Taking advantage of Big Data analytics

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  • 1. TAKING ADVANTAGE OF BIG DATA ANALYTICS Vaults of structured and unstructured data can point the way to higher revenue and competitive advantages. But efforts to capture and analyze big data need careful planning and firm shepherding. BY RICK SHERMAN UNLOCKING THE BUSINESS BENEFITS IN BIG DATA 2 SMALL STEPS BRING BIG REWARDS 3 ARCHITECTING A SUCCESSFUL DEPLOYMENT 4 WHO’S ON THE TEAM? 1 BIG DATA QUESTION TIME
  • 2. HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? Numerous stories have examined its use in applications from tracking customer sentiment and identifying social media trends to successfully predicting the outcome of the 2012 U.S. presidential election. Based on the amount of attention—and yes, hype—that big data technologies are receiving, one would be forgiven for thinking that their adoption and deployment is already pervasive. But the fact is that most companies are still trying to get a handle on what big data is, how to effectively manage it and how to get tangible business benefits from their invest- ments in big data tools. The first of those three questions is easy to answer: Big data envi- ronments consist of high-volume pools of information, often includ- ing a variety of structured and unstructured data types that are updated frequently. For example, data captured from social media sites, Internet clickstreams, server logs, sensors and mobile networks is commonly found in big data sys- tems. The goal is finding business value in that information—analytical insights that point to new revenue opportunities and ways to improve internal processes and operations. But managing and using big data isn’t so easy. In order to plan and implement a successful big data analytics project, an organization needs to consider a range of dif- ferent technologies and determine what kind of architecture it is going to deploy. Resource requirements are another key factor to take into account, as are the scope of the project and how it should be struc- tured and managed. Let’s take a closer look at those four elements and how best to approach them to put deployments of big data analyt- ics tools and applications on the right track. Initially, many big data projects flew under IT’s radar; they were launched independently by data analysts, programmers and technol- ogy-savvy users taking advantage of TAKING ADVANTAGE OF BIG DATA ANALYTICS 2 “BIG DATA” IS A HOT TOPIC NOT ONLY IN IT CIRCLES AND TECHNOLOGY PUBLICATIONS BUT ALSO IN BUSINESS MAGAZINES AND OTHER MAINSTREAM MEDIA OUTLETS. The fact is that most companies are still trying to get a handle on what big data is.
  • 3. TAKING ADVANTAGE OF BIG DATA ANALYTICS 3 HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? the open source nature of Hadoop and other components of the big data technology stack. But now that big data is squarely in the spotlight, projects often start off like the first generation of data warehouse, enterprise reporting and business intelligence (BI) dashboard projects did—with IT saying, “If we build it, they will come.” Whenever a new wave of technology is promoted so extensively, there’s a tendency for enterprises to buy into the hype and assume that the new technology fits their needs. Frequently, the result is expensive projects that fail to meet expectations and set back future efforts to invest in, and benefit from, the technology in question. 1 BIG DATA QUESTION TIME Before blithely beginning a big data project, get answers to the following questions: D Why is the business interested in big data? What are the long-term business objectives for implement- ing big data analytics applications? Is it, for example, to track what is trending on social networks? Increase the effectiveness of mar- keting campaigns? Improve supply chain performance? Knowing the “why” is essential to establishing the business scope and determining the expected return on investment (ROI) for these projects. D Where in the organization is big data going to be used? Once you know why you’re building a big data analytics system, you need to cata- log the business processes, applica- tions and data sources that will be involved. That information is essen- tial to assessing the impact not just from a technology perspective but also from the standpoint of people, processes and the corporate culture so you can develop a change man- agement plan up front. Not doing so can imperil efforts to unlock the business value of big data. D What kinds of information need to be included in your big data imple- mentation? Discussions about big data often concentrate on data from social media sites such as Facebook, LinkedIn and Twitter, but as men- tioned above, there’s a lot more to it than that. To begin the process of planning a big data analytics deployment, project managers need to determine which of the various types of data that could be captured are wanted for analysis by business users. Answering that question will also help identify applicable big data BIG DATA QUESTION TIME
  • 4. TAKING ADVANTAGE OF BIG DATA ANALYTICS 4 HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? applications designed to handle specific data types. A critical factor that many orga- nizations ignore at this stage is inte- grating structured transaction data with unstructured forms of informa- tion as part of an overall data ware- housing and big data architecture. It’s terrific, for example, to use tex- tual data from social networks and other sources to analyze how well your marketing campaigns are being received by customers and pro- spective buyers. But even greater business value can be derived by correlating that information with analytical findings on how valu- able individual customers are—how much they’ve bought, what the prof- it margins were, whether they’re repeat buyers and how much it costs to retain them. Big data sys- tems can become big data silos if they’re designed solely for analyzing certain information for its own sake, without a broader focus. D How big does your big data sys- tem need to be? Once the required data types have been identified, the anticipated data volumes and update frequency—that is, veloc- ity—need to be factored into your planning. Those two characteristics are often coupled with data variety and referred to as the three V’s of big data. Although rapid updates and significant data volumes are commonly assumed, the real- ity is that the needs of companies vary widely based on size and the intensity of information usage. Accurately assessing your organi- zation’s requirements will help you determine the architecture and the technology investments needed to effectively capture, manage and analyze big data. 2SMALL STEPS BRING BIG REWARDS It’s tempting to believe that big data analytics success is within your grasp provided you buy the right technology and commit enough resources to the project. In real- ity, a big data deployment typically requires significant systems and data integration work; introduces new tools and analytics techniques; and calls for new skills on both the systems management and analytics sides. Trying to boil the ocean will result only in doing too much, too fast—a recipe for frustration and failure. For better results, an organization should plan to build its big data envi- ronment incrementally and iterative- ly. An incremental program is the most cost- and resource-effective SMALL STEPS BRING BIG REWARDS
  • 5. TAKING ADVANTAGE OF BIG DATA ANALYTICS 5 HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? approach; it also reduces risks com- pared with an all-at-once project, and it enables the organization to grow its skills and experience levels and then apply the new capabilities to the next part of the overall project. An architectural framework still needs to be established early on to help guide the plans for individual elements of a big data program. But because the initial big data efforts likely will be a learning experience, and because technology is rapidly advancing and business require- ments are all but sure to change, the architectural framework will need to be adaptive. 3 ARCHITECTING A SUCCESSFUL DEPLOYMENT Hadoop, MapReduce, NoSQL data- bases and other big data technolo- gies initially were developed by companies looking to store and analyze large amounts of unstruc- tured and semi-structured data that weren’t a good fit for mainstream relational databases—Google and Yahoo, for example. The open source technologies have been used successfully by those organi- zations and other early adopters, and they’re now widely available in commercial versions supported by big data software vendors. But a key issue to consider in designing a big data architecture is how much of your data analysis needs can be met by Hadoop and its cohorts on their own. As I wrote earlier, combining the unstructured data prevalent in big data systems with structured trans- action data provides the most com- plete view of a company’s business operations, enabling it to deploy analytics applications that can yield valuable insights to aid in improving business processes and increas- ing revenue. This data integration requirement drives the need to cre- ate an enterprisewide architecture that includes both types of data. In such cases, the architectural options include moving all of the relevant data to either a big data platform or a traditional enterprise data warehouse for analysis, or building a hybrid architecture that incorporates and ties together the two kinds of systems. Ultimately, because of the fun- damental differences between ARCHITECTING A SUCCESSFUL DEPLOYMENT An architectural framework needs to be established early on to help guide the plans for individual elements of a big data program.
  • 6. TAKING ADVANTAGE OF BIG DATA ANALYTICS 6 HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? structured and unstructured data, it doesn’t make sense to try to host both types of data on either of the different platforms. The best approach is a mixed architecture that could also include data marts and specialized analytical data- bases, such as columnar systems. Choosing the hybrid option creates a logical infrastructure that lever- ages existing IT investments in data warehouses and relational databas- es while enabling organizations to channel data processing and analyt- ics workloads to the most appropri- ate platforms. Preconfigured appliance systems are also emerging from a variety of vendors for use in big data analyt- ics applications. The appliances mix hardware and software components and offer the promise of lower costs and shorter implementation times compared with manually piecing together big data systems; they can also reduce deployment risks and minimize the level of new develop- ment and management skills that are needed in organizations. In addition, database and data integration vendors have added capabilities for exchanging data between big data systems, data warehouses and analytical databas- es, eliminating the need for exten- sive amounts of custom integration coding. For example, connector software for linking Hadoop ARCHITECTING A SUCCESSFUL DEPLOYMENT MIX IT UP a hybrid architecture for big data analytics can include the following components: n Hadoop and other big data tools for storing, managing and analyzing unstructured data; n A data warehouse and data marts for storing transaction data and the aggregated results of unstructured data analysis processes; n Standalone analytical databases for doing heavy-duty data analysis; n Data integration technologies—such as extract, transform and load tools, data virtualization software and Hadoop connectors—for tying together information on different platforms and delivering it to data analysts and business users; and n Business intelligence and analytics tools.
  • 7. TAKING ADVANTAGE OF BIG DATA ANALYTICS 7 HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? clusters and relational databases has become widely available. Because of the relative immatu- rity of big data technology, and the under-the-radar nature of many big data projects, implementations often have been treated as the Wild West of analytics application devel- opment and management, with no rules or corporate standards. But as the focus of big data projects shifts to producing tangible and sus- tainable business value, more dis- cipline is needed. Building a hybrid architecture to support big data analytics processes also makes it easier to apply internal policies and procedures on data management, governance, quality, security and privacy. 4 WHO’S ON THE TEAM? An often-overlooked aspect of suc- cessful big data analytics projects is the importance of getting the right people with the right skills in place, both to develop and man- age the systems and to use them. Assembling a project team is com- plicated by a shortage of technical and analytics professionals with big data experience. As a result, orga- nizations likely will need to train existing employees to handle roles they can’t fill through hiring. That’s another good reason to adopt a strategy of incrementally building a big data environment. The required IT resources include a mix of architects, developers and business analysts, the latter to help identify relevant data and develop project requirements. On the user side, data scientists and other ana- lytics professionals with skills in realms such as predictive and sta- tistical modeling as well as text ana- lytics are needed to do the heavy lifting on analyzing data. In addition to their analytics skills, those work- ers must have extensive business and industry knowledge, or work side by side with business users who can provide that know-how, in order to generate useful insights from big data analytics tools. In the past, predictive analytics, data mining and statistical analysis applications often were constrained by limited data volumes and an inability to include nontransactional data types. With the advance of big data technologies, analytics WHO’S ON THE TEAM? With the advance of big data technologies, analytics pros have been able to expand the breadth and depth of their work.
  • 8. TAKING ADVANTAGE OF BIG DATA ANALYTICS 8 HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? pros have been able to expand the breadth and depth of their work, increasing its potential business value. Data scientists don’t come cheap; if your organization doesn’t already have people who can ana- lyze big data in-house, hiring them can be a big budget item—assuming you’re able to find candidates in the first place. But the ROI they make possible can easily justify their salaries. There’s no doubt that big data technologies are currently at the peak of hyped expectations. And although there certainly is signifi- cant business value to be gained from them, there are also significant risks because of technology imma- turity, still-developing deployment and management methodologies, and the shortage of available expertise. In addition, big data systems run the risk of being the next data silo if they’re developed in isolation from existing BI, analytics and data warehouse systems. Don’t turn a blind eye to the challenges and let your big data analytics initiatives go down the wrong path. With big data now on the radar screens not only of IT managers but also of corporate and business executives, the suc- cess—or failure—of projects surely won’t go unnoticed. n WHO’S ON THE TEAM? BIG DATA ANALYTICS ROSTER The project team for a deployment of big data analytics tools should include these members: n Development manager n Data and systems architects n Big data developers (experienced with Hadoop, NoSQL and other big data tools) n Data integration developers n BI and analytics developers n Business analysts n Data scientists or analytics professionals
  • 9. TAKING ADVANTAGE OF BIG DATA ANALYTICS 9 HOME BIG DATA QUESTION TIME SMALL STEPS BRING BIG REWARDS ARCHITECTING A SUCCESSFUL DEPLOYMENT WHO’S ON THE TEAM? RICK SHERMAN is the founder of Athena IT Solu- tions, a consultancy in May- nard, Mass., that focuses on business intelligence, data integration and data ware- housing. He is also an adjunct faculty member at Northeastern University’s Graduate School of Engineering, and he blogs at The Data Doghouse. Email him at rsherman@ athena-solutions.com. Taking Advantage of Big Data Analytics is a SearchBusinessAnalytics.com e-publication. Jason Sparapani Managing Editor, E-Publications Craig Stedman Executive Editor Melanie Luna Managing Editor Linda Koury Director of Online Design Neva Maniscalco Graphic Designer Mike Bolduc Publisher mbolduc@techtarget.com Ed Laplante Director of Sales elaplante@techtarget.com TechTarget Inc. 275 Grove Street, Newton, MA 02466 www.techtarget.com © 2013 TechTarget Inc. No part of this publication may be transmitted or reproduced in any form or by any means without written permission from the publisher. TechTarget reprints are available through The YGS Group. About TechTarget: TechTarget publishes media for information technology profes­sionals. More than 100 focused websites enable quick access to a deep store of news, advice and analysis about the tech­nologies, products and processes crucial to your job. Our live and virtual events give you direct access to independent expert commentary and advice. At IT Knowledge Exchange, our social commu­nity, you can get advice and share solu­tions with peers and experts. ABOUT THE AUTHOR