BeyeNETWORK RESEARCH REPORT. Open Source Solutions: Managing, Analyzing and Delivering Business Information. by Mark Madsen

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1 BeyeNETWORK RESEARCH REPORT Open Source Solutions: Managing, Analyzing and Delivering Business Information by Mark Madsen BeyeNETWORK th Street Suite 310 Boulder, CO

2 TABLE OF CONTENTS About the Survey... 1 Methodology... 1 Respondent Profile... 1 Executive Summary and Key Findings... 2 Introduction... 4 A Primer on Open Source... 4 Community Versus Commercial Open Source... 4 Open Source in the Business Intelligence and Data Warehouse Market... 5 Detailed Findings... 7 Open Source is Maturing... 7 Indications of Open Source Growth in the Business Intelligence Market... 7 Use is Mainly for New Projects... 8 People are Using Open Source Across All Software Categories... 0 Databases Data Integration Business Intelligence Advanced Analytics Who is Using Open Source and How are They Using It? Organization Size and the Use of Open Source Scope of Use What are Organizations Buying? Use by Consultants and Systems Integrators Rationale for Use Problems Encountered When Adopting Open Source Information Resources Found Useful Recommendations Profile of Survey Participants Pentaho Solution Overview BeyeNETWORK th Street Suite 310 Boulder, CO

3 About the Survey The report presents conclusions and recommendations based on a survey about open source software for reporting and analytics. It covers all parts of the data warehouse stack from the database to end user delivery. It is written for business and technical managers who are responsible for delivering reporting, business intelligence (BI) or analytics, whether part of a BI program or embedded in applications and websites. The research evaluated the rationale, practices and benefits that are driving use of open source as an alternative to the traditional vendors in this market. It also looked at the specific software projects, the scope and status of its deployment, and the challenges and practices of participating organizations. The business intelligence, reporting and analytics market has different drivers and requirements from the typical IT development and applications market. Most open source studies target open source impacts on operating systems, development tools and application infrastructure. The point of the research was to get a better picture of the factors influencing IT adoption in the BI and data warehouse segment. Methodology The research for this report is based on a survey and interviews with both consultants and IT professionals that Third Nature conducted between July and August of 2009 in addition to solicited survey participation via the BeyeNETWORK, sponsors' distribution lists and websites and the annual MySQL conference. More than 1,000 people completed the survey, although not all respondents answered every question. The aim was to gather a broad perspective of the evaluation, use and practices in both open source centric communities and in the broader IT market. Respondent Profile The majority of survey respondents are corporate IT professionals across firms of all sizes, with consultants being the next largest group. The composition of roles is shown in Figure 1. Most respondents are in North America and Europe, with 81 countries represented in the sample. Computer hardware, software and service companies are the largest industry represented with 22% of the total, with the rest spread across 15 other industry categories. Figure 1: Roles of survey respondents Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 1

4 Executive Summary and Key Findings Venture capital flooding into open source start ups over the past several years resulted in an explosion of enterprise ready tools and applications. Many of these start ups are focused on the business intelligence market. Open source rose quickly in the information management market, from almost nothing a few years ago to community and commercially supported projects for every possible use. The goal of this report is to explain aspects of the usage, challenges and practices of organizations adopting open source in the business intelligence and data warehouse market. Key questions explored in this research were: What organizations are using open source in the BI/DW segment of the market? What software is being deployed? What are the benefits and challenges? The survey found that interest and adoption are widespread. One third of respondents stated they use open source reporting, data integration or database software for analytic uses. More than one third are currently evaluating open source alternatives. Only 12% reported no plans to use open source. The top reason for adoption is still cost savings, although reduced vendor dependence and ease of integration followed closely behind. Some companies used open source deployments as a means of keeping their incumbent vendors honest. Highlights of the survey findings include: When dealing with database performance problems, people are more than twice as likely to migrate a data warehouse to an analytic database as they are to a different traditional database, open source or not. While this is good news for analytic database vendors, it's not that good because people are still married to their current choice of database. They are more likely to change, redesign or replace every other tool in the BI stack before replacing the database. In all software categories except advanced analytics, the most commonly used open source projects were from commercial open source vendors. The perception that open source is done largely by amateurs and volunteers is not true in this market. Experience breeds adoption. Organizations with less than one year of experience with open source use only one open source product, i.e., a BI tool, while the rest of the system is built from proprietary software. If the organization has been using open source longer, it is likely to be using more tools in different categories. All organizations with more than three years of production use are using more than one open source product. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 2

5 Open source is all about new projects. More than half the usage of open source was for new projects, with minimal focus on replacing existing tools. This is good news for open source projects and vendors, and potentially bad news for traditional vendors. It means open source is being adopted in the growth areas of the market, and that could be taking new customers from traditional vendors or taking away the mid to smaller sized organizations which have previously been priced out of the market. Traditional BI and data integration vendors have been introducing midmarket programs as they look for growth. By losing new projects or midmarket companies, they lose revenue in the fastest growing part of the BI market. There's a fine line between a community edition of an open source project and crippleware, and some vendors are crossing that line. By holding back features in the community edition in order to entice people to pay for the professional version, some vendors are inadvertently turning away customers. Survey respondents complained that some community versions were feature limited or scale limited to the point where they couldn't be used on a real project. The primary complaints about open source are related to maturity of the software, lack of internal skills and availability of consulting services. Given the rapid pace of innovation in open source projects, the gap in both core features and maturity between open source and traditional vendors is quickly closing. Roughly one third of open source users are purchasing services and support from open source vendors today. Based on this pattern in what is largely an early adopter segment, expect the commercial open source vendors to continue their growth. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 3

6 Introduction A Primer on Open Source Open source software (OSS) is released under a license that differs from traditional software licenses. The license guarantees several freedoms: access to the source code, the ability share to the code with others and the freedom to modify or deploy it as you wish. One misconception is that you must share any changes you make to the code. The requirement to share only applies if you give or sell the software to others outside your organization. If you redistribute, then any changes or additions you made must be provided as source code. If you don't redistribute, you do not need to share your work. Open source software is available as a project which is maintained by a community of people who write the code and documentation, provide quality assurance and help to manage distribution. These people may be independent volunteers, contract programmers or they may all work for a software company that maintains the software. Vendors use open source to enable a means of software production and distribution that provides lower operating costs and other benefits back to the vendor. Community Versus Commercial Open Source There are two models of support for open source. One is community based open source, often call "free and open source" or FOSS for short. The other is commercial open source software, usually abbreviated as COSS. Most people are familiar with the free and open source model because it's been around the longest and receives the most press coverage. In this model, volunteers contribute their efforts to development and maintenance. In some cases, they may be full time employees of a non profit organization owning the software, but the project does not operate like a traditional software company. There is no profit motive. The software is available free of charge to anyone who wants it. Many of the myths about commercial open source stem from the model's origin in the shareware and FOSS world. In the early days, open source was often designed and built for personal use by individual developers rather than as part of an organization's IT infrastructure. Because most people are familiar with the ideas of the FOSS model, they mis apply the ideas to the COSS model. Commercial open source came about for a different reason. Commercial open source vendors try to make money by filling the gaps in the FOSS model. Enterprise use of open source was challenging in the early days because the products lacked much of the finishing work commercial products receive. Documentation in FOSS projects is often weak, quality varies and regularly scheduled fixes, software releases and support are often lacking. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 4

7 Commercial open source evolved with recognition that companies are willing to pay for support, service, and other less tangible items like indemnification or certifying interoperability, for example of a BI tool with a given proprietary database. A commercial open source vendor is just like a traditional software vendor, except that the source code is not shrouded in secrecy. This enables more and deeper interaction between customers and developers, making the open source model more communityfocused than the traditional model. In contrast to the majority of FOSS projects, commercial open source vendors employ most of the project's developers and expect to make a profit while doing so. They provide the same services and support that traditional vendors do, frequently with more flexibility and lower cost. COSS vendors use elements of the proprietary model such as providing support contracts or selling non open source components that can be purchased in addition to, or in place of, the free version of the software. The two different versions of software (community and enterprise) can cause confusion. When you evaluate software it is important to note whether you are looking at the free or paid version. Some less scrupulous software companies have obscured this line or are calling themselves "open source" without an open source license or with software that you can get only after paying them for services. If you don't have access to the source code but they give you a software executable for free, then they are really offering a free trial version. The terms of your use can change at any time. Unless the vendor delivers software with source code that is under an OSI certified license, it is not open source. There is no regulation of terms or labels so these "fauxpensource" vendors will continue to operate until there is a backlash. COSS vendors are still software companies. If you purchase a paid enterprise version then you'll find that the experience is not substantially different than buying software from a proprietary vendor. The key difference is the transparency with which COSS vendors operate. As one interviewee noted, "We can see bug reports and enhancement requests made by anyone and help with prioritization by voting on their implementation. The same applies to features for upcoming releases." This level of transparency is not often found in proprietary vendors. Open Source in the Business Intelligence and Data Warehouse Market There are open source projects available for every element of the business intelligence and data warehouse stack. This includes core products such as databases as well as emerging analytic database platforms. The availability of advanced technology like analytic databases surprises many people because the common assumption is that open source is low cost, low feature software. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 5

8 As such, it applies to the commodity market rather than innovative or emerging technologies. Commercial open source changes the dynamics of software development by bringing these technologies to market as open source before they have a chance to go through a standard proprietary growth phase. This acceleration of the commodity process is one of the biggest effects open source brings to the enterprise software market. The survey conducted for this research asked people what open source software they are using or evaluating to assess the popularity of projects in the BI market. The emergence of commercial open source accelerated development of the software and open source adoption over the last several years. In three of the four software categories examined, the top ranked open source software is provided by commercial open source vendors. The holdout category is still relatively new to most organizations, whether using open source or not. FOSS and COSS are available for every possible BI application, from traditional reporting and OLAP tools to advanced data mining and statistics. Even more exotic tools like advanced data visualization, simulation and web based geographic information systems are available. Data integration software is a more recent entrant in the developer tools market, with ETL, data quality and data federation options available. The survey results showed that these tools are being applied equally in BI and transaction processing environments. Regardless of what you are seeking, it is likely there is an open source project to fill your need. Enterprise caliber software is readily available for the core data warehouse and BI components. The primary question is whether it has the features you are looking for. Software developed as open source is no different from traditional commercial software. The difference lies in a license that gives you more freedom with the code than a proprietary license. This means you should evaluate open source tools as you would any other software, by asking whether it meets your requirements at a competitive price. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 6

9 Detailed Findings Open Source is Maturing Indications of Open Source Growth in the Business Intelligence Market The software for most categories has matured, thanks in large part to venture capital that allowed commercial open source vendors to add important features and fill in major gaps. Now that the products meet most organizations minimum functional requirements, the vendors hope to capitalize on the economics behind the open source method of deploying and distributing. Download statistics for the most used projects have in many cases surpassed the million mark. While downloads are not a good measure of production use, they are a good indicator of interest. If even a fraction of a percent of downloads turn into active users, these products will have as many users as the major vendors. The difference is in paying customers approximately half of the active users of open source BI and data warehouse tools did not purchase anything. One vendor claims that they have more than 300,000 registered users of the software. While that claim does not indicate production use, the software is freely downloadable without registration. This means the users voluntarily went through the process of registration, so it's safe to assume that they were either doing a hands on evaluation or actively using the product. A single digit conversion rate puts them on par with many companies that have been in this market for twice as long, a considerable growth rate for any start up business. The current growth rate looks like it will continue. Roughly 30% of respondents said they are currently evaluating or piloting open source in one of the four software categories surveyed. About 20% of the respondents indicate that they are "considering," which is really just an indication of interest. We are still in a very early stage of open source use in the BI market. 43% of the respondents to the survey are not using any open source in their BI environments today. 23% Less than 1 year 46% 1 to 3 years 31% More than 3 years Figure 2: Length of use Almost half of those who are using open source in a production system have been doing so for less than one year, as shown in Figure 2. This indicates that many are still on the initial learning curve, and the people running in production today should be considered early adopters. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 7

10 An indicator of future growth is that experience with open source breeds new adoption. There is a direct correlation between the years of experience with open source and the number of different types of tools in use. No organization with less than one year of use is deploying more than one open source product. Some respondents said they wanted to see how well the first tool worked before deciding whether to replace any of their other tools. All organizations with more than three years of use had at least three different open source tools in place. The use of open source for data delivered or obtained outside the organization is another area showing strong response. 14% of respondents are delivering information externally. In this group, more than two thirds are using open source BI tools instead of traditional vendors' products. Use is Mainly for New Projects One question posed was, "Are people using open source to replace existing tools?" As Figure 3 shows, tool replacement and the retiring of custom code are happening at a relatively low rate, and the dominant use is new projects. This is not good news for traditional vendors in the market because it means that they are being forced to compete for new revenue. These projects are the long term revenue stream any vendor needs to sustain itself. This is good news for open source projects and vendors because it means that open source is being adopted in the growth area of the market. Database Data Integration BI Adv. Analytics 53% 50% 41% 36% 25% 18% 16% 14% 18% 15% 14% 14% 11% 10% 10% 10% 13% 8% 18% 7% Replacing proprietary software Replacing internally developed software Supplementing a system with similar features Adding new functionality to an existing system Figure 3: Current or expected scenario for deployment by software category Using as part of a new system or project Open source could be taking new customers from traditional vendors in the small to midmarket. This is an area they've been looking to for growth, introducing new small Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 8

11 and midmarket products and sales programs. By losing new projects or midmarket companies, they lose a long term source of revenue in the fastest growing part of the BI market. The other possibility is that open source is providing capabilities to an unserved segment of the market. Traditional vendors' products have been priced too high for most small and medium sized companies. This sample doesn't provide a clear answer. It appears that a little of both exists. People are Using Open Source Across All Software Categories Use is distributed across all layers of the BI stack and follows a pattern aligned with maturity of the software and the most common uses. Fewer organizations have both the sophistication and the need for advanced analytics or for embedding interactive reports into an application, so these should show somewhat lower use. The software was divided into four categories based on the layers in traditional business intelligence or data warehouse stacks: database, data integration, and reporting and analytics tools. Because there are so many different front end tools, they were further separated into business intelligence (reporting, OLAP) and advanced analytics (data mining, statistics, visualization). Software use by category is shown in Figure 4. Database Figure 7: Purchases related to open source 18% 13% 18% 29% 22% Data integration and ETL 18% 12% 17% 31% 22% Business intelligence 14% 8% 22% 37% 19% Advanced analytics 5% 8% 18% 43% 26% In production Prototype or pilot Evaluating Considering No plans Figure 4: Interest and use of open source by software type Interest in all categories is strong and growing as shown by the number of organizations in the currently evaluating phase. One interesting finding from the survey is that experience with open source leads to increased adoption of other open source tools in a sort of virtuous cycle. This means we should expect to see more use of open source in the BI and data warehouse stack as more companies gain experience. It's also a sign the proprietary vendors will face more open source competition in the future. This remainder of this section examines each of the four software categories in more detail. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 9

12 Databases Production use of open source databases shows the greatest use in this market. Open source databases have been in existence for many years, while many prominent projects in other categories are less than five years old. The nature of analytic workloads is holding back open source database adoption. Most of the engineering effort for OSS databases is focused on transaction processing. Analytic use requires better handling of complex queries, large single query data volumes and variable user concurrency. Overall, interview data shows that open source database use would be higher if it weren't for poor analytic support capabilities and lower query performance. How Much Data is Being Stored and Used? The chart in Figure 5 shows the responses to the question, "How much data is being stored or accessed?" This chart shows the size regardless of database type the constraint is that people are using open source in some part of the data warehouse stack, so an open source BI tool accessing a proprietary database would be included. 24% 14% 14% 15% 13% 4% 1% 3% Less than 50 to 100 to 500GB to 1 to <5TB 5 to <20TB 20TB to More than 50GB <100GB <500GB <1TB 50TB 50TB Figure 5: Data volume distribution (includes open source and proprietary databases) The distribution is the same as what we see in the overall data warehouse market where approximately 60% of databases are less than one terabyte in size. This survey shows most (67%) of respondents are accessing less than a terabyte of data and about onethird (38%) less than 100GB. While most of the sample (82%) is using proprietary databases such as Oracle or SQLServer, 18% of the survey respondents are using open source databases like MySQL and Postgres to store and query their data. Open source databases are storing less data than their proprietary counterparts. In this sample, 76% of the open source databases running in production are less than a Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 10

13 terabyte in size. Even so, 36% are more than 500GB in size, which is not insignificant for many organizations today. Databases and BI Performance Database sizes have increased significantly across all industries during the past few years. Size and growth are critical factors affecting query performance, but query response time at the sub terabyte scale is still a challenge for many organizations. This survey looked at performance because one statement about open source is that the software is slow when compared to proprietary counterparts. The survey responses show that roughly 30% of users reported problems with performance related to data size and scalability or with user concurrency. Overall, these complaints fell below other problems discussed in the concluding sections. Figure 6 shows the distribution of performance complaints. Poor interactive BI or analytics performance 69% Poor performance loading data Poor ETL or data integration performance Poor batch reporting performance 37% 33% 33% Figure 6: Complaints related to open source performance Query performance dominates complaints. This is not a surprise because query is the most visible element and affects the largest number of people. It's also the most difficult to diagnose because there are many design and technology factors that can affect query speed. Getting data loaded turns out to be less problematic for most people. That these problems are so low is a surprise because meeting batch ETL windows has historically been a major complaint in data warehouse and data mart projects. The standard solutions for these problems apply: database and application tuning and redesign. Open Source Database Use The high profile and difficulty of getting good query performance is one of the major factors driving IT customers to look at specialized analytic databases. This is particularly true if they are using standard open source databases like MySQL because of the lack of data warehouse specific scaling and performance features. Analytic databases include horizontal scale out databases that grow by adding more computers rather than resources to a single large computer, columnar databases and specialized data warehouse appliances that marry open source software to hardware. Several of the new open source analytic databases appear in the results shown in Figure 7, which lists all of the open source databases people report using in production. MySQL's dominance is not a surprise given that it is the most popular open source Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 11

14 database on the market. MySQL's popularity bodes well for MySQL compatible and MySQL engine products aimed at analytic workloads. MySQL Postgres Infobright EnterpriseDB BerkeleyDB Ingres Firebird Palo CouchDB SQLite MonetDB LucidDB Kickfire Bizgres 11% 10% 8% 7% 7% 3% 3% 3% 3% 2% 2% 2% 44% 75% Figure 7: Open source databases in use When faced with database performance problems, if the choice is to move from MySQL to an expensive traditional database, less expensive analytic databases become more appealing. This is borne out in the survey data which shows that people are more than twice as likely to migrate a data warehouse to an analytic database as they are to a different database in the same class. Because most open source analytic databases are aimed at databases less than 5 terabytes in size, they align well with the bulk of the data warehouse market and particularly with the open source database market. Data Integration As a category, data integration (DI) tools are almost as commonly used as databases, outranking business intelligence tools. Given the relative youth of these projects, it is surprising that they are as common as they are. The commercially supported open source integration tools have been available for a much shorter time than open source BI tools. The investments in commercial open source tools have had a significant impact on product maturity, making most of the suitable for organizations looking for "good Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 12

15 enough" tools. These products support all the basics needed in data integration projects. Their weaknesses are in the areas of administration, team support and advanced integration features such as data quality functions dealing with semi structured data. The open source tools are primarily single purpose, like the early ETL tools, although Talend has been extending its product line with data quality and master data management features. Batch ETL for a data warehouse or mart 30% Operational integration Data migration efforts Data quality efforts Master data management efforts Low latency ETL for a data warehouse or mart 21% 15% 15% 10% 8% Figure 8: Uses of open source integration software A factor increasing the popularity of this category is the use of these tools for operational as well as analytic data integration, which ranks as second most popular in the list shown in Figure 8. There are two different ways to use data integration tools: linking transactional applications or feeding data to business intelligence systems. These uses affect the approach, methods, feature requirements and best tools for the job. BI systems are most often loaded in batch cycles according to a fixed schedule, bringing data from many systems to one central repository. They have relatively large volumes of data to process in a short time, but have little concurrent loading activity. Most data integration products were originally designed to meet the specific needs of the analytic data integration market. Most application integration projects need data from one or two other systems, not the many sources and tables feeding a data warehouse. The scope is usually smaller, with lower data volumes and narrower sets of data being transferred and minimal transformation required. These differences in frequency of execution, data volume, latency and scope of sources are technical elements that differentiate operational and analytic data integration. Data integration is a small element of an application project, unlike a data warehouse where DI may consume 80% of the project budget and timeline. Hand coding is common in application projects because data integration is thought of in terms of application glue. In BI projects, hand coding is most often a way to save money on the high cost of enterprise ETL products. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 13

16 Community open source data integration tools can provide the cost advantages of hand coding with the productivity advantages of traditional data integration software. The most popular open source data integration tools in the survey are Pentaho DI/Kettle, Talend and Jitterbit, all commercially supported products. Pentaho and Talend make up 75% of the use in this category. One interesting element is the breakdown by use. While both are used for ETL, Talend is more likely to be used for operational data integration than any of the others. The full list of tools and their level of use is shown in Figure 9. Pentaho DI / Kettle Talend Jitterbit DataCleaner Red Hat Apatar OSDQ Open Data Quality Clover 13% 8% 5% 5% 2% 2% 2% 33% 42% Figure 9: Open source data integration tools in use These tools are established in the developer market which has been the traditional stronghold of open source software. Expect open source to be a key element of data integration (and especially of operational data integration) in the future, similar to open source use in application development environments today. Business Intelligence A detailed breakdown of the use of BI tools that are in production today is listed in Figure 10. Traditional reporting and dashboards are the most popular uses. This mirrors what we seen in with non open source BI tools in the market. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 14

17 Static reports Dashboards or scorecards End user or interactive reporting Reporting against an application database Reports embedded in an application or website OLAP 20.7% 17.1% 16.5% 15.9% 15.2% 14.6% Figure 10: Breakdown of production use for the BI and reporting tools category Application reporting and embedding are almost as popular, something you do not see with most of the traditional BI tools. This is partly due to the technical problem: marrying features to an application requires easy component integration or customizable code. Most proprietary BI tools are built to be stand alone applications, making them inappropriate for this type of use. For software providers, another integration need is customizing the BI interface to match the interface of the application. Software providers also have to worry about the incremental cost to the product. Leveraging open source can be a zero or low cost alternative to using BI tools from one of the proprietary vendors. One interviewee delivering software as a service applications stated that "Without open source BI tools, I would not have been able to provide reporting capabilities in my applications. My margins are too narrow to license BI tools from one of the big vendors. They're also more difficult to manage in a multitenant environment." Several respondents using open source BI tools embedded in their applications said that they chose this alternative because it offered neutrality. Because of the BI market consolidation, partnering with one of the major BI vendors could alienate customers invested in a competing vendor's applications. Pentaho and Jaspersoft together are used by three quarters of the survey respondents. The numbers do not add up to 100% because people in many organizations are using more than one tool, often for complementary purposes. One other thing to note: Julian Hyde is the founder of the Mondrian Project and is the Chief OLAP Architect at Pentaho. Pentaho hosts the Mondrian project, its forums, continuous build server, road map and wiki. Pentaho developers have accounted for more than 90% of the code contributions and fixes over the past three years. The list of projects is shown in Figure 11. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 15

18 Pentaho Jaspersoft Mondrian (a Pentaho project) BIRT Jfree SpagoBI Openl MarvelIT Palo OpenReports 28% 26% 19% 14% 9% 5% 5% 2% 2% 47% Series1 Figure 11: Open Source BI tools in use One element of open source BI that doesn't appear in the survey data is the people who chose to develop their own software using open source components. There are SQL generators, user interface components, graphing libraries and all the other elements needed for a do it yourself model. This is the approach taken by a number of web based companies and government agencies where the number of users is very high and the information delivery capabilities are well defined or constrained. While this is a relatively small percentage of the sample, it's useful to know because 14% of the respondents mention delivering data to external users within the scope of their deployments. Advanced Analytics This advanced analytics category is a combination of different types of software that fall outside the normal "query and reporting" realm, including statistics, data mining, visualization, modeling and simulation. Each type of software is different, but they all share a low overall adoption rate (5% in production in our sample). The low adoption rate is mostly due to the lower applicability of these tools for many organizations, as well as the level of analytical sophistication found in businesses rather than the fact that the tools are open source. The most popular products in use by survey respondents are shown in Figure 12. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 16

19 R Weka (a Pentaho project) RapidMiner Knime Graphviz Orange Processing Axiis Taverna Cytoscape 8% 8% 7% 4% 4% 3% 2% 23% 42% 46% Figure 12: Open source analytics tools in use The R project has long been popular for statistics, so its appearance at the top of the chart is not a surprise. Weka data mining software is a Pentaho project and pillar of its commercial offering. Pentaho employs the lead architect and steward of the Weka project (Mark Hall), drives the road map and release cycles, and is the only company that can sell a commercial Weka license, including support. Weka has been in existence for a long time and is often used in university settings, aiding visibility. Unlike all the other categories, the top two tools are community open source projects. RapidMiner and Knime are commercial open source products with freely available community editions. There is a tremendous amount of analytics software available as open source. The challenge for most organizations is that the tools are either single purpose, for example tied to a specific technique, or they are available as libraries of code rather than tools. This is the case with most data visualization software. Who is Using Open Source and How are They Using It? Organization Size and the Use of Open Source One persistent myth is that small companies are the primary users of open source. While there are more small organizations using open source today than mid sized or large, as show in Figure 13, the data also shows that medium and large organizations are doing more evaluations. This is a change from an earlier survey, where small companies were leading in both areas. The change is interesting because it reflects a shift in mid size and larger organizations as users of open source BI and data warehouse products. While the small company base Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 17

20 is important, the products appear to be good enough to meet the more stringent requirements of larger organizations. This is good news for commercial open source vendors because the largest group of production users were the least likely to pay for support or services. As companies with a better ability to pay move into the market, the revenue growth for open source vendors should increase and with it the quality of service and support. Small 32% Using Medium Large 23% 23% Small Medium Small 37% Large Evaluating Medium 41% Large 38% Figure 13: Open source use and evaluation by size of organization Scope of Use One common belief about analytics and BI projects is that open source is more likely to be used by departments in large organizations and across the company in smaller organizations. Figure 14 shows that small organizations are more likely than medium and large to do company wide deployments, and large organizations are doing smaller deployments, supporting this belief. Small companies and departments of large organizations share similar characteristics: they are often constrained by budget, they have a smaller user base and their usage is more uniform, making smaller deployments easier. Small Medium Large 27% 32% 40% 38% 35% 27% Department or Division Corporate wide Figure 14: Scope of open source use Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 18

21 Despite this general pattern, there are enterprise wide deployments of open source in large organizations. 40% of large organizations plan to or have deployed a BI or DW application corporate wide with some open source components, demonstrating a level of software maturity. What are Organizations Buying? More than half of the organizations did not purchase anything. They are using the free and community supported versions of the software. The current data reversed one conclusion from a prior survey: smaller organization purchase at about the same rate as larger organizations. What the new data shows is that smaller organizations are less like to pay for professional or enterprise versions. Figure 15 shows the purchases broken down by organization size. Organization size is reflected in purchasing profiles. Larger organizations are more likely to purchase training. Interviews with survey respondents found that larger organizations have more complex projects with more users, placing an emphasis on internal first line support. They also tend to dedicate more budget to staff development. Different forms of maintenance and support appeal to different buyers as well. Mid sized and large companies are more than twice as likely as small companies to purchase subscriptions. Small 14% 13% 38% 36% 30% 29% 23% 54% No purchasee Maintenance or support contract Training Consulting or installation services Phone, or on site support from the vendor Medium 9% 22% 28% 28% 31% 31% 38% 53% Commercial license Phone, or on site support from a third party Subscription to value added, enterprise features Large 6% 24% 21% 33% 33% 45% 52% 58% Figure 15: Purchases by size of organization Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 19

22 This information should help to budget for an open source implementation. Based on the size of your organization, you can see what the most common purchases are and check the prices on these items. Use by Consultants and Systems Integrators Many people believe that consulting firms and systems integrators are more likely to use open source because it allows them to be more competitive. They gain an edge by saving customers money on software licenses or by having more customizable tools for projects, thus pricing themselves under competitors or providing a better fit with client needs. The other hope is that by freeing project budget from the software licenses, this could translate into more money spent on work with the consultants. While these points are all valid, the survey data does not support that belief. Consultants are less likely than IT professionals to use open source tools in this space (10% for consultants to 36% for IT). The usage by respondent role is shown in Figure 16. Figure 16: Open source use by IT and consultants It is notable that 49% of the consultants and systems integrators are evaluating open source software today, signaling a possible shift in their use. What the data says is that, far from leading the technology market, systems integrators (SIs) and consultants seem to trail it, following the money rather than leading their customers in innovation. Interviews suggest that mostly smaller local or regional consulting companies are providing services using open source software. Even with the sudden rise is evaluation, consultants and SIs significantly trail IT departments. If you are in an IT organization that relies on consultants for project work then using open source tools will require that you factor the availability of qualified consultants into your decision. Given these statistics, they are likely to be rarer than you expect. Rationale for Use Cost savings is the number one reason given for open source, regardless of market. Fortunately for the companies involved, cost savings is also the number one reported benefit after deployment. The results show that the initial license cost is a bigger Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 20

23 obstacle in analytics and BI projects than the total cost of ownership (as reflected in ongoing maintenance costs). Figure 17 shows the top reasons given for choosing open source products. Reduced vendor dependence is surprisingly high in the list of reasons. The benefits anticipated are more than the obvious avoiding of a vendor's technology lock in, for example, the requirement that one run Windows and SQLServer in order to use Microsoft's BI tools. Also mentioned were more options to resolve problems, community support reducing the requirement for vendor aid and using open source to offset the effect of vendor acquisitions. Lower acquisiton costs 66% Open standards Reduced dependence on a vendor Lower maintenance costs 44% 43% 48% Flexibility in deployment Speed of innovation of the software Easier to evaluate or procure Open development process and road Extensibility, customizability of software Access to the source code 33% 32% 32% 32% 28% 28% Figure 17: Reasons for using open source The business intelligence and data warehousing market has seen several years of steady consolidation across all software categories. This consolidation makes it increasingly likely that a formerly multivendor installation is now entirely dependent on a single vendor. Many managers view having all of their technology decisions in the hands of a single vendor as a risk. In light of recent price increases and restrictions imposed by vendors, using open source is proving to be a way to reduce dependence and balance the risk of more vendor acquisitions or unilateral actions like raising prices on a captive customer base. Other advantages of open source software are the ease of adjusting deployments, for example adding or dropping end user licenses and customizing or extending it to fit specific project circumstances. Neither of these is as simple with traditional software. One item related to rationale is what influenced people's decisions. The answers (shown in Figure 18) reinforce the social and community aspects of open source and the web. Product reviews and peer feedback were believed to be the most influential items. One caveat to the data shown is that is a perilous question to analyze because influence is Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 21

24 often rationalized after the fact and people are often unaware of what is really influencing their decisions. Product reviews 47% Feedback from professional peers Prior success with open source software in other systems Information from open source vendors Recommendations from consultants_systems integrators Opinions of independent IT analysts Conmmunity size or activity Opinions of IT analyst firms Information from friends 37% 34% 33% 31% 29% 27% 24% 21% Figure 18: Factors that influence the choice of software Familiarity breeds success the number three item was developers' prior success with open source software. Open source vendors were also rated as trustworthy, appearing ahead of consultants and industry analysts. One item that was not listed but appeared frequently in survey comments was the hands on testing evaluation and testing people did in their own time. With open source tools, evaluations were easier to do than with proprietary software because there were no restrictions. It was reported to be much harder to obtain evaluation copies of software from proprietary vendors without first talking to a sales team. Problems Encountered When Adopting Open Source While most respondents provided favorable information regarding open source, it is not without problems. Figure 19 shows that 47% of respondents reported at least one failed open source evaluation. After going into production, 42% report no major problems. % respondents Yes 47.0% No 53.0% Figure 19: Responses to "Did any open source software fail your evaluation?" While there were many different reasons cited for the failed evaluations, reasons clustered around several key issues shown in Figure 20. While documentation complaints showed up at the bottom of the list, they were the biggest write in complaint in the survey, indicating that this strongly affects some people. Documentation is something community based open source projects often struggle with. It's one of the gaps COSS vendors are trying to fill to make the software more enterprise friendly. Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 22

25 Difficulty getting answers to questions or problems ranked in the top ten. This runs counter to the message that open source vendors and communities are more responsive and quicker to answer questions. We didn't compare this between open source and proprietary vendors, so we can't conclude that it's any worse than the situation with non open source vendors. However, this is an indication that there can be challenges, and that there's room for vendors to improve. Difficulty integrating into existing technology environment Figure 20: Problems encountered with open source Software installation is the biggest source of problems. Often the causes stem from the component nature of the open source products. There can be more discrete elements to configure within the environment. Traditional software components are usually preintegrated. Installation, configuration and reliability problems are directly related to the maturity of the software, as is scalability, which appears in two different places in the list. Performance has been a constant complaint with all BI and data warehouse projects, so we looked at what people were doing to address performance problems. Figure 21 lists the most common practices for dealing with performance problems in this environment. The survey response reflects this, showing the most common solution attempted is tuning the database, followed by throwing hardware at the problem. The number three result is surprising: change to a different reporting or BI tool. Given that the database may be the source of the problem, one would expect database technology changes; however, these appear at the very bottom of the list. The most common complaint about performance in BI environments focuses on the database because it is the hub of most activities. p Installation or configuration problems Crashing or other reliability problems Challenges finding training or education Limited data scalability Difficulty getting answers via the community Difficulty getting answers through vendor channels Limited user concurrency Poor / lacking documentation 5% 17% 24% 21% 21% 32% 31% 30% 41% Open Source Solutions: Managing, Analyzing and Delivering Business Information Page 23