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E X E C U T I V E B R I E F

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.”

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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

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

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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.

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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 processing-based 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

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

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Executive Brief

Learn More

Learn more about the Informatica Platform. Visit us at www.informatica.com or call +1 650-385-5000 (1-800-653-3871 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 +1 650-385-5000 (1-800-653-3871 in the U.S.), or visit www. informatica.com. Connect with Informatica at http://www.facebook.com/InformaticaCorporation, http://www.linkedin.com/company/informatica and http://twitter.com/InformaticaCorp.

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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.

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