Better Total Value of Ownership (TVO) for Complex Analytic Workflows with the IBM & Hortonworks Analytic Solutions

Editor's Notes

• #2: The hybrid Enterprise Data Warehouse of the Future – Do More with your Data     The Enterprise Data Warehouse (EDW) has traditionally been the foundation for data storage. So how do you leverage current investments while remaining relevant and competitive? It is important for your organization to continue to evolve, accelerate development/deployment times, provide high performance and to provide a cloud-ready platform.     Thank you for joining us today. In this webinar we will talk about current data challenges, and how Data Science and Machine Learning is driving the need for advanced analytic decisioning. We will also discuss the need for a Hybrid data strategy, and how the Enterprise Data Warehouse of the future remains an integral part of that strategy. Finally we will talk about IBM’s future vision and how current and future innovative, scalable, and flexible technology is harnessing growing data for even your most advanced workloads,\. While providing your data scientists with a platform for advanced analytics.   
• #4: Presenter: Hortonworks & IBM Together, we are better able to address the changing dynamics we’ve just outlined, solve the associated challenges and create valuable outcomes. By joining forces, we have brought together Hortonworks deep expertise in data with IBM's data science platform which was a leader in the Magic quadrant and the best SQL Engine broadens our toolset to be able to help you accelerate their business. So why did we hook up? Suggestion: IBM spokesperson outlines Hortonworks experience, and Hortonworks spokesperson outlines IBM’s thereby outlining the key strengths and underlining our partnership.
• #5: Let’s talk briefly about the strong capabilities that are behind this value. First is the base Hadoop platform. Unlike other vendors who focus primarily just on the Hadoop container, the combined platform allows us to sell the #1 Open Hadoop distribution HDP, along with the top open source solution for managing data in motion, HDF and then expand on that foundation with key IBM value add technologies. For example in Hybrid Data Management we have Big SQL to deliver the Common Analytic Engines to Hadoop where you can leverage your existing SQL and analytics tools to take advantage of evolving ecosystem. We also have Big Replicate to deliver unmatched capability for Active/Active disaster recovery, upgrade and migration to ensure we synchronize data across clusters. Across analytics, we bring the ability to expand the data governance and movement capabilities in HDF with the extensive capability and market place presence of the Unified Governance Portfolio with Big Integrate and Big Quality. Likewise we bring the #1 Data Science platform in the market with the capabilities of DSX and SPSS for visualization and creating/running models to help move clients up the AI ladder. Also important to note is that the relationship is driving innovation in our own products as well and goes far beyond just analytics. There are key offerings from the Power and Storage teams that clients can also use to achieve an optimized platform. The relationship is strong and expanding. In fact Hortonworks just named IBM as their partner of the year for 2017 in recognition of how much we’ve been able to accomplish together in such a short time. This value is key to our strategy and how we succeed. We aren’t here to convert existing Hortonworks clients to IBM paper, but we can add lots of new value to their client base and drive new clusters and additional growth in those accounts. We also are not out to be the lowest cost commodity Hadoop provider. We’ll lose if all you do is sell the cheapest repository. Let Cloudera and Mapr win the race to the bottom while we win clients through value.
• #6: Together, we are better able to address the changing dynamics we’ve just outlined, solve the associated challenges and create valuable outcomes. By joining forces, we have brought together Hortonworks deep expertise in data with IBM's data science platform which was a leader in the Magic quadrant and the best SQL Engine broadens our toolset to be able to help you accelerate their business. So why did we hook up? The ITIC survey result: "In its ‘mainstream’server category the survey found that IBM Power Systems achieved the highest levels of reliability and uptime when compared with 14 server hardware options and 11 server hardware virtualization platforms. " Press Release with more details: http://www-03.ibm.com/press/us/en/pressrelease/52693.wss
• #7: Presenter: Hortonworks & IBM Together, we are better able to address the changing dynamics we’ve just outlined, solve the associated challenges and create valuable outcomes. By joining forces, we have brought together Hortonworks deep expertise in data with IBM's data science platform which was a leader in the Magic quadrant and the best SQL Engine broadens our toolset to be able to help you accelerate their business. So why did we hook up? Suggestion: IBM spokesperson outlines Hortonworks experience, and Hortonworks spokesperson outlines IBM’s thereby outlining the key strengths and underlining our partnership.
• #15: The TVO framework categorizes the interrelated cost/value drivers for CAE solutions long the horizontal axis, the drivers are arranged based on whether they are primarily Technology or Business drivers. Along the vertical axis, drivers are arranged based on ease of measurability: Direct or Derived. The cost/value drivers for CAE are depicted as a circle whose size is proportional to the potential impact on a manufacturing client’s Total Value (Benefits – Cost) of Ownership or TVO as follows: Total Costs of Ownership (TCO): Typical costs include: one-time acquisition costs for the hardware and deployment, and annual costs for software, maintenance and operations. Improved Productivity/Quality: The TVO model quantifies the value of productivity gains of administrators, engineers and the organization. It also estimates improvements in product quality. Revenue and Growth: Faster time to value with a better optimized and more comprehensive CAE ecosystem. Greater innovation capabilities for manufacturers spur growth, revenues and improve profits. Risk Mitigation: A streamlined process, lower system downtime and better business and IT understanding/collaboration with suppliers minimize cumbersome iterations in rework and delays. Improved security, process consistency and data quality mitigate risks of failed/poor analysis outcomes.   The TVO for both HPE and white box systems typically grow by CAE solution size, with key differentiated features in the HPE solution driving added value over white box alternatives.
• #18: Results for a Small CAE Configuration: The costs and benefits mapped by each quadrant and value driver. HPE’s (Apollo 2000) slightly larger acquisition and maintenance costs are more than offset by higher client benefits in enhanced productivity and quality, higher revenues/profits and lower risks. Costs: Compared with white box clusters, HPE Apollo systems lower data center facilities, power and cooling costs, and require fewer systems administrators to manage CAE environments; reducing operations costs (annual). Often pre-configured in the factory, HPE systems have easy to use integration templates for commonly deployed CAE applications. This helps lower deployment times (one-time cost) and improve productivity. Productivity/Quality: With fewer downtimes and better streamlined/performance-optimized CAE solutions, HPE systems enhance the productivity of engineers. Systems administrator productivity for HPE systems is also enhanced with turnkey HPC cluster solutions and software for comprehensive ongoing node to rack to job management. Organizational productivity is further enhanced with other HPE value-added services tailored for the Manufacturing industry. Manufacturers can improve the quality of products and the CAE process by easily integrating with other HPE Manufacturing solutions: Edge, IoT, Machine Learning, etc., and detecting problems faster to reduce rework and costs. Greater Quality benefits accrue with larger configurations that enable more realistic Multiphysics CAE models. Revenues and Profits: HPE solutions also deliver faster time to value through a better and more comprehensive ecosystem of CAE partners whose applications are optimized for predictable performance, scale, ease of deployment and integration. Value added HPC software from HPE and partners enable manufacturers to optimize complex CAE workflows and automate cluster installation which becomes very cumbersome for white box clusters as configuration size grows. Risks: Manufacturers must mitigate risks which typically grow with configuration size. Better reliability, availability and serviceability (RAS) and security features in enterprise-grade HPE systems for CAE help manufacturers contain downtime and security expenses. HPE solutions also promote better collaboration with suppliers to further lower risks and drive revenues/profits.
• #20: Results for a Small CAE Configuration: The costs and benefits mapped by each quadrant and value driver. HPE’s (Apollo 2000) slightly larger acquisition and maintenance costs are more than offset by higher client benefits in enhanced productivity and quality, higher revenues/profits and lower risks. Costs: Compared with white box clusters, HPE Apollo systems lower data center facilities, power and cooling costs, and require fewer systems administrators to manage CAE environments; reducing operations costs (annual). Often pre-configured in the factory, HPE systems have easy to use integration templates for commonly deployed CAE applications. This helps lower deployment times (one-time cost) and improve productivity. Productivity/Quality: With fewer downtimes and better streamlined/performance-optimized CAE solutions, HPE systems enhance the productivity of engineers. Systems administrator productivity for HPE systems is also enhanced with turnkey HPC cluster solutions and software for comprehensive ongoing node to rack to job management. Organizational productivity is further enhanced with other HPE value-added services tailored for the Manufacturing industry. Manufacturers can improve the quality of products and the CAE process by easily integrating with other HPE Manufacturing solutions: Edge, IoT, Machine Learning, etc., and detecting problems faster to reduce rework and costs. Greater Quality benefits accrue with larger configurations that enable more realistic Multiphysics CAE models. Revenues and Profits: HPE solutions also deliver faster time to value through a better and more comprehensive ecosystem of CAE partners whose applications are optimized for predictable performance, scale, ease of deployment and integration. Value added HPC software from HPE and partners enable manufacturers to optimize complex CAE workflows and automate cluster installation which becomes very cumbersome for white box clusters as configuration size grows. Risks: Manufacturers must mitigate risks which typically grow with configuration size. Better reliability, availability and serviceability (RAS) and security features in enterprise-grade HPE systems for CAE help manufacturers contain downtime and security expenses. HPE solutions also promote better collaboration with suppliers to further lower risks and drive revenues/profits.
• #22: Results for a Small CAE Configuration: The costs and benefits mapped by each quadrant and value driver. HPE’s (Apollo 2000) slightly larger acquisition and maintenance costs are more than offset by higher client benefits in enhanced productivity and quality, higher revenues/profits and lower risks. Costs: Compared with white box clusters, HPE Apollo systems lower data center facilities, power and cooling costs, and require fewer systems administrators to manage CAE environments; reducing operations costs (annual). Often pre-configured in the factory, HPE systems have easy to use integration templates for commonly deployed CAE applications. This helps lower deployment times (one-time cost) and improve productivity. Productivity/Quality: With fewer downtimes and better streamlined/performance-optimized CAE solutions, HPE systems enhance the productivity of engineers. Systems administrator productivity for HPE systems is also enhanced with turnkey HPC cluster solutions and software for comprehensive ongoing node to rack to job management. Organizational productivity is further enhanced with other HPE value-added services tailored for the Manufacturing industry. Manufacturers can improve the quality of products and the CAE process by easily integrating with other HPE Manufacturing solutions: Edge, IoT, Machine Learning, etc., and detecting problems faster to reduce rework and costs. Greater Quality benefits accrue with larger configurations that enable more realistic Multiphysics CAE models. Revenues and Profits: HPE solutions also deliver faster time to value through a better and more comprehensive ecosystem of CAE partners whose applications are optimized for predictable performance, scale, ease of deployment and integration. Value added HPC software from HPE and partners enable manufacturers to optimize complex CAE workflows and automate cluster installation which becomes very cumbersome for white box clusters as configuration size grows. Risks: Manufacturers must mitigate risks which typically grow with configuration size. Better reliability, availability and serviceability (RAS) and security features in enterprise-grade HPE systems for CAE help manufacturers contain downtime and security expenses. HPE solutions also promote better collaboration with suppliers to further lower risks and drive revenues/profits.
• #23: Key financial metrics for a 3-year time horizon for all three configurations for HPE and White Box clusters include: Return on Investment (ROI) and Payback Period (PP). For all cases, the ROI and Payback Period improve as configuration sizes grow from small to medium to large. This is expected. As HPC systems scale, the marginal value increases because of the very large business benefits for manufacturers driven by innovation and the ability to solve intractable problems; both critical for new product development.   The percent improvements in ROI and Payback Period for HPE systems over white box clusters also grow with configuration size. This means as manufacturers grow their CAE footprint to drive more innovation, HPE systems will deliver better marginal value compared to white box clusters. HPE systems are also simpler to manage and operate. The 3-year TVO analysis presented here quantifies all these cost/value drivers holistically for three configurations: small, medium and large. The ROI for an HPE solution ranges from 100% (small) to 386% (large) while an equivalent white box cluster delivers an ROI of 56% (small) to 192% (large). This implies that HPE systems deliver a considerable ROI improvement of 78% (small) to 101% (large) over corresponding white box clusters.   Likewise, the Payback Period (PP) in months for an HPE solution ranges from 5.01 (large) to 7.72 (small) while the corresponding white box cluster delivers a PP in months of 6.18 (large) to 8.82 (small). This implies that HPE systems also deliver better Payback improvements from 19% (large) to 12% (small) over corresponding white box clusters.