Buying vs. Building Business Analytics. A decision resource for technology and product teams

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1 Buying vs. Building Business Analytics A decision resource for technology and product teams

2 Introduction Providing analytics functionality to your end users can create a number of benefits. Actionable reporting, analysis, dashboard, and visualization tools can drive higher user satisfaction, competitive advantages for your business, and accelerated business growth. However, getting to these results requires a well-thought-out approach and proper consideration for resource investments, project timing, risk management, and end user needs. As a part of this process, almost every development organization is faced with a fundamental question whether to build the required analytics functionality in-house or to buy that functionality from a business intelligence software vendor. Build vs. Buy is a classic software dilemma, and certain companies are often tempted to build BI in house, given the presence of a large existing engineering team or a belief that only internal development can meet a project s highly specific requirements. At times, building analytics features may make sense specifically when most or all of the following are true: User requirements are simple, as in a static reporting use case There is a small user base that isn t expected to grow meaningfully over time User needs aren t anticipated to change significantly over time The development team already has technical expertise in BI and data integration The problem is that these conditions rarely hold. Users often want high performance interactive analytics right off the bat, and they are always quick to make unanticipated requests for new types of analytics on different data. Moreover, most organizations are already trying to meet the needs of a growing user base for their existing applications and if a homegrown BI solution doesn t scale with future growth, it s not really a solution. Even when teams do have BI expertise, they often find that building their first analytics solution solves just a subset of their challenges and that only a flexible analytics platform can meet the full scope of user needs in the right timeframe and at an acceptable cost. Read on to understand in detail the potential pitfalls of a build approach and why a buy approach makes the best business sense for most embedded analytics deployments. We will cover the following project categories: Up-Front Development Costs Time to Market Ongoing Release and Maintenance Costs Support and Training Costs Core Competency Focus PENTAHO 2

3 Buying Over Building The business intelligence software market is large and well established, with vendors like Pentaho offering expertise in deploying complete analytics solutions to application end users. Here we ll discuss the major Total Cost of Ownership (TCO) and Return on Investment (ROI) drivers that illustrate why partnering with a leading analytics provider is often a better decision than building analytics platform functionality on your own. UP-FRONT DEVELOPMENT COSTS These are the major engineering and design costs incurred before any end user application can be launched Just like any major internally developed functionality, homegrown analytics requires significant design, coding, testing, and documentation work. Building analytics is not building a feature but rather building a whole integrated system, and as such the development team is likely to be on the larger side. Indeed, during the initial development phase, an organization can expect to staff a full engineering team on the analytics initiative, including designers, front-end engineers, back-end engineers, and QA staff. This can necessitate several new hires, depending on the level of BI experience at your organization. Under a buy scenario, the technical work is not only significantly reduced, but it is also less complex. Your technical team needs only to integrate or embed the 3rd party analytics software with existing applications, and this normally requires only a web developer skillset and potentially some lightweight customization. As such, just 1-2 people can often perform virtually all the technical work (and in a much shorter time see Time to Market below). Meanwhile, with Pentaho, business and IT analysts are able to use out-of-box tools (i.e. no coding) to integrate multiple sources of information in a data warehouse or data mart as required. Bottom Line: The cost of staff and complexity of technical work for your analytics project is drastically lowered in a Buy scenario. TIME TO MARKET This is the time between the start of development and the launch of analytics functionality in production with users For most technology groups, launching analytics is essentially launching a new product and, in a build scenario, the time required from start to finish can often be over a year. This is because of the sheer number of integrated components that need to be designed, developed, and tested, which often include: Data and application integration, including orchestration, ETL, and cleansing for required data sources, as well as metadata management Reporting engines, including components like aggregation, filtering, custom calculation, and more Data visualizations with user interaction capabilities A presentation layer with user interfaces for analytics, administration, scheduling, distribution and more Overall system security, administration, content management, and business logic PENTAHO 3

4 A high degree of complexity opens the project up to the risk of unanticipated delays and course corrections. This all means the day that the first user gets value out of your analytics offering is pushed further and further out into the future. Moreover, homegrown BI capabilities often fall short in that they may still require engineering involvement each time a user requests a new type of report. Faster time to value means being able to satisfy users, drive valuable insights, and recoup costs more quickly. The drastically reduced engineering burden in the buy scenario normally facilitates a launch in a quarter or less, while vendors like Pentaho can also provide developer enablement and implementation best practices to further accelerate that timetable. Uncertainty is reduced, as the manageable embedding and integration tasks are already documented for your technical team. Bottom Line: A decision to Buy means a much easier and faster analytics implementation, with the added benefit of expertise from a battle-tested partner to help ensure success. This means you see accelerated ROI. ONGOING RELEASE & MAINTENANCE COSTS These are costs after the initial rollout, which are related to application improvements, bug fixes, and development of any new required features Once you deliver an internally built 1.0 analytics module to users, you will undoubtedly have to fix any reported errors and perform basic enhancements. During your first few releases, getting the kinks out of your application can be a very time and labor intensive process. On an ongoing basis, you will most likely need several technical FTEs just to keep up with this maintenance. Your users will also have more significant new feature requests such as new types of analysis and visualization or the ability to connect to their own in-house data sources. And what if you add your own new data stores, such as Hadoop or NoSQL, that need to be integrated? Each of these would represent an additional build effort unto itself, meaning that you d have to spin up at least a portion of the initial launch team again for at least several months. This also applies to building additional scalability and speed into your system as your user load, data volume, and query complexity increase. Under a buy scenario, many of the components for future releases will already be readily available, depending on the purchased platform s breadth and extensibility. As such, the only real engineering work would be light embedding and customization, as discussed under Up-front Development Costs. In addition, your platform vendor will be responsible for fixing errors that relate to the core analytics functionality. As such, your maintenance costs are reduced significantly. Finally, analytics platforms like Pentaho are already architected for scalability across data size, user base, and query complexity so you can rest easy regarding how to accommodate future growth. Bottom Line: The right 3rd party BI platform minimizes the burden of maintenance on your team and makes several releases worth of features available up front for quick, scalable integration with your existing systems. PENTAHO 4

5 SUPPORT AND TRAINING COSTS These are ongoing costs related to providing your analytics end users with technical support and feature training Whether you buy or build, you will need to provide end user support and training on analytics. However, if you build analytics, you must support the whole enchilada, which includes the very hairiest troubleshooting. Further, you must create documentation and training materials from scratch. When you choose to work with an established analytics vendor, you can escalate any serious product issues to their support team and they help resolve the problem. Plain and simple, this means you don t need as many people supporting your analytics offering. At the same time, you can leverage the vendor s existing training materials, documentation, and best practices to accelerate the process of getting each end user trained and successful. Bottom Line: Because analytics providers stand behind their products with technical support and have created many training and troubleshooting resources already, you incur substantially lower services costs when you buy analytics. CORE COMPETENCY FOCUS This refers to your team s ability to continue executing on technology and business strategy for the market you are in (i.e. not business intelligence) Whether your main business is finance and investing, sector-specific SaaS, networking hardware, or something else entirely analytics is meant to fit into your existing strategy. Unfortunately, when building BI in-house, teams can become so consumed with analytics that it takes resources away from improving and expanding your bread and butter capabilities. Indeed, many technology groups decide to buy an analytics solution because they don t want to have to ramp a whole engineering team up on the underpinnings of data orchestration and business intelligence. The opportunity cost is just too high. Rather, they want that team to focus on building and maintaining the best possible core systems that will help drive business success. While providing actionable analytics to customers, partners, or internal staff can generate competitive advantage, you don t have to become an analytics software development company in fact, doing so is probably a poor strategic and financial decision based on your current market position. Bottom Line: Buying BI minimizes the investment, risk, and overall resource drain of your analytics initiative, ultimately letting you do what you do best that is build the best core capabilities and competitive advantages you can. PENTAHO 5

6 Build vs. Buy: Summary of ROI & TCO Drivers BUSINESS DRIVER BUILD SCENARIO BUY SCENARIO BOTTOM LINE Up-front Development Costs Fully staffed engineering and design teams up-front Design, coding, development, documentation for 1-2 FTEs working up-front Manageable embedding & re-skinning tasks with vendor analytics components TCO, cost of staff, & complexity of work is drastically reduced in the Buy scenario all components Time to Market A year or more to market Develop integrated system with ETL, metadata management, reporting, analysis, OLAP, visualization, user interfaces, security, administration 2-3 months to market Embed vendor s components with light customization Vendor provides best practices, developer enablement, documentation ROI accelerated in Buy approach, as users go live much faster with lower risk, drastically shorter go-to-market timeframe Ongoing Release & Maintenance Costs Full team working during new feature roll-outs Staff responsible for full development of new releases, all bug fixes and enhancements 1-2 FTEs during new feature launches BI platform bug fixes handled by vendor BI components ready when you need to integrate them Ongoing TCO and staff investment is substantially reduced in the Buy scenario Support & Training Costs Must resolve all technical support issues Must develop training materials & documentation from scratch Escalate major product issues to vendor s support team Standard training materials, documentation already created Ongoing TCO and services headcount reduced in the Buy scenario Core Competency Focus Must build deep internal analytics expertise Major BI development investment has potentially high opportunity cost Allows engineering team to focus on building best possible core products Minimize risk and distraction Buying BI can reduce risk and boost overall business ROI because it often makes better use of total company resources Potential distraction from core products Note FTE (full-time equivalent) and time to market estimates are approximations based on customer experience PENTAHO 6

7 Conclusion We hope this paper provided some useful reference points as you prepare for your analytics initiative. While every use case has its nuances, Pentaho s experience has time and again illustrated that a Buy approach to end user analytics reduces up-front and ongoing costs, accelerates time to market, and reduces risk translating to higher ROI and lower TCO. As you evaluate your project options, we encourage you to take a look at Pentaho s complete platform for embedded analytics. Unlike proprietary visualization tools or manual reporting processes, Pentaho s platform is built to deliver a fully tailored analytics experience to empower your customers, partners, and employees through: Embedded visual analytics in your technology products on virtually any blend of data sources, including Big Data, relational databases, and web services Extensible technology that enables flexible visual branding, scalable multi-tenant cloud deployments, and robust security configurations all to fit your technical architecture A visualization and dashboard design toolset that makes 100% custom analytics user experiences possible To learn more, visit embedded-analytics PENTAHO 7

8 Learn more about Pentaho Business Analytics pentaho.com/contact +1 (866) Global Headquarters Citadel International - Suite Hazeltine National Drive Orlando, FL 32822, USA tel fax US & Worldwide Sales Office 353 Sacramento Street, Suite 1500 San Francisco, CA 94111, USA tel toll free United Kingdom, Rest of Europe, Middle East, Africa London, United Kingdom tel +44 (0) toll free (UK) FRANCE Offices - Paris, France tel toll free (France) GERMANY, AUSTRIA, SWITZERLAND Offices - Munich, Germany tel +49 (0) toll free (Germany) BELGIUM, NETHERLANDS, LUXEMBOURG Offices - Antwerp, Belgium tel (Netherlands) toll free (Belgium) ITALY, SPAIN, PORTUGAL Offices - Valencia, Spain toll free (Italy) toll free (Portugal) Be social with Pentaho: Copyright 2015 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest information, please visit our web site at pentaho.com.

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