Een Business intelligence project of afdeling is gericht op het verzamelen en het analyseren van informatie over klanten, beslissingsprocessen, concurrentie, markttoestand en algemene economische, technologische en culturele trends. Deze data moet verwerkt, verrijkt en gepresenteerd worden om beslissingsondersteunende informatie en kennis (de intelligence) te verkrijgen.
Het doel van business intelligence is afgeleid van de strategie: het bedrijfsdoel of een mission statement. Deze doelen kunnen gebaseerd zijn op de korte termijn dan wel de lange termijn. Bijvoorbeeld het vergroten van een product-marktaandeel, marge/volume verbetering of het creëren van aandeelhouderswaarde kan een geschikte doel-definitie zijn.
Om te komen tot goede business intelligence moeten een aantal stappen doorlopen worden:
1>Gegevens verzamelen uit verschillende systemen en in een datawarehouse plaatsen.
2>De verzamelde gegevens omvormen zodat de gegevens uit de verschillende systemen met elkaar te vergelijken zijn en uniform zijn.
3>De verzamelde gegevens analyseren en omvormen tot informatie die bruikbaar is voor het management.
4>De gevonden informatie presenteren in een dashboard of andere presentatievorm. Eventueel kunnen de gevonden parameters afgezet worden tegenover de gewenste parameters, zoals in de balanced scorecard.
Er zijn innovatieve ontwikkelingen op het gebied van Business Intelligence icm de grote groeiende stroom aan data en nieuwe spelers op het gebied van Big Data, analyse en presentatie.
Een korte samenvatting van het rapport staat hieronder, het volledige document is terug te lezen op de site van Gartner. Bel of stuur een bericht per email voor contact over onze visie, uw vragen en eventuele uitdagingen waarbij we u zo mogelijk kunnen verder helpen. Een bericht per email bedrijfspunt[at]bedrijfspunt.nl of 033 4654637
Magic Quadrant for Business Intelligence and Analytics Platforms
Magic Quadrant for Business Intelligence and Analytics Platforms
Published by Gartner: 16 February 2017
The business intelligence and analytics platform market’s shift from IT-led reporting to modern business-led analytics is now mainstream.
Data and analytics leaders face countless choices: from traditional BI vendors that have closed feature gaps and innovated, to disrupters continuing to execute.
Strategic Planning Assumptions
By 2020, smart, governed, Hadoop/Spark-, search- and visual-based data discovery capabilities will converge into a single set of next-generation data discovery capabilities as components of modern BI and analytics platforms.
By 2021, the number of users of modern BI and analytics platforms that are differentiated by smart data discovery capabilities will grow at twice the rate of those that are not, and will deliver twice the business value.
By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms.
By 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be autogenerated.
By 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not.
Through 2020, the number of citizen data scientists will grow five times faster than the number of data scientists.
The Five Use Cases and 15 Critical Capabilities of a BI and Analytics Platform
We assess and define 15 product capabilities across five use cases as outlined below.
Vendors are assessed for their support of five main use cases:
- Agile Centralized BI Provisioning. Supports an agile IT-enabled workflow, from data to centrally delivered and managed analytic content, using the self-contained data management capabilities of the platform.
- Decentralized Analytics. Supports a workflow from data to self-service analytics. Includes analytics for individual business units and users.
- Governed Data Discovery. Supports a workflow from data to self-service analytics to SOR, IT-managed content with governance, reusability and promotability of user-generated content to certified SOR data and analytics content.
- OEM or Embedded BI. Supports a workflow from data to embedded BI content in a process or application.
- Extranet Deployment. Supports a workflow similar to agile centralized BI provisioning for the external customer or, in the public sector, citizen access to analytic content.
Vendors are assessed according to the following 15 critical capabilities. Changes, additions and deletions from last year’s critical capabilities are listed in Note 2. Subcriteria for each capability are listed in Note 3, and detailed functionality requirements are included in a published RFP document (see “Toolkit: BI and Analytics Platform RFP” ). How well the platforms of our Magic Quadrant vendors support these critical capabilities is explored in greater detail in “Critical Capabilities for Business Intelligence and Analytics Platforms.”
- BI Platform Administration, Security and Architecture. Capabilities that enable platform security, administering users, auditing platform access and utilization, optimizing performance and ensuring high availability and disaster recovery.
- Cloud BI. Platform-as-a-service and analytic-application-as-a-service capabilities for building, deploying and managing analytics and analytic applications in the cloud, based on data both in the cloud and on-premises.
- Data Source Connectivity and Ingestion. Capabilities that allow users to connect to structured and unstructured data contained within various types of storage platforms, both on-premises and in the cloud.
- Metadata Management. Tools for enabling users to leverage a common SOR semantic model and metadata. These should provide a robust and centralized way for administrators to search, capture, store, reuse and publish metadata objects such as dimensions, hierarchies, measures, performance metrics/key performance indicators (KPIs), and report layout objects, parameters and so on. Administrators should have the ability to promote a business-user-defined data mashup and metadata to the SOR metadata.
- Self-Contained Extraction, Transformation and Loading (ETL) and Data Storage. Platform capabilities for accessing, integrating, transforming and loading data into a self-contained performance engine, with the ability to index data and manage data loads and refresh scheduling.
- Self-Service Data Preparation. “Drag and drop” user-driven data combination of different sources, and the creation of analytic models such as user-defined measures, sets, groups and hierarchies. Advanced capabilities include machine-learning-enabled semantic autodiscovery, intelligent joins, intelligent profiling, hierarchy generation, data lineage and data blending on varied data sources, including multistructured data.
Analysis and Content Creation
- Embedded Advanced Analytics. Enables users to easily access advanced analytics capabilities that are self-contained within the platform itself or through the import and integration of externally developed models.
- Analytic Dashboards. The ability to create highly interactive dashboards and content with visual exploration and embedded advanced and geospatial analytics to be consumed by others.
- Interactive Visual Exploration. Enables the exploration of data via an array of visualization options that go beyond those of basic pie, bar and line charts to include heat and tree maps, geographic maps, scatter plots and other special-purpose visuals. These tools enable users to analyze and manipulate the data by interacting directly with a visual representation of it to display as percentages, bins and groups.
- Smart Data Discovery: Automatically finds, visualizes and narrates important findings such as correlations, exceptions, clusters, links and predictions in data that are relevant to users without requiring them to build models or write algorithms. Users explore data via visualizations, natural-language-generated narration, search and NLQ technologies.
- Mobile Exploration and Authoring. Enables organizations to develop and deliver content to mobile devices in a publishing and/or interactive mode, and takes advantage of mobile devices’ native capabilities, such as touchscreen, camera and location awareness.
Sharing of Findings
- Embedding Analytic Content. Capabilities including a software developer’s kit with APIs and support for open standards for creating and modifying analytic content, visualizations and applications, embedding them into a business process and/or an application or portal. These capabilities can reside outside the application, reusing the analytic infrastructure, but must be easily and seamlessly accessible from inside the application without forcing users to switch between systems. The capabilities for integrating BI and analytics with the application architecture will enable users to choose where in the business process the analytics should be embedded.
- Publish, Share and Collaborate on Analytic Content. Capabilities that allow users to publish, deploy and operationalize analytic content through various output types and distribution methods, with support for content search, scheduling and alerts. Enables users to share, discuss and track information, analysis, analytic content and decisions via discussion threads, chat and annotations.
Overall platform capabilities were also assessed:
- Platform Capabilities and Workflow. This capability considers the degree to which capabilities are offered in a single, seamless product or across multiple products with little integration.
- Ease of Use and Visual Appeal. Ease of use to administer and deploy the platform, create content, consume and interact with content, as well as the visual appeal.