Big Data analytics best practices
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Looking at in-memory applications for Big Data analytics.

Looking at in-memory applications for Big Data analytics.

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Big Data analytics best practices Document Transcript

  • 1. Big Data Analytics Best Practices
  • 2. Page 2 of 8 Sponsored by Big Data Analytics Best Practices Contents „Big Data‟ Analytics Projects Easier Said Than Done—but Doable Match In-Memory Processing Speed to Big Data Analytics Business Needs “Big data” has become one of the most talked- about trends -- and yes, buzzwords -- within the business intelligence (BI), analytics and data management markets. A growing number of organizations are looking to BI and analytics vendors to help them answer business questions in big data environments. Unfortunately, gaining visibility into pools of big data is easier said than done. And with vendors marketing a wide variety of technology offerings aimed at addressing the challenges of big data analytics projects, businesses may be hard-pressed to identify the one that best meets their needs. ‘Big Data’ Analytics Projects Easier Said Than Done—but Doable “Big data” has become one of the most talked-about trends -- and yes, buzzwords -- within the business intelligence (BI), analytics and data management markets. A growing number of organizations are looking to BI and analytics vendors to help them answer business questions in big data environments. Unfortunately, gaining visibility into pools of big data is easier said than done. And with vendors marketing a wide variety of technology offerings aimed at addressing the challenges of big data analytics projects, businesses may be hard-pressed to identify the one that best meets their needs. So, what is big data -- really? A recent story by the IT publication eWeek offered the following take on it, based partly on Gartner Inc.‟s definition of the term: “Big data refers to the volume, variety and velocity of structured and unstructured data pouring through networks into processors and storage devices, along with the conversion of such data into business advice for enterprises.” That hits the mark in terms of data management and the analytics part of the equation, but it misses the essential aspect of the business challenges surrounding big data: complexity. For instance, big data installations often involve information -- from social media networks, emails, sensors, Web
  • 3. Page 3 of 8 Sponsored by Big Data Analytics Best Practices Contents „Big Data‟ Analytics Projects Easier Said Than Done—but Doable Match In-Memory Processing Speed to Big Data Analytics Business Needs activity logs and other data sources -- that doesn‟t fit easily into traditional data warehouse systems. And in many cases, all of that disparate data needs to be pulled together in order to make sense of it on a broader level. That can have big implications for business rules, table joins and other components of big data analytics systems. The complexity of big data is what really makes it different from more conventional data when it comes to storage and query management, and it‟s the main reason why analytical database and data analytics software vendors have had to step up their game to help companies deal with big data. Understanding big data is the first step in assessing your technology needs and putting a big data analytics plan in place. The second is understanding the market and the current trends that are affecting organizations looking to derive business value, and competitive advantages, from increasingly large and diverse data sets. Big agendas for big data analytics projects Many businesses have always had large data sets, of course. But now, more and more companies are storing terabytes and terabytes of information, if not petabytes. In addition, they‟re looking to analyze key data multiple times daily or even in real time -- a change from traditional BI processes for examining historical data on a weekly or monthly basis. And they want to process more and more complex queries that involve a variety of different data sets. That might include transaction data from enterprise resource planning and customer relationship management systems, plus social media and geospatial data, internal documents and other forms of information. Increasingly, companies also want to give business users self-service BI capabilities and make it easier for them to understand analytical findings. All of that can play into a big data analytics strategy, and technology vendors are addressing those needs in different ways. Many database and data warehouse vendors are focusing on the ability to process large amounts of complex data in a timely fashion. Some are using columnar data stores in an effort to enable quicker query performance, or providing built-in query
  • 4. Page 4 of 8 Sponsored by Big Data Analytics Best Practices Contents „Big Data‟ Analytics Projects Easier Said Than Done—but Doable Match In-Memory Processing Speed to Big Data Analytics Business Needs optimizers, or adding support for open source technologies such as Hadoop and MapReduce. In-memory analytics tools may help speed up the analysis process by reducing the need to transfer data from disk drives, while data virtualization software and other real-time data integration technologies can be used to assemble information from disparate data sources on the fly. Ready-made analytics applications are being tailored to vertical markets that routinely have to deal with big data -- for instance, the telecommunications, financial services and online gaming industries. Data visualization tools can simplify the process of presenting the results of big data analytics queries to corporate executives and business managers. Organizations that fit into the categories described above in relation to their data and analytics needs should begin by considering the following issues and questions, among others, before creating an implementation plan and finalizing their big data infrastructure choices: • The required timeliness of data, as not all databases support real-time data availability. • The interrelatedness of data and the complexity of the business rules that will be needed to link various data sources to get a broad view of corporate performance, sales opportunities, customer behavior, risk factors and other business metrics. • The amount of historical data that needs to be included for analysis purposes. If one data source contains only two years of information but five are required, how will that be handled? • Which technology vendors have experience with big data analytics in your industry, and what is their track record? • Who is responsible for the various data entities within an organization, and how will those people be involved in the big data analytics initiative?
  • 5. Page 5 of 8 Sponsored by Big Data Analytics Best Practices Contents „Big Data‟ Analytics Projects Easier Said Than Done—but Doable Match In-Memory Processing Speed to Big Data Analytics Business Needs Those considerations don‟t constitute in-depth requirements planning, but they can help businesses get started on the road to deploying a big data analytics system and identifying the technology that will best support it. Match In-Memory Processing Speed to Big Data Analytics Business Needs Michael Minelli is vice president of sales and global alliances for the information services division of MasterCard Advisors, the professional services and data analytics arm of MasterCard International. He's also one of the three co-authors of Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses, a book that aims to explain the big data phenomenon to both IT and business readers. Minelli, who worked at software vendors Revolution Analytics and SAS Institute Inc. before joining MasterCard, has been involved in big data projects at organizations such as Time Inc., Cablevision, Foxwoods, Major League Baseball, Standard & Poor's and Sony. In an interview with SearchBusinessAnalytics.com, Minelli discussed big data analytics applications and the role that in-memory analytics technology can play in them. One of his pieces of advices: The faster performance supported by in- memory processing won't provide the hoped-for business benefits unless the analytical results are fed into real decision-making processes. Excerpts from the interview follow. What's the key message in your book? Michael Minelli: The book's main theme is that big data analytics are a game changer for the industry, whether you're in IT or the business. Big data is going to make a big impact, and this is going to continue over time. The message is to think about how to do things differently: "If we can do things faster, then what does that mean for the business?" Big data allows us to innovate and make decisions quickly while transforming the way we do business intelligence. People have been talking about building one version of the truth for a while -- that's the whole genesis of the data warehouse movement. The name of the game was who could build the most valuable
  • 6. Page 6 of 8 Sponsored by Big Data Analytics Best Practices Contents „Big Data‟ Analytics Projects Easier Said Than Done—but Doable Match In-Memory Processing Speed to Big Data Analytics Business Needs repository to make better decisions. What's changed is that it's not all about what happens in your world, but what happens in other companies and even in other industries. It's about going from having an insular mindset around data to having an abundance mentality for leveraging data. Can you give a couple of real-world examples of the opportunities for taking advantage of big data analytics? Minelli: Online up-selling and cross-selling on the fly before a customer's attention fades. Mining blogs and customer service notes to perform customer sentiment analysis, good and bad. Providing secure e-commerce transactions with built-in anti-fraud controls. Deploying marketing automation that delivers real ROI by enabling actionable insights into customer buying habits. That's how competitive organizations are using big data analytics today. What specific role can in-memory analytics technology play in turning big data into a competitive advantage? Minelli: It's all about pure speed -- taking advantage of the hardware and RAM capabilities that have become cheaper to create queries on the fly. Doing so removes some of the barriers between IT and the business so that there's more agility for people to do on-the-fly business intelligence and predictive analytics and to move beyond traditional sampling techniques if they don't have to wait 24 to 48 hours for results. When is in-memory processing for big data analytics not the right fit or more trouble than it's worth? Minelli: It's the notion of fit to purpose. The main thrust is, do you really need the additional speed and do you have the types of users and processes in place so that if empowered with this information, they could really do something with it to impact the business? Everyone talks about faster access to data leading to better insights, but the other critical part is connecting that insight to an actual operation. So it's not just viewing a report, but actually using that report to make a decision on the fly to trigger an event like addressing a fraudulent transaction or initiating a cross-sell opportunity. Having faster analytics is great, but [the results] have to be able to make their way into the decision process.
  • 7. Page 7 of 8 Sponsored by Big Data Analytics Best Practices Contents „Big Data‟ Analytics Projects Easier Said Than Done—but Doable Match In-Memory Processing Speed to Big Data Analytics Business Needs So how can organizations get to the level where they truly can take advantage of in-memory analytics in combination with big data? Minelli: For starters, IT should work with the analysts to assess the low- hanging fruit where there are some productivity gains [to be had]. A good example is reducing a data mining process from 24 hours to a matter of minutes so that a data scientist can be more responsive to the rapid changes in today's businesses. The next step would be for the business to develop some use cases where speed can make a difference and then give it a try. The technology part isn't a no-brainer, but it's not quantum physics, either. From my perspective, the major challenge is finding and hiring the right talent and then managing that talent to achieve specific business objectives in a reasonable time frame. Beth Stackpole is a freelance writer who has been covering the intersection of technology and business for more than 25 years.
  • 8. Page 8 of 8 Sponsored by Big Data Analytics Best Practices Contents „Big Data‟ Analytics Projects Easier Said Than Done—but Doable Match In-Memory Processing Speed to Big Data Analytics Business Needs Free resources for technology professionals TechTarget publishes targeted technology media that address your need for information and resources for researching products, developing strategy and making cost-effective purchase decisions. Our network of technology-specific Web sites gives you access to industry experts, independent content and analysis and the Web‟s largest library of vendor-provided white papers, webcasts, podcasts, videos, virtual trade shows, research reports and more —drawing on the rich R&D resources of technology providers to address market trends, challenges and solutions. Our live events and virtual seminars give you access to vendor neutral, expert commentary and advice on the issues and challenges you face daily. Our social community IT Knowledge Exchange allows you to share real world information in real time with peers and experts. What makes TechTarget unique? TechTarget is squarely focused on the enterprise IT space. Our team of editors and network of industry experts provide the richest, most relevant content to IT professionals and management. We leverage the immediacy of the Web, the networking and face-to-face opportunities of events and virtual events, and the ability to interact with peers—all to create compelling and actionable information for enterprise IT professionals across all industries and markets. Related TechTarget Websites