Big Data Meets Business

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1 E XECUTIVE BRIEF Big Data Meets Business Informatica Executive Insights Vol. 2 Executives increasingly recognize that data holds tremendous value that remains largely untapped. The advent of Big Data exponential growth in traditional transactional data plus new information from social media, call detail records, sensors and devices, and geolocation systems has made it imperative that executives take the lead in harnessing Big Data for competitive advantage. To turn Big Data into big business opportunities, both business and IT executives are challenged to rethink their information management practices, break down organizational and data silos, and improve business/it collaboration. Gartner summed up the challenge: Big Data is a disruptive force and an immediate problem that is already affecting traditional understanding and business models. It represents a huge opportunity that public sector, business, and IT leaders cannot afford to ignore. 1 1 Gartner, CEO Advisory: Big Data Equals Big Opportunity, March 2011.

2 To turn Big Data into big opportunities, leading organizations are paying greater attention to data integration to provide more actionable, trustworthy, and timely information across all their business units and geographic locations. They are putting data integration to work for uncovering new insights as well as facilitating the integration of business processes. The end results involve driving growth, reducing costs, and delivering innovations through spotting patterns and trends while meeting compliance and risk mandates more effectively. But how do you accomplish that? This article addresses questions frequently asked by business and technology leaders interested in Big Data. What exactly is Big Data and what big opportunities can it offer to business? Big Data is the confluence of three megatrends: Big Transaction Data, Big Interaction Data, and Big Data processing. Each involves continuous innovation and tremendous promise. Big Transaction Data: Organizations are using such innovations as data warehousing appliances to handle massive volumes of transaction data. Enterprises that can process and analyze all relevant data without resorting to data samples have a better understanding of their business. Big Interaction Data: Businesses are leveraging data from Facebook, Twitter, LinkedIn, and other social media sources to crowd-source new product ideas, take cues for operational improvements, and build a more complete customer view important for one-to-one marketing. Organizations are also using device-generated data to improve yields, geolocational data to spot anomalies, and scientific and genomic data to accelerate R&D. Big Data Processing: The open source Hadoop framework has emerged as a cost-effective means of processing and storing large volumes of data and as a platform for analytics on unstructured data. Backed by industry leaders such as Facebook, Yahoo!, and Google, Hadoop is a powerful, evolving ecosystem supported by an open source community. Big Data is also being shaped by three technology trends cloud computing, social computing, and mobile computing. These fast-growth areas are redefining how we run our businesses and look for the next best opportunity, the next best customer, and ultimately, the next best innovation. Without the ability to integrate data, these opportunities can slip through your fingers. These trends are also driving greater data volume and diversity, as well as bigger demand for historical and real-time delivery of data. Big Data integration uniquely enables an organization to achieve the full value of Big Data. A Big Data integration platform helps you harness the value of data: trustworthy and secure transaction data to analyze, authoritative and relevant interaction data to relate, timely and actionable data to correlate, and holistic and accessible data to process. We have heavily invested in IT systems and saw some mixed results from our investments. Can we use our current infrastructure to take advantage of Big Data opportunities? Traditionally, many organizations have focused on application infrastructure and business process optimization. With Big Data, you can use some of what you built from an application perspective; you also need to rethink data from end to end from how it enters your enterprise to how to best present it to end users, who can benefit from it the most. For example, if you are just using social media data on a small scale for spotting customer complaints and shaping support or PR responses, you can keep the status quo. If you want to leverage customer or partner feedback on social media to improve sales, marketing, procurement, manufacturing, or R&D, you need to mine the data and use the analytic insights to gain a complete picture, identify opportunities and weaknesses, and establish or modify processes. For example, you can enrich a single customer view you ve established through master data management 2

3 Executive Brief with Big Interaction Data such as social media. In this case, you likely need to adjust or revamp your architecture to feed the right data to the right business processes and departments. Do we need to change our organization to succeed in managing Big Data? How can business and IT work together? Some enterprises do make organizational changes to help facilitate the transformation. In particular, organizations that don t have clear ownership of information management including predictive analytics, data mining, business intelligence (BI), data warehousing (DW), data management, and data modeling may appoint an owner to promote collaboration and alignment between business and IT in gathering requirements, designing and implementing Big Data systems, and measuring performance. This collaboration becomes more crucial because of additional subsystems and business units that are impacted by nontraditional sources relevant to Big Data projects. Other organizations that may not have an official role for information management can broaden the scope of the leader in advanced analytics, BI, or enterprise DW. Here arethe key factors for the organization: It must be business-focused and think from a perspective of an end-to-end business use of information and associated processes. It needs to define user-driven service-level agreements (SLAs) and understand system implications. Big Data SLA metrics usually cover data latency, data accuracy and consistency, and data availability and uptime. How you define these metrics is particularly important; many organizations naturally choose high-priority, time-sensitive projects for Big Data. It must use Big Data to complement its business strategy and technical infrastructure, instead of reinventing the entire wheel. Business or IT areas that can deliver early dividends in customer acquisitions and loyalty may be suited for early Big Data initiatives. The office of the CTO or VP of engineering may be well positioned to lead or significantly contribute to Big Data projects or Hadoop implementations because the staff often has the necessary expertise and a strong familiarity with the business side. Another area of business and IT alignment can be pursued with data integration. A Big Data integration platform will provide self-service capabilities for business and data analysts to define requirements, enabling IT to more rapidly design and implement solutions. Realizing business benefits from Big Data sounds interesting but also very challenging. Are any companies actually using Big Data and gaining value from it today? Absolutely. Here are a few examples: T-Mobile USA: T-Mobile uses Informatica PowerCenter to integrate data across a disparate federated architecture, including a Hadoop implementation that supports advanced customer churn analysis based on CDRs, Web logs, billing data, social media information, and more. By combining Big Transaction and Big Interaction Data, T-Mobile is gaining a more complete view of the customer and the reasons behind churn, and cut its churn in half in a single quarter. US Xpress: With a far-reaching program called No Data Left Behind, trucking company US Xpress collects 900 data elements from tens of thousands of trucking systems: sensor data for tire and gas usage, engine operation, geospatial data for fleet tracking, and complaints and feedback posted on blogs and social media sites like Facebook and Twitter by their truck drivers, customers, and partners. Using Hadoop and Informatica technology, US Xpress processes and analyzes this Big Data to optimize fleet usage, reducing idle time and fuel consumption and saving millions of dollars a year. HealthNow: A healthcare insurer and service provider in upstate New York, HealthNow uses Informatica Data Services to support and streamline a service-oriented architecture spanning 16 enterprise sources, reducing data infrastructure complexity and providing a single, trusted view. Its success has positioned it to leverage Big Data, including data from social networks and the unstructured data in claims and medical notes, to support predictive analytics on healthcare issues. The company is exploring Hadoop as the framework for this Big Data initiative. Big Data Meets Business 3

4 Direct Edge: One of the big four U.S. stock exchanges, Direct Edge is meeting the low- latency and highthroughput demands of Big Data, characterized by massive volumes and extreme velocity. Direct Edge uses Informatica Ultra Messaging as part of a next-generation trading platform that has slashed latency by 83 percent and quadrupled its peak capacity, while providing a single data integration backbone that spans from front-end order gateways to back-end compliance systems and supports all market data, order information, and trade flow. We have significant initiatives around business intelligence (BI) and data warehousing (DW). Is there something wrong with using that as a platform? No. In fact, many organizations start Big Data projects by focusing on core BI/DW strengths of providing analytical and operational data from structured sources. Later, they incorporate Big Data oriented technologies that traditional systems were not designed to handle, such as complex analysis and predictive modeling based on unstructured data, behavioral analysis, and natural language processingbased text analysis. In those cases, organizations are incorporating a new Big Data processing platform, such as Hadoop, to augment their BI/DW environments. Hadoop helps organizations access a class of analytic tools available from new and existing vendors and take advantage of CPU and scale cost-effectively. Ultimately, relational database systems and BI are critical parts of the Big Data equation because they are proven for handling mixed workloads with prioritized handling based on query characteristics. They are also geared to ensuring data governance and data quality, as well as concurrent execution of data tasks that may not be suited for distributed, batch systems like Hadoop. Big Data is on the radar, but we have other high-priority projects. Is there something we can do now while preparing to start Big Data projects down the road? Yes you don t need to jump into Big Data projects right away. You can start rethinking your data strategy for traditional data projects. For example, establish a strong foundation that can scale to fast-changing demands and accommodate new data of large volume and timeliness requirements, ranging from historical records to those requiring microsecond processing. Also, focus on building a data-centric culture that bridges the gap between business and IT and puts data at the heart of any business initiative. A data-centric approach that ensures the delivery of trusted, complete data is essential to any data project and a key ingredient in successfully harnessing Big Data. As part of a data-centric culture, rethink data vs. process structures, in which applications and business processes are separate from data sources and data consumers. A data-centric infrastructure enables you to dissolve those barriers and position your organization to capitalize on Big Data opportunities. This is timely because we are in the middle of scoping out our Big Data strategies. How do I get started? The key is to pick the right Big Data project that you can rally your organization around. Once you pick the right project, you must clarify what specific changes you want to see in targeted business areas and develop metrics so that IT and business can scope out requirements. We have found that many Big Data projects focus on customer centricity, with initiatives aimed at improved cross-sell and up-sell or new customer acquisitions, because of the high payback and relative ease of marshalling a concerted effort. It s also important to incorporate lean management principles so that your organization can become lean and agile for any data project, especially critical when dealing with Big Data. To get started, consider the three key dimensions mentioned earlier: Big Transaction Data: Can you handle the massive growth of transaction data? What is missing? Big Interaction Data: What s your social media strategy? Do you plan to incorporate insights from social media with transaction data from applications and transactional systems? Is locational data part of your strategy? What other interaction data would be of high value? Big Data Processing: Do you have projects that are suited for the unique strengths of Hadoop? How do you integrate data and data processing results from Hadoop for further analysis and processing? To learn more, please explore these additional resources: Big Data Unleashed White Paper Big Data Related Blog ESG Research on Informatica 9.1 and Integrating Big Data Lean Integration White Paper 4

5 Executive Brief Learn More Learn more about the Informatica Platform. Visit us at or call ( in the U.S.). About Informatica Informatica Corporation (NASDAQ: INFA) is the world s number one independent provider of data integration software. Organizations around the world rely on Informatica to gain a competitive advantage with timely, relevant and trustworthy data for their top business imperatives. Worldwide, over 4,440 enterprises depend on Informatica for data integration, data quality and big data solutions to access, integrate and trust their information assets residing on-premise and in the Cloud. For more information, call ( in the U.S.), or visit www. informatica.com. Connect with Informatica at and Big Data Meets Business 5

6 This document contains Confidential, Proprietary and Trade Secret Information ( Confidential Information ) of Informatica Corporation and may not be copied, distributed, duplicated, or otherwise reproduced in any manner without the prior written consent of Informatica. While every attempt has been made to ensure that the information in this document is accurate and complete, some typographical errors or technical inaccuracies may exist. Informatica does not accept responsibility for any kind of loss resulting from the use of information contained in this document. The information contained in this document is subject to change without notice. The incorporation of the product attributes discussed in these materials into any release or upgrade of any Informatica software product as well as the timing of any such release or upgrade is at the sole discretion of Informatica. Protected by one or more of the following U.S. Patents: 6,032,158; 5,794,246; 6,014,670; 6,339,775; 6,044,374; 6,208,990; 6,208,990; 6,850,947; 6,895,471; or by the following pending U.S. Patents: 09/644,280; 10/966,046; 10/727,700. This edition published September 2011 Worldwide Headquarters, 100 Cardinal Way, Redwood City, CA 94063, USA phone: fax: toll-free in the US: Informatica Corporation. All rights reserved. Printed in the U.S.A. Informatica, the Informatica logo, and The Data Integration Company are trademarks or registered trademarks of Informatica Corporation in the United States and in jurisdictions throughout the world. All other company and product names may be trade names or trademarks of their respective owners. First Published: October (10/06/2011)

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