Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio

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1 White Paper Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio By Nik Rouda, Senior Analyst October 2015 This ESG White Paper was commissioned by Dell and Intel and is distributed under license from ESG.

2 White Paper: Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio 2 Contents How Should Organizations Really Approach Big Data and Analytics?... 3 Big Data Isn t a Solo Sport... 3 How to Build a Fully Operational Big Data Technology Stack... 4 The Better Way: Dell Big Data and Analytics Solutions... 5 The Dell Ecosystem... 7 Greater Operational Efficiency... 7 The Bigger Truth... 8 All trademark names are property of their respective companies. Information contained in this publication has been obtained by sources The Enterprise Strategy Group (ESG) considers to be reliable but is not warranted by ESG. This publication may contain opinions of ESG, which are subject to change from time to time. This publication is copyrighted by The Enterprise Strategy Group, Inc. Any reproduction or redistribution of this publication, in whole or in part, whether in hard-copy format, electronically, or otherwise to persons not authorized to receive it, without the express consent of The Enterprise Strategy Group, Inc., is in violation of U.S. copyright law and will be subject to an action for civil damages and, if applicable, criminal prosecution. Should you have any questions, please contact ESG Client Relations at Intel Inside. Powerful Solution Outside. Dell, the Dell logo, Dell ProSupport, PowerConnect, EqualLogic and PowerEdge are trademarks of Dell Inc. Intel, the Intel logo, Xeon, and Xeon Inside are trademarks or registered trademarks of Intel Corporation in the U.S. and/or other countries.

3 White Paper: Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio 3 How Should Organizations Really Approach Big Data and Analytics? Many big data projects don t add any value, at least not yet. Some organizations are looking to transform their business capabilities, while others have goals around making big improvements to operational efficiency, or are pursuing a range of competitive advantages across both areas. While the industry is extremely enthusiastic about the potential to apply analytics, the problem is that many initiatives are poorly defined, lack a clear objective, and often miss the bigger picture of the environment in which they will operate. To be successful (i.e., not waste time and money on a fruitless project) in serving the needs of business units, government departments, and research institutions, owners of big data and analytics initiatives should recognize the logical and physical interdependencies of each part of the solution. The clear dynamic flow for every instance can be summed up as: 1. Decision makers want answers to pressing questions about their operations and environment. This statement may seem obvious, but it s the right starting point. Consider how any big data project will actually answer specific questions, the value of knowing the answers, what subsequent questions are likely, and critically, how the organization would utilize all those answers to achieve better results. Skip this step at your own peril of being ineffective or irrelevant. 2. Answers come from analytics, so set about carefully defining appropriate analytical models and figuring out how to support ad hoc queries, too. Sometimes the approach will be simple and intuitive; sometimes it will take significant work to arrive at the best algorithm where best isn t just accuracy, but also feasibility. It s no good achieving perfect certainty if it takes forever to do so. Also, some questions will be on demand by non-technical staff, so have a facility to find information as needed through a friendly user interface. 3. Analytics require integrating and preparing data from an increasingly wide variety of sources, and doing so in a timely fashion. People recognize that in general, more information is better, and they are seeking a big-picture view of related factors. It can be challenging to aggregate and reconcile data from previously independent sources, so it is critical to plan out a strategy for swiftly combining disparate well-structured and loosely organized data, and a method to organize this into a workable mix. It doesn t have to be one repository, but it does have to work together. 4. Data needs to be on managed platforms that are cost efficient, scalable, reliable, secure, and fast! In the past, this usually meant a fairly rigid relational database management system (RDBMS), but today there is much more variety in data platforms, including NoSQL databases, Hadoop, and Spark. The qualities of the data platform will affect the capabilities of the analytic exercises running on the data. Since there are likely multiple sources, you need either a universal source of truth or to prepare to integrate platforms, too. 5. Data platforms ultimately run on IT infrastructure, and the qualities of that infrastructure also affect capabilities. Storage holds the data, servers process it, and networks transport it, and there is no getting away from the physical realities of each component. As much as distributed scale out approaches to analytics and data platforms may try to commoditize the hardware layer, there is no escaping the reality that the performance of the whole will be dependent on the sum of the (underlying) parts. And yes, cloud infrastructure is still physical infrastructure somewhere! 6. This is all in a complementary ecosystem of strategic partners, hardware and software vendors, and professional services, which each bring a piece of the solution. Having expert assistance to figure out what products you need and how to fit it all together can be invaluable, particularly in a market as broad and immature as the big data space is today. Big Data Isn t a Solo Sport Many big data and analytics initiatives are started by IT executives eager to shed the derogatory cost center label and instead make a bigger impact on their businesses by providing and supporting new technologies in this area. Still, 46% of ESG research respondents report that these new initiatives originate from senior execs in groups

4 White Paper: Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio 4 outside of IT, reinforcing that the interest is in the outcomes, not just in the having the latest tools (or toys?). 1 While it is clearly beneficial for the lines of business to define the specific use cases, it is often challenging for IT to find a workable solution to fit the requirements. Independent of origin, the project s design and long-term support inevitably become the responsibility of the IT department. A significant factor is that while a lot of IT technologies are narrowly focused (i.e., standalone solutions), this isn t the case with big data and analytics, which touches on all areas of IT (see Figure 1). ESG research shows that a majority of respondents consider no fewer than seven different IT disciplines important or even crucial to new initiatives or projects in this area. Clearly, successful initiatives will be based on taking a holistic, thoughtful approach to assembling all the requisite components of the technology stack, both hardware and software. Picking your data sources, plugging them into an analytics engine, and building the models is only part of the story. Figure 1. Importance of the Involvement of IT Groups for New Business Intelligence, Analytics, and Big Data How important is the involvement of the following IT disciplines for new initiatives and projects in the area of business intelligence, analytics, and big data to be successful? (Percent of respondents, N=375) Crucial Nice-to-have, but not required Important Completely unnecessary Don t know / no opinion Database/BI/analytics team 50% 35% 12% 3% Security/risk/governanc e team 47% 37% 12% 4% 1% Applications team 42% 43% 11% 3% 1% Infrastructure/cloud architects 41% 41% 14% 4% Server/virtualization team 36% 45% 16% 2% 1% Storage team 35% 46% 15% 3% Networking team 35% 44% 18% 3% Source: Enterprise Strategy Group, How to Build a Fully Operational Big Data Technology Stack It is readily apparent that a lot of vendors want to own a piece of the hot big data and analytics market. Each one has some of the shiny puzzle pieces to offer, and they will go out of their way to impress you on the merits of that product or service. Occasionally, vendors marketing departments get a little bold in their claims. The challenge for most IT organizations is completing the puzzle when the pieces come in different sizes, boxes, and (brand) colors, and the descriptions are either sweepingly grandiose or focused on minutiae. Choosing the right set of pieces is a difficult job for many IT technical decision makers. One strategy is to pick the best of breed for each component of the stack. Certainly, this can have good results and allow for a very tightly tailored solution. The downside here is that if you want to evaluate three to five vendors 1 Source: ESG Research Report, Enterprise Data Analytics Trends, May All ESG research references and charts in this white paper have been taken from this research report, unless otherwise noted.

5 White Paper: Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio 5 for each of a dozen layers including storage, servers, networking, security, data ingestion, data preparation, data warehouse, data lake or hub, database, analytics engine, visualization tool, query tool, etc. then you are going to need a pretty big whiteboard to keep track. Each of these items will need to be carefully evaluated against a prioritized list of criteria (see Figure 2 for how this list looks according to ESG research). Figure 2. Most Important Attributes When Considering a Big Data or Analytics Solution(s) Which of the following attributes are most important to your organization when considering technology solutions in the area of business intelligence, analytics, and big data? (Percent of respondents, N=375, three responses accepted) Cost, ROI and/or TCO Security 26% 26% Reliability Performance Ease of integration with other applications, APIs Built-in high availability, backup, disaster recovery capabilities Flexibility Efficient use of storage resources Scalability Ease of administration Efficient use of server resources Reporting and/or visualization Open standards-based Public cloud hosting options 22% 21% 20% 18% 18% 16% 15% 14% 13% 13% 11% 10% Source: Enterprise Strategy Group, After picking the winners, you will need to make sure the pieces are all compatible; execute contracts with each vendor; receive the hardware; install the software; connect the data sources; test it in a lab; deploy into production; retest the applications in a production environment; train staff; and then support it all through future versions, upgrades, and shifting end-user requirements. Clearly, this all could certainly keep a large IT department busy for years, even before any meaningful business value is realized by the sponsors of the big data initiative. This is certainly one approach that could (eventually) lead to a very sophisticated big data solution, assuming you have a large IT department with nothing else to do. There. Has. Got. To. Be. A. Better. Way. The Better Way: Dell Big Data and Analytics Solutions A different strategy is gaining many adherents in the general big data and analytics marketplace, and that is to look for the few trusted vendors who can offer that much more of the puzzle directly. Finishing off a jigsaw that s

6 White Paper: Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio 6 already 80-90% done is generally much faster and easier, and leads to the same lovely picture at the end. Or put another way, being business user outcome-oriented versus product-oriented is always the right way to approach the problem. Dell fits in this category. You can certainly buy any one of a number of requisite technologies from Dell, but the real advantage is leveraging its portfolio, which boasts a greater breadth compared to a market mostly filled with niche point players. Whether you want to adopt just one product to fill a gap, find a suite of complementary technologies, or jumpstart with a fully built appliance, Dell offers solutions that enhance one another s capabilities, including: Data Analytics - With analytics becoming the centerpiece of many IT roadmaps, most companies are reexamining their tools in this area, but still want to leverage skills via common scripting and programming languages. Dell big data analytics solutions are robust, user-friendly, and can be utilized with structured, semi-structured, or unstructured data. Statistica is a comprehensive platform for analytics on any data, from any source, structured or unstructured. Statistica offers a field-proven toolset to deliver data-driven insights. Data preparation and extract-transform-load (ETL) capabilities easily set up data for mining, machine learning, and a wide range of advanced analytics functions and models. Expanded visualization and reporting features make it easier for users to understand and interpret their findings. Data Integration - Unlocking the value of your data requires a real-time, single, and accurate view of company data across multiple domains and sources. Dell data integration solutions help you eliminate fragmented analysis and data governance issues as well as free up your IT staff to do more with less. Boomi a simple integration-platform-as-a-service (IPaaS) offering that enables multidirectional data migration and integration to and from the cloud. As both external customer and internal user applications generate data for analysis, Boomi AtomSphere combines these data sets in a controlled manner, making it easier for developers to build applications to utilize all available data. Data integration is complemented by master data management (MDM) functionality and APIs to ensure a consistent and richer basis for subsequent analytics, independent of where the data is born. Data Management - Effective data management is critical to optimal online transaction processing and big data analytics success. To meet this challenge, Dell solutions support traditional relational databases and structured, semi-structured, and unstructured data types both on-premises and in the cloud. TOAD a suite of tools that automate development and database management tasks to increase productivity, optimize performance by auto-tuning code, and reduce costs. TOAD is popular with Oracle, SQL Server, and IBM environments for application development and testing scalability under load. Problems are quickly identified and resolved with recommended SQL changes, reducing the risks associated with releasing new versions and updates of code. TOAD Data Point and TOAD Intelligence Central can also help with the necessary data collection, joins, integration, preparation, and query development. Foglight addresses database and platform management issues by providing insights to IT operations around virtualization and resource utilization. By managing both cloud and endpoints, performance and availability issues are handled, improving delivery against service level agreements (SLAs) and helping to meet end-user expectations for mission-critical database and analytics environments. Data Infrastructure a huge range of servers, storage, and ready-to-go appliances is available for use in big data and analytics environments. At its core, big data is reliant on computational horsepower. That s why Dell PowerEdge servers are built on powerful Intel Xeon processors, maximizing data processing performance for intensive analytic operations as well as data transformation and integration. It should also be noted that Intel has made a large investment in Cloudera, creating a three-way partnership specifically around Apache Hadoop, culminating in tightly designed reference architectures and preconfigured, pre-

7 White Paper: Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio 7 tuned appliances. For real-time analytics, an in-memory appliance supports Cloudera Enterprise and Spark streaming. And while many distributed big data platforms leverage servers internal DAS storage, some customers are also using Dell NAS or SAN approaches for shared, central data stores, often combined with Dell networking solutions. Reference architectures offer tested and certified blueprints for small, medium, and large data analytics deployments. These technical resources guide deployment of solutions that incorporate technologies from Dell and strategic partners and industry leaders. Reference architectures cover all the hardware and software needed to contain costs and increase the ease of use and scalability of solutions for a complete big data environment. Engineered solutions bring together the components of complete data analytics environments in turnkey appliances that are easy to order and quick to deploy. Expertise proven solutions and skilled professional services teams are ready to help reduce the risks of delay, extra costs, and disappointment. Whether from Dell s own services organization or Dell s wide range of channel partners, including resellers, consultants, service providers, and systems integrators, there are plenty of people who have gained experience and defined best practices to achieve success faster. The Dell Ecosystem Dell recognizes that it is part of a larger ecosystem, and is engaging in meaningful partnerships with leading industry vendors, systems integrators, and service providers. This enables stronger engineering integrations for stronger solutions, and more robust service offerings to provide customers with the expertise they need to be successful. As noted, Dell Professional Services and partner service offerings also can provide advice or take complete ownership of delivery for a turnkey, hosted, or managed solution to fit operational requirements. Technology alliance partnerships such as those with Cloudera and Intel also drive added value. For example, Intel Xeon processors are tuned for the particular requirements of Cloudera Hadoop and Dell hardware. Other notable partnerships include Microsoft, SAP, and Oracle, which drive enhanced value through joint product development, reference architectures, and bundled, purpose-built appliances that can optimize performance in-memory, perform ETL offload of data warehouses, and other common use cases. Greater Operational Efficiency Following a better together approach to construct a complete big data and analytics technology stack leads to significant gains. Some seek not only transformative business insights, but also great gains in operational efficiency for the environment. Existing data warehouses and infrastructure can be made to run better and faster with Dell solutions complementing them and augmenting where their functionality leaves off. A perfect example of this would be Dell s data warehouse optimization solution using ETL offload. Data warehouses are a great area to explore for optimization by offloading less-structured analytics and ETL outside of the very expensive traditional systems. This gives more flexibility and adaptability, and should generally speed up reporting to keep the data warehouses using their resources as originally conceived and designed, not stretching them to handle newer big data-style workloads. The costs of licensing, hardware, and support for traditional data warehouse systems like those from Oracle justify exploring technologies that can improve performance while controlling unnecessary expansion. If you are like the many who are taking a green-field approach to big data and analytics in light of new possibilities, Dell can help show you how these technologies apply to your business goals and would tie into your existing environment. If you re unsure where to get started, as previously noted, Dell has a number of reference architectures and engineered solutions for Cloudera Hadoop, SAP HANA, Microsoft SQL Server, and other common environments. These can be used off the shelf or adapted to suit specific business and IT requirements, delivering a faster time to value over building an architecture from scratch.

8 The Bigger Truth White Paper: Gaining Operational Efficiency with Dell s Big Data and Analytics Portfolio 8 There is no one right way to build a big data or analytics environment, especially as the range of technologies in the market has exploded in the last decade and continues to rapidly evolve. Every organization will have unique goals, concerns, existing infrastructure and applications, and (limited) resources to leverage. Finding the right approach is as much a matter of philosophy since the collected wisdom hasn t fully solidified into a universal codex for success. Some may choose to build their own stack from basic units, taking the time and cost to learn and assemble all of the software and hardware involved. Others may wish to shortcut this process in the interest of time to value from having something in place and adapting as they go. The most important recommendation is to focus on the opportunity that a new big data initiative can deliver to the rest of the company, research, or governmental group. These solutions are all about better operational efficiency and gaining actionable insights. You can incrementally improve or even drastically transform your activities with big data and analytics, finding new possibilities where you didn t have the capabilities, staff, funds, or IT resources before. Each step of the process will take some thought, but finding the right partner for your journey will reduce risk and advance your cause. Dell might well be that partner, helping you to capture and unify all your data, analyze it in a meaningful and timely fashion, and even optimize these analytics operations going forward.

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