Analytical review: BI in Russia 2009
Analysts of the TAdviser center completed preparation of the open review of the market of the platforms for the business analysis (BI) presented on Russian market. On this page you can read the most interesting sections of the review.
Benefits of using a BI system
Systems for business analysis solve a very wide range of tasks. Thus, the “near horizon” is monitoring, analysis and adjustment of operational goals:
support for the development of business processes and structural changes of the enterprise;
the possibility of modeling various business situations in a single information environment;
conducting operational analysis on non-standard requests;
reducing the routine workload on staff and freeing up time for deeper analytical work;
stable operation with an increase in the volume of processed information, the possibility of scaling.
In terms of supporting the strategic development of an enterprise, BI systems provide:
evaluation of the effectiveness of various business areas;
assessment of the feasibility of the goals set;
assessment of the efficiency of resource use, including by subsidiaries;
assessment of the effectiveness of operating, investment and financial activities;
business modeling and evaluation of investment projects;
cost management, tax planning, capital investment planning.
To date, according to experts from Gartner, only 15-20% of business users are actively working with BI applications, while the rest consider systems for business analysis too complicated to use. However, the active development of tools for interactive data visualization and the further spread of Internet technologies should soon improve the situation.
According to analysts from MiPro Consulting, the introduction of an independent BI system in an organization provides a number of advantages over using analytical tools built into other corporate information systems. Some of the benefits of a BI system include:
greater visibility and convenience of working with information for business users, including top management;
the ability to use several analytical solutions for different areas of activity on an enterprise-wide scale, and not within individual departments;
allows you to extract, analyze and consolidate data from virtually any source;
is based on an industrial, supported and developed BI platform;
has the status of an independent, strategic, business-critical application;
provides the necessary scalability, efficiency, performance;
allows you to build and maintain end-to-end procedures and processing processes, unified centralized analytical models and projects throughout the organization;
contains built-in tools for solving various and diverse analytical tasks, both from a business and IT point of view;
provides access to data and analytical tools to more users.
The use of analytical tools built into other corporate information systems, for example, the ERP or CRM class, as a rule, has the following limitations:
a limited set of implemented analytical tools that are the same for all users, regardless of their roles and tasks;
the ability to use only your own, internal data for analysis, while information from other systems remains inaccessible, and data from various sources cannot be consolidated;
the lack of developed built-in tools for analysis leads to the fact that the system is used only to extract the data stored in it, which are then exported and analyzed in Excel;
ERP and CRM systems, as a rule, have a limited number of users, which “cuts off” a large number of company employees from analytics who would find this information useful and interesting (a significant increase in the number of users reduces the performance of transactional systems);
transactional systems usually do not contain all the indicators necessary for analysis, do not include tools such as dashboards, which have already become the standard for presenting analytical information;
the results of analysis in such systems are usually presented in the form of tabular reports or diagrams, which does not allow obtaining a detailed and comprehensive idea of the real state of affairs and does not answer many questions that arise;
the ability to create flexible custom (ad-hoc) requests is limited;
the use of large volumes of accumulated historical information is limited.
When choosing or upgrading a system for business analysis, you should consider ways to store and integrate data, visualization and analytics tools.
Data storage
If a company is faced with the task of identifying long-term or periodic trends, that is, users need to analyze historical data coming from various departments over the past 3-5 years, then most likely they should think carefully about organizing ETL operations to load data into data warehouses.
If a company or any of its departments needs to analyze information on a monthly or weekly basis, then the best solution would be to allocate and organize for these purposes (for each of the departments or for solving specific problems) separate data marts, also using ETL tools.
If the company plans to analyze operational data in a near real-time mode (that is, updated several times during the day), then it may be necessary to abandon the organization of the data warehouse and pay attention to the development of integration tools based on the intermediate virtual metadata layer with elaboration appropriate interfaces and algorithms (according to the EII principle).
Data integration
As noted above, if the purpose of implementing a BI system is to solve individual, specific tasks, then it is advisable to limit ourselves to organizing data marts. In this case, the use of any separate integration algorithms is not required.
If, on the contrary, BI is implemented in order to obtain a single, holistic view of the overall state of the business, then it is probably impossible to do without creating a centralized data warehouse and, accordingly, introducing the necessary ETL tools. In addition, in order to obtain a truly adequate picture of the business, it is necessary to pay special attention to ensuring the high quality of the analyzed data, and this will require the introduction of an expanded set of tools for their "cleaning" - identifying incomplete or erroneous data, duplicate information, bringing data from various sources to a single format.
If the company focuses on the study of operational data, then you should consider means of replication and access.
Visualization and analytics
Depending on the tasks set, as well as on the qualifications of users, data visualization tools are also selected - control panels, scorecards, reports, OLAP cubes.
For experienced, qualified users, OLAP cubes will be the best tool, which will allow them to conduct deep and detailed business analysis, with the required degree of detail.
Users who, in their daily activities, are faced with the need to adopt management decisions, as well as business performance analysis, are interested in organizing a workplace in the form of a control panel, which displays the state of the business as a whole in the form of visual scales and indicators, with the ability to switch between individual areas of activity.
Ordinary managers need funds to solve their current tasks, control the progress of certain types operations, as well as to control the activities of their employees (each individual employee and the team as a whole). In addition, in order to organize clear interaction with related departments (or regions), it is necessary to be able to get an idea of the progress of the implementation of interrelated tasks.
Vertical or horizontal solution
There are both horizontal BI solutions on the market that implement a set of generally applicable tools, as well as specialized vertical solutions “tailored” for specific industries or tasks. Both of them have their advantages and disadvantages.
The advantage of horizontal solutions can be seen as their ability to grow with the organization. Such solutions are usually scalable and can cover all lines of business and all departments. big company and are also easier to change. The downside of this breadth of possibilities is the need for a longer and more thorough customization of solutions, adaptation to specific requirements. Implementation projects are becoming more costly and the demands on IT professionals are becoming higher.
Vertical solutions, for their part, do not require a separate lengthy and laborious setup to solve specific problems and to meet the requirements of industry regulatory organizations (financial, medical, etc.). However, it may turn out that different departments within the same structure will not be able to use a single solution, and it may be necessary to master and integrate several different systems for business analysis.
Those organizations that now and in the future plan to engage in their specific activities that require compliance with certain strict regulations are likely to benefit from the implementation of vertical solutions. If there is no confidence in such a commitment to a certain type of activity in the future, and it is likely that the company's specialization will expand significantly, then choosing a vertical BI solution is a certain risk.
BI systems are analytical systems designed for business analysis that are able to combine data from completely different sources of information. Data software systems process information and provide a report in a convenient interface for detailed study and subsequent evaluation of the information obtained in the process.
The obtained reporting data and its optimal use help in achieving the set business goals. Data analysis in a complex is the acquisition of knowledge, a kind of squeeze from a mass of sources, including the direction of the business, which can significantly increase the efficiency of the process and significantly reduce costs.
BI systems are a single, extremely transparent and complete source of all data about the company's business for its administrative resource, but mainly for management.
Today, reporting generation and competent analysis are no longer a luxury, but rather a necessity for companies; reporting documentation is required both within a business and in every constituent element of the entire process.
The solutions provided by the BI system are optimal for the preparation of all reporting, including covering all aspects of the business without exception, the presence of such capabilities is already considered mandatory and is considered, together with other basic technologies, as a corporate standard.
- BI tools. These tools are divided into query and report generators, BI analytical processing tools, corporate BI platforms and BI suites. The bulk of BI tools consists of enterprise BI suites and BI platforms. The tools provided for generating queries and reports are mostly being absorbed, or corporate BI suites are replacing them. OLAP engines - online analytical data processing or servers, including relational ones. OLAP engines are the infrastructure for BI platforms and BI tools. Most of the tools are used by users for access as well as analysis, including the generation of reports, which in most cases are located in warehouses, a data mart or an operational warehouse for data.
- BI applications. Applications that are not considered as tools. An example is EIS, an executive information system.
Characteristic features of BI systems
- The systems use portal technologies that provide a single entry point to the Internet and the information space of enterprises.
- The interface is presented in the form of a control panel or dashboard displaying several key indicators. This makes it possible to quickly assess the situation. It also provides the ability to quickly access key indicators by departments and divisions, they are stored in a separate folder located on the dashboard.
- Layered: All data is displayed in multiple layers, with each subsequent layer presenting more and more detailed information about indicators, events or processes.
- The interactivity of BI systems, which allows the user to quickly navigate, including viewing data in various sections and sections, as well as “drilling” data, moving through various kinds of measurements. Users can directly perform operations on data.
- manageability and relevance. Proactivity, which contains a rule engine that allows users to define targets and thresholds for various indicators and determine at what data values an alert should be issued. The system provides the ability to set parameters or indicators: if they reach critical values, alarm signals are displayed on the monitor - visual and / or sound.
- Customization of BI-systems - individual configuration of the remote control or dashboard for the control level and user role. Personalization allows the user to independently select objects from authorized lists and arrange data on the dashboard according to their importance.
- Flexible access allows users to intuitively access the data and reports they need from a huge range of results reports and graphs, including remote access and mobile apps.
- Collaboration involves the simultaneous collaboration of a large group of employees, including viewing reports.
Magic quadrants
Competently assessing the state of the modern market, as well as giving an exhaustive objective description of its main players, is a rather non-trivial task. There are many manufacturers on the market, which differ from each other in the size of their business, organizational structures, management style, strategy and other factors.
This state of affairs greatly complicates the process of comparing them, and the direction of movement and development of the market is extremely ambiguous and difficult to predict. To solve this problem, a "magic quadrant" of BI systems was developed, in which 2 indicators are used, one of them is the completeness of vision. The other is the ability to realize.
24.04.2003 Valery Artemiev
The term “business intelligence” has existed for a relatively long time, although it is little used in our country due to the lack of an adequate translation and clear understanding, which, however, is also typical for the West. Let's try to understand its essence.
In Russian, the word "intelligence" is unambiguously understood as the mental ability of a person. At first glance, a good translation for the term business intelligence proposed in "data mining", but the question immediately arises whether there is "non-data mining".
The ambiguity of the term under discussion was influenced by the ambiguity of the English word "intelligence":
- the ability to recognize and understand; willingness to understand;
- knowledge transferred or acquired through training, research or experience;
- action or state in the process of cognition;
- intelligence, intelligence data.
In Russian, the word "intelligence" is unambiguously understood as the mental ability of a person. At first glance, a good translation for the term Business intelligence is proposed in “data mining”, but the question immediately arises whether there is “non-mining data analysis”. The ways of the language are inscrutable, so we will use both the original in English and the “business intelligence” tracing paper.
Various definitions
The term "business intelligence" was first coined by Gartner analysts in the late 1980s as "a user-centric process that includes information access and exploration, analysis, intuition and understanding that lead to improved and informal decision making." Later in 1996, a clarification appeared - "tools for analyzing data, building reports and queries can help business users navigate the sea of \u200b\u200bdata in order to synthesize meaningful information from them - today these tools collectively fall into a category called business intelligence ( business intelligence).
BI as methods, technologies, means of extracting and representing knowledge
According to original definitions, BI is the process of analyzing information, generating intuition and understanding for improved and informal decision making by business users, and tools for extracting business-relevant information from data. It should be noted that most definitions interpret "business intelligence" as a process, technologies, methods and means of extracting and representing knowledge.
BI, EIS, DSS, eBusiness and Commerce
Over the past 10 years, the names and content of information and analytical systems have changed from information systems manager (executive information systems, EIS) to decision support systems (DSS) and now to business intelligence systems.
In the days of mainframes and minicomputers, when most users did not have direct access to computers, organizations depended on their IT departments to provide standard and parametric reports. But in order to get reports other than the standard ones, users had to order their development and wait for several days or weeks.
The EIS applications were customized to the needs of executives and managers and provided the ability to obtain basic aggregated information about the state of their business in the form of tables or charts. Usually they included scheduled requests with a set of parameters. Such packages were usually developed by their own IT departments. For getting additional information and further analysis, other applications were used or SQL queries or reports were created on demand.
The first generation DSS applications were application packages that dynamically generated SQL scripts based on the type of information requested by the user. They allowed analysts to get information from relational databases without requiring knowledge of SQL. Unlike EIS, DSS applications can answer a wide range of business questions, have multiple reporting options, and certain formatting options. However, the flexibility of such packages was still limited due to the focus on a specific set of tasks.
With the advent of PCs and local area networks, the next generation of DSS applications is built on the basis of BI and allows a non-programmer user to easily and quickly extract information from various sources, generate their own customized reports or graphical representations, and conduct multidimensional data analysis. The development of business intelligence systems has gone from "fat" clients to Web applications in which the user conducts research using a browser and can work remotely. You can also create what-if scenarios and collectively view and update information.
Although users of enterprise BI information have traditionally been located within the enterprise, with the spread of the Web for e-business, B2B, CRM, and SCM, BI users may be external to the enterprise, and in B2C, C2B and trading floors BI users are Internet users.
BI and data warehouses
The concept, methods and tools of the data warehouse (Data warehousing) define approaches and provide integration, cleaning, retrospective storage of information intended for analysis, answer the question "How to prepare information for analysis?". Business intelligence technology defines methods and means of accessing and real-time analysis of information in terms of subject area. BI tools do not have to work in the data warehouse infrastructure, but in this case, the problem of data cleaning and reconciliation is assigned to them, and these operations will have to be performed on the fly or previously, but for a separate information resource. In addition, there is an impact on the performance and reliability of the online transaction processing system. That is why it is good corporate practice to separate the transactional and analytical components and apply for the second various solutions by data warehouse. The main joints are not only at the level of information, but also at the level of metadata. In the case of a data warehouse, metadata can be centrally managed.
It should be noted that often the term "data warehouse" refers to a DSS decision support system or an information and analytical system based on data warehouse and business intelligence technologies.
Classification of business intelligence products
Today's BI product categories include: BI tools and BI applications. The former, in turn, are divided into: query and report generators; advanced BI tools, primarily online analytical processing (OLAP) tools; corporate BI suites (enterprise BI suites, EBIS); BI platforms. The main part of BI tools is divided into corporate BI suites and BI platforms. Query and reporting tools are being largely absorbed and replaced by enterprise BI suites. Multidimensional OLAP engines or servers and relational OLAP engines are BI tools and infrastructure for BI platforms. Most BI tools are used by end users to access, analyze, and report on data that is most often located in data warehouses, data marts, or operational data warehouses. Application developers use BI platforms to create and deploy BI applications that are not considered BI tools. An example of a BI application is the EIS executive information system.
Query and Report Generation Tools
Query and report generators are typically "desktop" tools that provide users with access to databases, perform some analysis, and generate reports. Requests can be either unscheduled (ad hoc) or routine in nature. There are reporting systems (usually server-based) that support routine queries and reports. The desktop query and report generators are also enhanced with some lightweight OLAP features. The developed tools of this category combine the capabilities of batch generation of routine reports and desktop query generators, distribution of reports and their operational updates, forming the so-called corporate reporting (corporate reporting) . Its arsenal includes a report server, distribution tools, publishing reports on the Web, a mechanism for notifying events or deviations (alerts). Characteristic representatives are Crystal Reports, Cognos Impromptu and Actuate e.Reporting Suite.
OLAP or advanced analytical tools
OLAP tools are analytical tools that were originally based on multidimensional databases (MDBs).
MDBs are databases designed specifically to support the analysis of quantitative data with multiple dimensions, containing data in a "purely" multidimensional form. Most applications include the dimension of time, other dimensions may be geography, organizational units, customers, products, etc. OLAP allows you to organize dimensions in a hierarchy. The data is presented in the form of hypercubes (cubes) - logical and physical models of indicators that collectively use dimensions, as well as hierarchies in these dimensions. Some data is pre-aggregated in the database, others are calculated on the fly.
OLAP tools allow you to explore data across multiple dimensions. Users can choose which metrics to analyze, which dimensions and how to display in the crosstab, swap rows and columns "pivoting", then slice and dice to focus on a specific combination of dimensions. You can change the detail of the data by moving through the levels using drill down/roll up drill down and drill down, as well as drill across across other dimensions.
To support the MDB, OLAP servers are used that are optimized for multidimensional analysis and come with analytical capabilities. They provide good performance, but usually take a long time to load and expand the MDB. Comes with reach-through capability, allowing you to move from aggregates to details in relational databases. Classic OLAP server - Hyperion Essbase Server.
Today, relational DBMSs are used to emulate MDBs and support multivariate analysis. OLAP for relational databases (ROLAP) has the advantage of scalability and flexibility, but loses performance to multidimensional OLAP (MOLAP), although there are methods to improve performance, such as the star schema. Although MDBs are still the most suitable for online analytical processing, this capability is now being built into or extended by relational DBMSs (for example, MS Analysis Services or ORACLE OLAP Services is not the same as ROLAP). There is also hybrid online analytical processing (HOLAP) for hybrid products that can store multidimensional data natively as well as relationally. MDBs are accessed through APIs for generating multidimensional queries, while relational databases are accessed through SQL queries. An example of a ROLAP server is the Microstrategy7i Server.
Desktop OLAP tools (eg BusinessObjects Explorer, Cognos PowerPlay, MS Data Analyzer) now built into EBIS make it easy for end users to view and manipulate multidimensional data that can come from ROLAP or MOLAP data backend resources. Some of these products have the ability to download cubes so that they can work offline. As part of EBIS, these desktop tools are equipped with server-side processing capabilities that go beyond their traditional capabilities, but do not compete with MOLAP tools. Desktop tools, compared to MOLAP tools, have little performance and analytical power. Often an interface is provided through Excel, such as MS Excel2000/OLAP PTS, BusinessQuery for Excel. Almost all OLAP tools have Web extensions (Business Objects WebIntelligence for example), for some they are basic.
Enterprise BI suites
EBIS is a natural way to deliver BI tools that were previously delivered as disparate products. These kits are integrated into query, report, and OLAP toolkits. Enterprise BI suites should be scalable and extend beyond internal users to key customers, vendors, and others. BI suite products should help administrators implement and manage BI without adding new resources. Due to the close relationship between the Web and enterprise BI suites, some vendors describe their BI suites as BI portals. These portal offerings provide a subset of EBIS capabilities through a Web browser, but vendors are constantly increasing their functionality to bring it closer to the capabilities of thick client tools. Typical EBIS are provided by Business Objects and Cognos.
BI platforms
BI platforms offer a set of tools for creating, implementing, supporting and maintaining BI applications. There are data-rich applications with "custom" end-user interfaces, organized around specific business problems, with targeted analysis and models. BI platforms, although not as fast growing and widely used as EBIS, are an important segment due to the expected and ongoing growth of BI applications. With relational database vendors creating OLAP extensions to their databases, many platform vendors that provided multidimensional databases for OLAP were forced to migrate to BI applications in order to survive. The DBMS product families that provide BI capabilities are really pushing the growth of the BI platform market. This is partly due to the increased activity of a number of DBMS vendors. Looking at the various tools, we see that EBIS are highly functional tools, but they are not as important as BI platforms or custom BI applications. On the other hand, BI platforms are usually not as functionally complete as corporate BI suites. When choosing BI platforms, the following characteristics should be considered: modularity, distributed architecture, support for XML standards, OLE DB for OLAP, LDAP, CORBA, COM/DCOM, and web provisioning. They should also provide functionality specific to business intelligence, such as database access (SQL), multidimensional data manipulation, modeling functions, statistical analysis, and business graphics. This category of products is represented by Microsoft, SAS Institute, ORACLE, SAP and others.
BI applications
Business intelligence applications often have built-in BI tools (OLAP, query and report generators, modeling tools, statistical analysis, visualization, and data mining). Many BI applications extract data from ERP applications. BI applications are usually focused on a specific function of an organization or task, such as sales analysis and forecasting, financial budgeting, forecasting, risk analysis, trend analysis, "churn analysis" in telecommunications, etc. They can also be applied more broadly, as in the case of enterprise performance management applications or system balanced scorecard(balanced scorecard).
Data Intelligence
Data mining is the process of discovering correlations, trends, patterns, relationships, and categories. It is performed by rigorous data mining using pattern recognition technologies, as well as statistical and mathematical methods. Data reconnaissance is performed repeatedly various operations and transformations on raw data (feature selection, stratification, clustering, visualization and regression), which are designed to: 1) find representations that are intuitive for people who, in turn, better understand the business processes underlying them activities; 2) to find models that can predict the outcome or meaning of certain situations using historical or subjective data.
Unlike the use of OLAP, data intelligence is much less user-driven, instead relying on specialized algorithms that correlate information and help recognize important (and previously unknown) trends, free from user bias and assumptions.
Other BI methods and tools
In addition to the listed tools, BI may include the following analysis tools: statistical analysis packages and time series analysis and risk assessment; modeling tools; packages for neural networks; fuzzy logic tools and expert systems.
Additionally, it should be noted the means for graphic design of the results: means of business and scientific and technical graphics; "dashboards", means of analytical cartography and topological maps; means of visualization of multidimensional data.
business intelligence architecture
An enterprise BI architecture should be developed after the user's BI needs have been identified, but before the choice of BI tools. The Business Intelligence architecture defines the components of BI information delivery and BI technology components (Fig. 1). Once the usage profiles of BI information have been defined, an information delivery architecture can be designed based on these profiles and the type of implementation required. This can be any mixture of network-connected desktop clients, desktop clients and server, web-based thin clients, and other mobile computing devices. The information delivery architecture will define user interfaces, which are often personalized portals.
Fig.1. Business intelligence architecture
The BI technology architecture defines the infrastructure and components needed to support the implementation, operation, and administration of BI tools and applications, and the interconnection of these components. A solid BI technology architecture will consist of two important layers: infrastructure and application services (or functionality). The infrastructure layer includes informational resources, administration and networks. At this layer, data is collected, integrated and made available. The data warehouse is one of the possible components of the infrastructure layer. Using BI in operational systems may require an operational data store (ODS), possibly linked to corporate workflow structures. Application services include all BI services such as query, analysis, reporting, and visualization engines, as well as security and metadata.
Storage environment and access to BI information
In addition to traditional Oracle9i and MS SQL Server2000 data warehousing solutions, ERP warehousing applications are on the rise, such as SAP BW for R/3, or PeopleSoft Enterprise Warehouse with Enterprise Performance Management BI applications. However, in both cases, the functionality is tied to specific ERP systems, and therefore limited.
The use of ROLAP for storing BI information is growing rapidly, due to the convenience of relational DBMS for applications with very large detailed databases and due to the inclusion of OLAP capabilities in DBMS. The use of MDB and OLAP remains unchanged and is the most predominant, since they provide better performance and functionality where aggregated data and complex analytical calculations are important.
It is not surprising that with the high cost of two-tier client-server structures, access to BI is increasingly via the Web. The focus shifts to the server, reflecting the fact that access to corporate BI information is an important element, while standalone PCs are clearly not functional enough. Popular and growing delivery of BI reports e-mail, and mobile and wireless delivery methods are still slow to spread.
metadata
Most BI tools on the market use a metadata layer or repository. Business metadata includes definitions of data that are stored in data sources, in terms of the subject area. They may also contain rules and calculations that must be defined for that business. In addition, there are technical metadata for accessing physical data. CASE-tools, relational DBMS, tools for extracting, transforming and loading data use metadata. When creating data warehouses and data marts, it is often possible to automatically retrieve metadata from data sources, but sometimes users must retrieve the metadata themselves. Thus, a complex situation with several repositories existing in the same organization is possible. The lack of common metadata for tools - due to the lack of standards for metadata - is a major problem for IT departments.
Pros and cons of technology
The user's ability to conduct multi-aspect operational analysis of information in terms of the subject area to support business decision-making is rapidly expanding. The parallel movement from information anarchy or dictatorship to information democracy is expanding the contingent of business intelligence users. The need for flexible access to corporate data comes to the fore, and not just the need to solve a specific functional task. There is less direct dependence on IT departments to produce custom reports or queries. It is possible to move from static regulatory reports to a “live report”, and the most advanced analysts get the opportunity to conduct cross-thematic analysis and build summary reports from scratch, having a semantic layer that describes all indicators and sections of corporate information. The same tools can be used by programmers to quickly create routine, parametric reports. Web access to BI (both static and dynamic content) will provide a real corporate information space and teamwork of employees.
The main risk is too rapid changes in BI technology, the use of untested solutions and tools. It is necessary to track suppliers, evaluate their sustainability, development directions, regularly try new tools, typify and unify BI. Another risk is related to data quality - if they are not properly transformed, cleaned and consolidated, then no "fancy" features of BI tools or applications will be able to increase the reliability of the data. A number of problems can arise due to inconsistent metadata. Within a large corporation, these issues are resolved at the infrastructural level by creating a corporate data warehouse and centralized metadata management. The creation of a repository will help to bring order to the nomenclature of collected indicators, data collection, dissemination and authorization of access. The BI technology itself is not able to solve these problems comprehensively, and neglecting them returns to information anarchy and “data silo pits”.
Major players in the BI field
According to Gartner's notorious magic squares, EBIS technology leaders today are Business Objects and Cognos, Information Builders on the border between leaders and contenders, and Microsoft and Oracle are contenders. One does not have a standalone OLAP client, but uses the Excel200x pivot table functionality and no report generator, the other does not yet have a replacement for Oracle Express Analyzer. In the group of "visionaries" stand out Crystal Decisions on the border with the leaders. Also of note are Actuate and MicroStrategy.
There are practically no leaders for BI platforms, which indicates the immaturity of technologies and the market. So far, only Microsoft is on the border of this area due to solutions for embedding OLAP services in MS SQL Server and developing them to an analytical server. Among other contenders - SAS Institute, further the dense group is formed by Oracle, PeopleSoft and SAP. Hyperion is literally at a crossroads - SAS and Hyperion lost their leading positions in 2000. Among the visionaries, MicroStrategy should be noted. Unfortunately, Crystal Decisions is still a niche player.
Trends
Among BI tools, EBIS is experiencing the most growth, reflecting increased competition in today's economy. The use of tools for generating queries and reports, data analysis is declining, organizations are updating them and replacing them with corporate BI suites. The core tools (ad hoc queries, reporting, and basic OLAP analysis) are still the most common, covering most needs. There is also a growing use of OLAP and other advanced BI tools like data mining technology. However, standalone data mining tools are disappearing, this technology is being absorbed and included in other BI tools, such as database extensions.
Within 5 years, capabilities such as XML for Analysis (XML/A), BI Web services, collaboration, wireless and mobile communications are expected to converge to form business intelligence networks (BI networks), which will be complemented by business monitoring tools. activities (Business activity monitoring, BAM).
XML for parsing. XML / A originally appeared as a communication protocol between different BI layers (client, analytical server, database server). XML/A has serious performance problems - it creates a lot of overhead and is currently only applicable to a "lightweight" OLAP client. However, if these issues are resolved, XML/A could become a common language (lingua franca) between different BI environments, crossing multiple domains, vendors, and technologies, thus supporting BI networks.
BI Web Services. Vendors often identify EBIS products as BI portals because the Web versions of these products provide an entry point to corporate information. In fact, these BI portals often also support links to unstructured information, although this usually requires some sort of integration system. More and more EBIS products focus on the external components of the corporation (extranet e-business intelligence). The new service-oriented SOA component architecture is an evolution of application servers and enterprise portals. This innovation is also related to J2EE and .NET technologies. BI Web services make BI tools open components with known interfaces and available on all kinds of networks. An increasing number of vendors of BI products are implementing them as Web services, but more often under the guise of portals.
Collaboration. Adding annotations to reports and sharing analysis results among multiple users has been possible since the days of EIS, but this functionality is now popular and workflow capabilities have been added to many BI applications. Users are expected to be able to work on the same model at the same time or link different BI applications in real time.
Wireless and mobile business intelligence. Another strong trend in delivering BI information is seen with vendors enabling BI products to deliver reports via mobile technology, including PDAs, Internet phones, and pagers.
Monitoring of business activity. New technology BAM is essentially operational BI and combines real-time application integration with business intelligence capabilities. Using transactional data extracted from real-time transaction processing systems, BI tools analyze this data and issue critical event alerts and information to operational decision makers.
Literature
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- Christine Comaford. .
- Tom Sullivan. .
Valery Artemiev(avi @cbr.ru) - Advisor to the Director of the Main Informatization Center of the Bank of Russia (Moscow).
There are a huge number of terms: analytics, data mining, data analysis, business intelligence and the difference between them is not always so obvious even for people who are connected with this. Today we will talk about what is Business Intelligence (BI) in an accessible and understandable language. The topic is certainly huge and cannot be covered in just one short article, but our task is to help take the first step and interest the reader in the topic. The interested reader will also find an exhaustive list for further steps.
Article structure
Why all this is needed: from the life of an analyst
(clickable)Let's imagine that we (a certain analyst Petrovich at the supplier Flower) are tasked with evaluating the sales of a number of stores (where we supply goods) and each store keeps its own records of the goods sold. The reality is that the accounting forms will be filled in no matter how and no one understands by whom, that is, they will have a different structure and different storage format (some form of tables). Schematically, this task is depicted in the diagram above.
It would seem that the task is simple and therefore we will consider a frontal solution: let's say we have N tables and we need to collect them together into one table, then we will write N scripts that convert these tables and one collector that collects them together.
Cons of this approach:
- it is necessary to support N scripts at the same time (where N is in the order of thousands);
- when changing the structure of store reports over time (for example, a new employee has appeared in the store), it is necessary to search for and rewrite individual scripts;
- when a new store appears, you need to write a new script;
- when changing our reporting (supplier Flower), it is necessary to make changes to all scripts;
- difficult debugging and support, since stores do not notify structure changes and do not follow any specifications.
If we rise to the level of the whole organization, we will see that there are even more problems.
What is the problem: a problem at the company level
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The Flower manufacturer actually does not work directly with stores, but through some intermediaries. Intermediaries visit stores and directly by their actions try to stimulate sales. Accordingly, they are materially interested parties and the information they give out has to be double-checked.
Basically, the problem looks similar: if we have N stores and K distributors, can we aggregate store data and compare it with the results of distributors? (All data have a different structure and format.)
Here, in addition to tables, we can already encounter a whole zoo of formats to which distributors' reports are added. As a rule, the task is characterized by very low data quality, including duplication, inconsistency and errors. Based on the results obtained and the comparison of data, the purchasing department makes decisions about how much, to whom and how much to ship what. That is, the solution of this problem directly affects financial indicators companies, which is very important.
Consider several solutions at the company level:
- self-written solution: the manufacturer will need to hire a specialist not in the company's profile and critical software will depend on this specialist. If he leaves, the company will be forced to urgently look for a replacement that can support the software and the quality will directly depend on the hired specialist;
- buy software from a third party, there are three key factors: price, quality and integration time. As a rule, the price and integration time are too high for the average manufacturer, and it also requires a significant amount of time for employees. The choice of supplier is also not trivial;
- SaaS solutions: the methodology is still new to the market and many companies are skeptical about such services.
In general, if we are talking about a small or medium-sized manufacturer, then in terms of integration time, price and quality of the solution, the service looks like the best option because pricing is dynamic and integration is minimal via the web. As a rule, the advantage of corporate software is customizability and customization (each business considers itself unique), but the described task is quite typical and standard for a fairly wide range of companies. Of course, there is no single solution for everyone, but for each individually it can be found.
The process itself at the company level looks similar: data is consolidated, transformed (aggregated) in a certain way and loaded into the system for analysis.
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We generalize the problem: all these are links of one chain
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What is the difference between analytics, data mining and business intelligence (BI)? The former include a set of methods for analyzing already clean data, and in practice, cleaning and converting data into a format convenient for analysis is an important and integral process. Also, in addition to working with the transformation and consolidation of data, the main task of BI is decision making for business.
