For many years now I have been heading a company that is one of the leaders in the implementation of BI systems in Russia and is regularly included in the top lists of analysts in terms of business volume in the field of BI. During my work, I participated in the implementation of BI systems in companies from various sectors of the economy - from retail and manufacturing to the sports industry. Therefore, I am well aware of the needs of customers of business intelligence solutions.
The solutions of foreign vendors are well known, most of them have a strong brand, their prospects are analyzed by large analytical agencies, while domestic BI systems for the most part still remain niche products. This seriously complicates the choice for those looking for a solution to meet their needs.
To eliminate this drawback, a team of like-minded people and I decided to make a review of BI systems created by Russian developers - βGromovβs BI circleβ. We analyzed most of the domestic solutions on the market and tried to highlight their strengths and weaknesses. In turn, thanks to it, the developers of the systems included in the review will be able to look at the pros and cons of their products from the outside and, possibly, make adjustments to their development strategy.
This is the first experience of creating such a review of Russian BI systems, so we focused specifically on collecting information about domestic systems.
The review of Russian BI systems is being conducted for the first time; its main task is not so much to identify leaders and outsiders, but to collect the most complete and reliable information about the possibilities of solutions.
The following solutions took part in the review: Visiology, Alpha BI, Foresight.Analytical platform, Modus BI, Polymatica, Loginom, Luxms BI, Yandex.DataLens, Krista BI, BIPLANE24, N3.ANALYTICS, QuBeQu, BoardMaps OJSC Dashboard Systems, Slemma BI , KPI Suite, Malahit: BI, Naumen BI, MAYAK BI, IQPLATFORM, A-KUB, NextBI, RTAnalytics, Simpl.Data management platform, DATAMONITOR, Galaxy BI, Etton Platform, BI Module
To analyze the functionality and architectural features of Russian BI platforms, we used both internal data provided by the developers and open sources of information - solution sites, advertising and technical materials from suppliers.
Analysts, based on their own experience in implementing BI systems and the basic needs of Russian companies for BI functionality, have identified a number of parameters that allow them to see the similarities and differences of solutions, and subsequently highlight their strengths and weaknesses.
These are the parameters
Administration, security, and BI platform architecture β in this category, the presence of a detailed description of the capabilities that ensure the security of the platform, as well as functionality for user administration and access auditing, was assessed. The total amount of information about the platform architecture was also taken into account.
Cloud BI β this criterion allows you to evaluate the availability of connectivity using the Platform as a Service and Analytic Application as a Service model for creating, deploying and managing analytical and analytical applications in the cloud based on data both in the cloud and on-premises.
Connecting to the source and receiving data β The criterion takes into account the capabilities that allow users to connect to structured and unstructured data contained in different types of storage platforms (relational and non-relational) - both local and cloud.
Metadata Management β takes into account the presence of a description of tools that allow the use of a common semantic model and metadata. They should provide administrators with a reliable and centralized way to find, capture, store, reuse, and publish metadata objects such as dimensions, hierarchies, measures, performance metrics, or key performance indicators (KPIs), and can also be used to report on layout objects , parameters, etc. The functional criterion also takes into account the ability of administrators to promote data and metadata defined by business users into SOR metadata.
Data storage and loading β This criterion allows you to evaluate the platform's capabilities for accessing, integrating, transforming and loading data into an autonomous performance engine with the ability to index data, manage data loading and update schedules. The availability of functionality for extranet deployment is also considered: does the platform support a workflow similar to flexible centralized BI provisioning for an external client or citizen access to analytical content in the public sector.
Data preparation β the criterion takes into account the availability of functionality for βdrag and dropβ user-controlled combinations of data from different sources and the creation of analytical models such as user-defined measures, sets, groups and hierarchies. Advanced capabilities under this criterion include semantic auto-discovery capabilities with support for machine learning, intelligent aggregation and profiling, hierarchy generation, distribution and blending of data across multiple sources, including multi-structured data.
Scalability and complexity of the data model β The parameter evaluates the presence and completeness of information about the on-chip memory mechanism or architecture in the database, due to which large volumes of data are processed, complex data models are processed and performance is optimized and deployed to a large number of users.
Advanced Analytics β Evaluated the availability of functionality that allows users to easily access advanced offline analytics capabilities through menu-based options or by importing and integrating externally developed models.
Analytical dashboards β this criterion takes into account the presence of a description of the functionality for creating interactive information panels and content with visual research and built-in advanced and geospatial analytics, including for use by other users.
Interactive visual exploration β Evaluates the completeness of the data exploration functionality using a variety of visualization options that go beyond basic pie and line charts, including heat and tree maps, geographic maps, scatter plots and other specialized visualizations. Also taken into account is the ability to analyze and manipulate data by directly interacting with its visual representation, displaying it as percentages and groups.
Advanced Data Discovery β This criterion assessed the presence of functionality to automatically find, visualize and communicate important definitions such as correlations, exceptions, clusters, links and predictions in data that are relevant to users, without requiring them to build models or write algorithms. It also considered the availability of information about opportunities to explore data using visualizations, storytelling, search, and natural language query (NLQ) technologies.
Functionality on mobile devices β this criterion takes into account the availability of functionality for developing and delivering content to mobile devices for the purpose of publishing or studying online. Data on the use of native mobile device capabilities such as touchscreen, camera and location is also assessed.
Embedding Analytical Content β this criterion takes into account the availability of information about the set of software developers with API interfaces and support for open standards for creating and modifying analytical content, visualizations and applications, integrating them into a business process, application or portal. These capabilities can reside outside the application, reusing the analytics infrastructure, but should be easily and seamlessly accessible from within the application without forcing users to switch between systems. This parameter also takes into account the availability of analytics and BI integration capabilities with the application architecture, which allow users to choose where analytics should be embedded in the business process.
Analytic Content Publishing and Collaboration β This criterion considers capabilities that enable users to publish, deploy, and consume analytic content through a variety of output types and distribution methods, with support for content discovery, scheduling, and alerting.
Ease of use, visual appeal and workflow integration β this parameter summarizes the availability of information about the ease of administration and deployment of the platform, content creation, use and interaction with content, as well as the degree of attractiveness of the product. Also considered is the extent to which these capabilities are offered in one seamless product and workflow, or across multiple products with little integration.
Presence in the information space, PR β the criterion evaluates the availability of information about the release of new versions and implemented projects in open sources - in the media, as well as in the news section on the product or developerβs website.
Source: habr.com