Posted by Noel Shannon on Thu, Mar 03, 2011 @ 10:20 AM
BI platforms enable users to build applications that help organizations learn and understand their business. Gartner defines a BI platform as a software platform that delivers the 13 capabilities listed below. These capabilities are organized into three categories of functionality: integration, information delivery and analysis. Information delivery is the core focus of most BI projects today, but we are seeing an increase in interest in deployments of analysis to discover new insights, and in integration to implement those insights.
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BI infrastructure — All tools in the platform should use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel.
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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.
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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, for 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.
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Collaboration — This capability enables BI users to share and discuss information and/or manage hierarchies and metrics via discussion threads, chat and annotations, either embedded in the BI platform or through integration with collaboration, analytical master data management (MDM) and social software.
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Reporting — Reporting provides the ability to create formatted and interactive reports (parameterized) with highly scalable distribution and scheduling capabilities.
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Dashboards — This subset of reporting includes the ability to publish formal, Web-based 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.
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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.
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Microsoft Office integration — In some 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
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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 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.
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OLAP — This enables end users to analyze data with extremely fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are (often) able to easily navigate multidimensional drill paths.
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Interactive visualization — This gives 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.
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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.
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Scorecards — These take the metrics displayed in a dashboard a step further by applying them to a strategy map that aligns KPIs with a strategic objective.
To find out more about the Gartner Magic Quadrant, contact us at info@qlikpower.com
Posted by Conor Hanley on Mon, Oct 11, 2010 @ 07:16 AM
Traditional online analytical processing (OLAP) uses queries against pre-aggregated data for decision support. Many variations of OLAP exist. Some are flexible and others are high-performance. But by their very nature, most query-based tools divorce data from their context, leaving gaps for people who are trying to make data-driven business decisions.
ROLAP, MOLAP, and HOLAP all have shortcomings
The ubiquity of structured query language (SQL) creates a blind spot to the shortcomings of using queries — whether SQL, multidimensional query expressions, or otherwise — as the fundamental component of a decision support engine.
- ROLAP extracts data in real-time as it is needed, making it flexible. The oldest form of OLAP decision support is relational online analytical processing (ROLAP). ROLAP is still prevalent today. It uses SQL or other query technology to extract and calculate data aggregates in real time as the user needs them. Once thought of as slow and unresponsive, today ROLAP is enjoying something of a renaissance with the more scalable decision support database architectures. ROLAP can be flexible, without requiring predefined dimensionality, but is computationally intensive and can therefore be slow. And because ROLAP is query-based, it is unable to maintain associations.
- MOLAP pre-aggregates data, making it fast. The next generation of technology for decision support came in the form of multidimensional online analytical processing (MOLAP), also known as cube-based OLAP. The main difference between ROLAP and MOLAP is that with MOLAP the query results are aggregated in advance while for ROLAP they are aggregated as needed. With MOLAP, data is pre-aggregated for multiple permutations of data points along preselected dimensions. This approach provides near-instantaneous access to aggregates as long as the question the business user has in mind lies within the predefined dimensionality. Because the aggregates are pre-calculated, MOLAP can be faster than ROLAP. However, with this speed comes a loss of flexibility. And again, because MOLAP is query-based it cannot maintain associations.
- HOLAP offsets some ROLAP and MOLAP weaknesses. The relative strengths and weaknesses of ROLAP and MOLAP led to the creation of a third technology: hybrid online analytical processing (HOLAP). HOLAP is any architecture that leverages both ROLAP and MOLAP in an attempt to offset the relative weaknesses of each. Because HOLAP is the product of the marriage of two query-based technologies, it is also a fundamentally a query-based technology. And — you guessed it — it does not maintain associations in the data.
QlikView is different
In contrast, QlikView is flexible, fast, and maintains associations among all data elements. QlikView offers the flexibility of ROLAP (no predefined dimensionality) with the speed of MOLAP (near-instantaneous access to aggregates). While MOLAP tools sometimes have drill-through capabilities (in essence, a multidimensional engine with on-demand relational queries), QlikView is just the opposite: a relational engine with on-demand cubes. QlikView manages associations among data sets at the engine level, not the application level. QlikView stores individual tables in its in-memory associative engine. Every data point in every field is associated with every other data point anywhere in the entire schema. Datasets can be hundreds of tables with thousands of fields. When users look at two different data points they know precisely how the points relate to each other. They are not restricted to seeing the effect upon just a set of query results. Any and all aggregates are recalculated in real time, regardless of the source fields.
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