Five Best Practices for Maximizing Big Data ROI

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1 E-PAPER FEBRUARY 2014 Five Best Practices for Maximizing Big Data ROI Lessons from early adopters show how IT can deliver better business results at less cost. TW_

2 Organizations of all kinds have begun to capitalize on the tremendous opportunities presented by big data. Consider these examples: eharmony s big data infrastructure investment processes 30GB of data every day to help 20 million registered users find the relationship they seek. FleetRisk Advisors gleans predictive insights from 4,500 operational parameters to help customers reduce minor accidents by 20 percent and major accidents by 80 percent. Visa International performs analytics on 20,000 transactions per second to avoid $1.5 billion in fraud losses. SUNY Buffalo examines more than 2,000 genetic and environmental factors in just minutes to develop breakthrough therapies and prevention strategies for multiple sclerosis (MS). How can other organizations achieve these kinds of results? And how can they ensure the maximum return on their investment in big data capabilities? The answer to this question is complex and will vary considerably among organizations, given differences in their strategic objectives and existing IT resources. The experiences of successful early adopters, however, reveal best practices that can significantly contribute to higher big data ROI. 1. Be Smart About Infrastructure Decisions Organizations starting out on their big data journey often focus first on the data science issues associated with analytics and reporting. Ultimately, those analytics and reports will deliver value to the organization through actionable insights, so it may seem logical to begin by figuring out which data sets should be included in the big data initiative and what kind of insight might be gained from them. But experienced big data adopters know that it s as important to make the What s Driving Interest in Big Data? 1. Predicting customer behavior 2. Correlating disparate data sources 3. Predicting sales 4. Predicting risk/fraud 5. Analyzing clickstreams 6. Identifying computer security risks 7. Analyzing social content for customer sentiment 8. Utilizing machine data from sensors and other sources Source: InformationWeek 2014 Analytics, BI and Information Management Survey

3 right infrastructure investments as it is to ask the right data science questions. In fact, achieving maximum ROI on big data is largely contingent on ensuring that investments in big data infrastructure generate the results the organization is looking for as efficiently and effectively as possible. When it comes to big data infrastructure, the DBMS is of particular importance. After all, while analytics may be applied to many data sets, transactional systems and structured data remain the primary data source for most big data efforts. The wrong DBMS can easily become the weak link in any big data initiative undermining ROI by both limiting IT s ability to deliver desired results and excessively consuming IT s finite resources. 2. Ensure Speed at Scale To get the right answers from big data, it s important to be able to work with data on a massive scale. Analyzing complete data sets rather than samples of data will help eliminate sampling skew and provide more accurate results. Big data infrastructure must be able to scale to whatever volume of data is necessary for extracting those answers. But it s also important to get those answers on time. When business users need to make a decision, they need as much information as possible to guide them. They need insight from big data, and they need that insight now not in a week or even a day. Providing the right information and insight to business users at the right time empowers them to make better decisions faster and with more confidence. And those better decisions can cumulatively have a substantial positive impact on the bottom line. It s not useful to offer customers a potentially enticing deal two weeks after they ve already made a purchase from a competitor. And it s better to stop a fraudulent transaction while it s occurring than it is to allow the transaction to The big data infrastructure at eharmony processes 30GB of data daily to help 20 million users find relationships. Getting the Most from Existing IT Staff Resources As organizations start to grapple with big data, they need to make sure they have the right skills in their IT departments. IT staff may need to learn new skills, or the organization may need to go outside and hire new skills. The question is how to execute on big data innovation if IT staff has to spend so much of its time and energy on managing, tuning and administering existing systems. The right DBMS can offer a compelling answer to this question by providing faster and easier deployment, streamlined management and administration, and support for rolling maintenance updates. These attributes significantly reduce burdens on IT staffs reducing costs and freeing up staff resources so they can be applied to a broader range of big data projects.

4 go forward and chase restitution afterwards. Big data success isn t just about scale. It s about achieving speed at that scale. This need for speed at scale makes it vital to implement the right infrastructure. A DBMS that can continually ingest new data without affecting running query performance and can process terabytes of data as fast as traditional databases process gigabytes is essential. Typically, this requires a variety of specialized capabilities that optimize performance including in-memory columnar database architecture, parallel vector processing, data skipping and intelligent I/O caching and prefetching. 3. Choose Multipurpose Solutions Many organizations make the mistake of allowing their initial use-case to drive their big data infrastructure decisions only to find that their second and third big data use cases require something entirely different. Big data isn t just a single use case or even a single category of use cases. The term big data is actually better understood as referring to a phenomenon in the world at large: the relentless generation of massive volumes of highly diverse data with potentially great value if properly analyzed. To reap this value, organizations must be able to effectively and adaptively execute a wide range of big data use cases including: online transaction processing multiple types of analytics (operational, and predictive) mobile data serving To successfully stretch budgets, IT decision makers have to consider the full range of costs associated with initial big data implementation. These costs include hardware, software, installation/integration tasks and staff training. A DBMS that requires a lot of configuration, physical modeling, index creation and tuning to achieve desired performance will push initial implementation costs up. So will a IT organizations need to make infrastructure investments Visa DBMS that doesn t readily integrate with existing applications that can be leveraged across International these diverse use cases to keep costs down and options open as business needs evolve. This means it s important to choose a DBMS analyzes 20,000 transactions per second to avoid $1.5 billion in fraud losses. that may be running on popular platforms such as Oracle and Microsoft SQL Server. That s why organizations looking for quick, high-roi big platform that is readily adaptable to a broad range of data types, workloads and applications. data wins should adopt a DBMS that delivers great performance out of the box and that can support existing instances of third-party databases. 4. Minimize Implementation Costs Given the constraints on IT budgets, it s important to keep implementation costs down and deployment timelines short especially in early adoption stages, when business benefits have not yet materialized. 5. Plan Ahead for Low Long-Term TCO When it comes to big data, one thing seems certain: Tomorrow, it will be bigger data. And bigger data brings bigger storage costs. While storage is getting less expensive on a

5 per-gb basis, explosive data growth poses a threat to IT budgets. A good DBMS can help better utilize existing storage and let organizations push off the decision to buy more through data compression. With sufficiently effective compression, the right DBMS can reduce storage costs by more than 70 percent. In addition, with techniques such as operating on compressed data without decompression, skipping nonrelevant data and intelligently distributing data across media based on access frequency (which IBM calls multi-temperature data management ), the right DBMS can perform well while still using legacy servers. This can enable IT to defer purchases of additional hardware and can even save on software licensing costs by reducing the number of cores needed to support big data workloads. Why the DBMS Matters Big data can offer tremendous insights to the business. But to get those insights on time and at the right cost, IT has to make the right infrastructure decisions especially regarding its strategic DBMS platform. By embracing a DBMS that supports diverse big data workloads, delivers exceptional big data performance and keeps costs under control, IT decision makers can substantially improve big data ROI today and tomorrow. SUNY Buffalo examines 2,000-plus genetic and other factors in minutes to develop new therapies for MS. Accelerating Time to Benefit In the early stages of any big data initiative, it s important to start delivering tangible business results as soon as possible. Project delays cost the business opportunities and undermine IT s credibility regarding big data. The sooner IT can start delivering results, the more likely it is to get the resources it needs to continue expanding its big data efforts. The right DBMS can help accelerate time to benefit in many ways. Some DBMS platforms require IT staff to do physical modeling, create indexes, tune memory and I/O, determine ETL logic and perform other time-consuming work before big data workloads can be successfully processed. IBM is the world s largest information technology company, with 80 years of leadership in helping clients innovate. Drawing on a breadth of capabilities and best practices from across IBM and our extensive partner ecosystem, we offer clients within every industry a wide range of services, solutions and technologies that can help them improve productivity, respond rapidly to the needs of their business and reduce development and operations costs. A DBMS that reduces or even eliminates these tasks increases ROI and enables IT to start delivering business benefits earlier. Quick delivery is vital whether the business is eager to start reaping big data benefits or it needs to see immediate, tangible proof to be convinced of big data s value UBM LLC. All rights reserved.