CTO, International Marketing Services Firm In-database analytics programs include a variety of techniques for finding patterns and relationships in large volumes of data. Popular in-database analytics capabilities include data-mining algorithms implemented in the database, native SQL functions for basic statistical activities, and integration with statistical programming languages like R. Because these techniques are applied directly within the database, analysts can eliminate data movement to and from servers, which accelerates cycle times and reduces total cost of ownership. By not having to move the data to a separate analytics warehouse, analysts achieve faster time to insight and IT pros gain an easier, better-automated environment. Organizations can more easily tackle big data projects by minimizing data movement and ensuring better security, scalability, and performance. Indatabase data mining tools let them create complex models and deploy them on very large data sets. Examples include predictive models to target top customers, identify fraud, and predict market fluctuations. 9. In-memory technologies supercharge data warehouse performance. Queries that used to take 10 minutes with the previous platform are now down to a few seconds. MIS Manager, UK Grocery Retailer Making the most of big data means quickly acquiring and analyzing a high volume of data generated in many different formats. All warehouse data used to be stored on magnetic disks. Now that data is being moved into RAM to achieve performance improvements that are orders of magnitude faster than previous methods. Thanks to new database capabilities, the entire data warehouse doesn t need to be placed in-memory at the same time, enabling even the largest data warehouses to gain inmemory performance benefits. Data warehouse administrators can configure these environments to optimize data among RAM, Flash, and disk-based access methods based on heuristic access patterns. Important or frequently accessed data resides in RAM where processing becomes instantaneous. This lightning-fast database processing can entirely eliminate the need to create analytics indexes. All that power opens up new analytics opportunities. For example, a restaurant chain might want to generate a daily consumption report to do dynamic spot promotions the following day. A bank might want to monitor unusual purchase patterns in real time to minimize loss from fraudulent credit card transactions. 6

7 10. Data warehouses become more critical to business operations. We saw an average of 133x improvement with our key queries without any costly application rewrites. We expect the compression capabilities to save us on storage costs downstream as well. Senior Manager, IT Development, Pacific Region Bank Many data warehousing workloads are mission-critical for running business operations. Whether analyzing sales performance or studying weather patterns, enterprises rely on warehouse information to support operational analytics and make sound business decisions. For example, a retailer might monitor weather fluctuations to determine which goods to stock on its shelves. A distributor might analyze incoming orders throughout the day to send demand signals to manufacturers for more goods each night. This level of criticality necessitates highly available data warehouse solutions that ensure continuous access to information even during planned and unplanned outages. Applying software patches, consolidating databases, and migrating information to new platforms must be done without compromising user activities. The security of the data is equally important, especially in industries such as banking and healthcare that have strict regulations about personally identifiable information. Today s data warehouses are no longer just deployed for usage by a few back-office analysts. They are foundational parts of the information architecture. This makes availability and security essential. For more information on Oracle Data Warehouse solutions, visit oracle.com/datawarehouse. 7

8 Oracle Corporation World Headquarters 500 Oracle Parkway Redwood Shores, CA U.S.A. Worldwide Inquiries: Phone: Fax: oracle.com Copyright 2013, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only and the contents hereof are subject to change without notice. This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are trademarks or registered trademarks of Advanced Micro Devices. UNIX is a registered trademark of The Open Group.