Business Intelligence Essay

1. Integration * 1.1 BI infrastructure * * All tools in the platform use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel. * 1.2 Metadata management * Not only should all tools leverage the same metadata, but the offering should provide a robust way to search, capture, store, reuse and publish metadata objects such as dimensions, hierarchies, measures, performance metrics and report layout objects. * 1.3 Development tools * The BI platform should provide a set of programmatic development tools and a visual development environment, coupled with a software developer’s kit for creating BI applications, integrating them into a business process, and/or embedding them in another application. The BI platform should also enable developers to build BI applications without coding by using wizard-like components for a graphical assembly process. The development environment should also support Web services in performing common tasks such as scheduling, delivering, administering and managing. In addition, the BI application can assign and track events or tasks allotted to specific users, based on predefined business rules. Often, this capability can be delivered by integrating with a separate portal or workflow tool. 1.4 Collaboration * This capability enables BI users to share and discuss information, BI content and results, and/or manage hierarchies and metrics via discussion threads, chat and annotations, either embedded in the BI platform or through integration with collaboration, social software and analytical master data management (MDM). 2. Information Delivery 2.1 Reporting * * Reporting provides the ability to create formatted and interactive reports, with or without parameters, with highly scalable distribution and scheduling capabilities. In addition, BI platform vendors should handle a wide array of reporting styles (for example, financial, operational and performance dashboards), and should enable users to access and fully interact with BI content delivered consistently across delivery platforms including the Web, mobile devices and common portal environments. * 2.2 Dashboards * This subset of reporting includes the ability to publish formal, Web-based or mobile reports with intuitive interactive displays of information, including dials, gauges, sliders, check boxes and traffic lights. These displays indicate the state of the performance metric compared with a goal or target value. Increasingly, dashboards are used to disseminate real-time data from operational applications or in conjunction with a complex event processing engine. * 2.3 Ad hoc query * This capability enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a robust semantic layer to allow users to navigate available data sources. These tools should include a disconnected analysis capability that enables users to access BI content and analyze data remotely without being connected to a server-based BI application. In addition, these tools should offer query governance and auditing capabilities to ensure that queries perform well. * 2.4 Microsoft Office integration * In some use cases, BI platforms are used as a middle tier to manage, secure and execute BI tasks, but Microsoft Office (particularly Excel) acts as the BI client. In these cases, it is vital that the BI vendor provides integration with Microsoft Office applications, including support for document and presentation formats, formulas, data â€Å"refreshes† and pivot tables. Advanced integration includes cell locking and write-back. * 2.5 Search-based BI * * This applies a search index to both structured and unstructured data sources and maps them into a classification structure of dimensions and measures (often, but not necessarily leveraging the BI semantic layer) that users can easily navigate and explore using a search (Google-like) interface. This capability extends beyond keyword searching of BI platform content and metadata. 2.6 Mobile BI This capability enables organizations to deliver report and dashboard content to mobile devices (such as smartphones and tablets) in a publishing and/or interactive (bidirectional) mode, and takes advantage of the interaction mode of the device (tapping, swiping and so on) and other capabilities not commonly available on desktops and laptops, such as location awareness. 3. Analysis * 3.1 Online analytical processing (OLAP) * * This enables end users to analyze data with extremely fast query and calculation performance, enabling a style of analysis known as â€Å"slicing and dicing.† Users are (often) able to easily navigate multidimensional drill paths. And they (sometimes) have the ability to write-back values to a proprietary database for planning and â€Å"what if† modeling purposes. This capability could span a variety of data architectures (such as relational or multidimensional) and storage architectures (such as disk-based or in-memory). * 3.2 Interactive visualization * This gives users the ability to display numerous aspects of the data more efficiently by using interactive pictures and charts, instead of rows and columns. Over time, advanced visualization will go beyond just slicing and dicing data to include more process-driven BI projects, allowing all stakeholders to better understand the workflow through a visual representation. * 3.3 Predictive modeling and data mining * This capability enables organizations to classify categorical variables and to estimate continuous variables using advanced mathematical techniques. BI developers are able to integrate models easily into BI reports, dashboards and analysis, and business processes. 3.4 Scorecards These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns key performance indicators (KPIs) with a strategic objective. Scorecard metrics should be linked to related reports and information in order to do further analysis. A scorecard implies the use of a performance management methodology such as Six Sigma or a balanced scorecard framework. Market Leaders IBM. SAS. Oracle. 1 Oracle 1.1 Strengths * * In 2011, Oracle Business Intelligence Foundation Suite, with its principal component Oracle Business Intelligence Enterprise Edition (OBIEE), continued to execute on its stated top-to-bottom BI vision. This year, the products have the highest aggregate Ability to Execute scores. References depict a customer base that is Oracle through and through — 85% run Oracle Database as their data warehouse, nearly 75% run Oracle Applications, and a majority utilizes Oracle Fusion Middleware. Oracle is deployed most broadly (in respect of global deployment) of any vendor in this Magic Quadrant, with average user populations nearing 3,000 and data volumes of more than 5 TB, and it is considered the BI standard for nearly 70% of firms surveyed. While complex workloads are below average, the breadth of use scores in the highest quartile. * * During the Magic Quadrant evaluation process, Oracle announced and completed its acquisition of Endeca, a search-based provider of e-commerce and analytic capabilities. Customer surveys were conducted before the Endeca acquisition was completed; therefore, Endeca is not factored into the Magic Quadrant evaluation of Oracle’s execution, but was considered as part of its long-term product vision. Relatively low numbers of existing references access hybrid data types using OBIEE. Gartner believes that this is a forward-looking acquisition that will have significant impact on the company’s business analytics future (see â€Å"Endeca Buy Extends Oracle’s Ability to Support and Discover Diverse Data† for a more detailed opinion of the acquisition). * * In October 2011, the company announced an engineered system — Oracle Exalytics In-Memory Machine — that leveraged assets across the Oracle stack. The integrated hardware/software analytics solution features a package of OBIEE with new in-memory capabilities (based on Oracle’s acquisition of TimesTen), optimized Oracle Essbase to support the range of traditional BI (reporting, dashboards and analysis), and dynamic planning, what-if and scenario analysis, as well as interactive visualization and data discovery capabilities. The system is designed to support high-performance BI and performance management use cases with the intention of improving the performance, scale and speed of reporting, analysis and planning applications. It is now generally available. * * References select Oracle primarily for functionality, enterprise application integration, and data access capabilities. Additionally, customers indicated that they valued the products’ ability to support large numbers of users. Like other megavendors, the product road map plays an important role in the evaluation process. Ease of use and cost do not factor significantly into the selection process. * * Oracle Business Intelligence Applications (OBIA) are predefined analytic applications for horizontal business processes such as finance, procurement and sales analysis. Customers and prospects find this combination of analytic applications built using the OBIEE toolset appealing, with many buyers selecting both at evaluation time. Additionally, the company also delivers vertical-specific analytic data models for industries such as retail and financial services for IT buyers looking to establish a common data model standard as the foundation for analytics. 1.2 Cautions * References rate OBIEE as difficult to implement, with only SAS Institute considered more difficult. Also, the product was rated as having lower than average ease of use scores. As ease of use for both developers and end users takes on an even more important role in business analytic deployments and evaluations, Oracle must explicitly address these issues or risk being marginalized in user-driven projects. The company has been slow to respond to the data discovery trend. However, some functions are now available in the Exalytics In-Memory Machine, and the Endeca acquisition will add more capabilities in this important area. * * Product functionality evaluation scores remain below average again this year, a trend that appeared in last year’s report. Additionally, customer support and product quality issues are rated below the average (in the fourth and third quartiles respectively) for all vendors in this report. In fact, both support and product quality were also noted as issues that blocked further deployments within customer organizations. This represents a slip from last year’s scores. While not huge red flag items now, they may become more problematic without dedicated company attention to address client concerns. * * Oracle customers use the product mostly for static report viewing, parameterized reporting and scorecard capabilities, leading to below average user complexity ratings. Slightly more than 25% of customers Gartner surveyed for this report run the most current version of the BI suite, which is significantly below average for vendors in this analysis. * * More than 10% of survey respondents indicate that they plan to discontinue, or are evaluating a discontinuation of, software use in the next three years — a relatively high response rate given responses from the prior year. This is above the average for all vendors in this research. 2 SAS 2.1 Strengths * * SAS gets high marks for its global footprint and broad industry initiatives. Unlike some other BI platform vendors, SAS focuses on advanced analytical techniques, such as data mining and predictive modeling, where references acknowledge it as a leader of the pack. SAS’s clients also have above average complexity scores (for the depth of use of different BI use cases) on larger than average data sources. SAS customers also access and interpret unstructured internal and external data more often than any other vendor’s clients surveyed for this Magic Quadrant. * * SAS’s solution-oriented analytic application approach to the market is a differentiator, giving the company the advantage of having a wide variety of cross-functional and vertically specific analytic applications out of the box for a variety of industries, including financial services, life sciences and manufacturing. While others are also adopting this approach, SAS remains in the lead. Customers also report an above average sales experience. * * The primary drivers for customers choosing SAS remain functionality and data integration. In addition, references reported that they select SAS because of availability of skills. In the past, we have heard concerns over a lack of available SAS expertise; we suspect that this improvement is linked to the aggressive stance the company has taken to forge substantial partnerships with services firms, specifically Accenture. This broadened ecosystem also expands SAS’ sales channels with multiple partners positioning SAS-based solutions to their customers. * * On the software partnership front, SAS has partnered with a number of database vendors (such as Teradata) to push the execution of its models directly into the database management system without moving the data. Not only does this reduce data duplication and movement, it also allows SAS users to leverage the power and scalability features of the database to run predictive models against very large datasets with high performance. * Overall, SAS has a wide and loyal user base, many of whom have built careers around these products. References have a solid, positive outlook for SAS’s success within their organizations, as well as in the market as a whole. The company recently reported double-digit revenue growth for 2011. 2.2 Cautions * * References report that SAS is very difficult to implement — it was the No. 1 firm in this category. Companies also indicate that the product is considered difficult to use for business users (it was ranked No. 2 in this category). Its dashboard capabilities were rated lowest of all the vendors in this research. SAS is very much aware of these criticisms, and in 2011 embarked on a major development initiative involving hundreds of resources to improve usability and implementation activities. While it is too early to see the results of these efforts in surveys, we expect to see improvement in these areas in next year’s reference assessment. If no improvement is noted, this will directly impact SAS’s Ability to Execute scores for 2013. * * SAS’s dominance in predictive analytics and statistics continues to be challenged on many fronts. In addition to the SPSS suite, IBM also acquired Algorithmics in 2011 to bolster its portfolio; we are seeing greater adoption of open-source â€Å"R† in some products and embedded predictive and statistical capabilities in others. New entrants to the BI platform Magic Quadrant Prognoz and Alteryx accentuate these capabilities as core components of their product suites. While SAS still remains the acknowledged front runner, buyers have more options now, and SAS must continue to defend its franchise. The company recognizes this and, for example, has reinvigorated its emphasis on placing its software products in higher education settings for student and teacher use. * * Customer references report that cost is the most common factor blocking further adoption. In fact, verbatim responses to the survey mention cost in many ways — leasing terms, expensive to maintain, ongoing costs and so on — and, again, the company is very much aware of this criticism. With more options now available, SAS should also remain responsive to customers and prospects in these areas. The average tenure of SAS’s reference customers that participated in this survey was five years. Over 10% reported that they are planning to replace or are considering replacing the software in the next three years. Despite SAS’s success and awareness as a leader in the predictive analytics space, the company is still challenged to make it onto BI platform shortlist evaluations when predictive analytics is not a primary business requirement. While a little less than 60% of references indicated that SAS was their company’s BI standard, functionality used in traditional BI areas (reporting, dashboards, OLAP and so on) was lower than for other BI leaders in this report. Like last year, ad hoc query remains the one exception, with clients aggressively using SAS BI for that component. 3 IBM 3.1 Strengths * * IBM maintains its leading position on the Completeness of Vision axis for this year’s Magic Quadrant. The company takes a holistic approach to what it calls Business Analytics and Optimization (BAO), combining comprehensive software, hardware and services in a coordinated market offering. IBM’s business analytics software portfolio includes a unified BI, analytics and performance management platform, and is complemented by IBM information management software and appliances (Netezza, for example). Services are made up of a consulting line of nearly 9,000 people, which is a growing part of IBM Global Business Services (GBS). IBM can offer both a tools-based and/or a solution-driven offering, along with significant vertical expertise, to customers and prospects. * In 4Q10, IBM introduced its latest business analytics platform, IBM Cognos 10. Throughout 2011, additional capabilities have been released and customer adoption has begun in earnest. Cognos 10 references who responded to this year’s Magic Quadrant survey painted a very interesting snapshot — on average nearly 4,000 users, over 12 TB of data, broad functional use, and very high platform integration scores, all at or near the top of all ratings for all vendors in this report. Overall, Cognos 10 references were significantly more satisfied than Cognos 8 customers, who were the majority of IBM’s survey respondents. While some indicated that upgrading from Cognos 8 to Cognos 10 had some complexity, the majority rated it as straightforward or very straightforward. This bodes well for IBM’s future ability to execute, providing the firm delivers superior service and support and problem-free software. * * The average tenure of IBM respondents was seven years, second highest of all vendors in this survey. Gartner often hears this long-standing customer commitment in inquiry, and this represents a strong customer loyalty factor. This year, less than 7% of references noted that they are planning to discontinue use of the software in the next three years (or are considering doing so), which is significantly lower than last year’s result. * * Advanced analytics is a particular IBM strength. The company’s SPSS software continues to advance nicely, readily allowing IBM to bid for predictive analytics and statistical use cases. Customers rated IBM’s predictive capabilities in the top quartile of all vendors. A secret weapon at IBM’s disposal — IBM Research — delivers another level of research and development prowess to the overall IBM value proposition. For example, Watson, the Deep Question and Answer system that interprets natural language and scores possible answers based on probability, is a visible example of IBM Research at work. While not a part of the Cognos 10 platform, it demonstrates the depth and breadth that IBM can bring to clients’ advanced analytic scenarios. * The top reasons why customers select IBM are functionality, ease of use for end users, and data access and integration. IBM’s road map and future vision weighed heavily in reference decisions. In 2011, IBM delivered a new Cognos 10 mobile application for the iPad that is included free in existing user roles. In early 2012 the company will introduce Cognos Insight, a personal, desktop BI product that enables independent discovery and â€Å"what if† modeling, while also providing full interoperability with the larger workgroup and enterprise solutions. 3.2 Cautions * Twenty-three percent of Cognos 8 references indicate that performance continues to be problematic (a persistent problem for the last several years), nearly three times the average response for other vendors evaluated in this Magic Quadrant. In contrast, Cognos 10 references reported below average performance concerns. This is a sure signal that IBM must encourage upgrades to Cognos 10 without technical and/or financial disruption. * Again this year, references consider the Cognos products more difficult to implement and use than those of competitors. While Cognos 10 was rated slightly below average, other IBM products (Cognos 8, SPSS software and Cognos TM1) were deemed significantly more difficult. These are cited as two major reasons that limit expanded BI deployments with Cognos 8. As such, improved system administration and end-user usability were major development themes of the Cognos 10 release. References indicate that Cognos software is used largely by a consumer/casual user population. Reporting is the most extensively deployed component, followed by ad hoc query and OLAP analysis. * * IBM’s customers also continue to have less than optimal customer experiences, with support and sales interactions, along with product quality, rated in the bottom quartile of all vendors reviewed in this report. References also rate product functionality slightly below the average for all vendors. The bright spot is that Cognos 10 references rated product functionality near the top of all vendors, and support, sales and product quality were rated better than for Cognos 8. These issues remain IBM’s Achilles’ heel, and will limit its ability to raise execution scores next year unless action is taken quickly. * * License cost continues to be another source of customer concern across all products in the IBM business analytics portfolio. Gartner client inquiry also bears out this concern. Higher than expected costs to upgrade from Cognos 8 to Cognos 10 have stalled some projects, but changes in configuration, user roles, and/or support costs appear to drive the increase. As a counterpoint, existing Cognos 10 users did not identify license cost as a concern.