1 bi business process management technologies. Key BI tools

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.

1. 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.
2. BI applications. Applications that are not considered as tools. An example is EIS − Information system for the leader.

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 for 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.

Using business intelligence improves quality and responsiveness management decisions, and also helps to manage business processes, which leads to an increase in the competitiveness of the company. This has become one of the main reasons for the significant surge of interest in BI (business intelligence) class solutions, which IDC has been celebrating in Russia since 2010.

Experts argue about the functionality of information systems that allow business analysis. But the process of working with analytical data and the IT solution designed for these purposes are not at all the same thing. Before embarking on the implementation of a BI system, a company must prepare for its use: formalize business processes, determine the points of collection of information, the types of data collected and the purpose for which this information will be used. After that, you can talk about the specific BI tools that the business needs.

Business intelligence differs from the manual analysis of indicators in Excel spreadsheets in the same way that an airplane differs from a hang glider. It's not just a matter of speed. After all, BI is the automation of the process of collecting information and reporting. While working with Excel suggests that someone must collect the analyzed data from all information sources of the company, bring it to a single template, and only then generate reports.

The difference in the results of working with this information is also great. BI is the multidimensionality of the data used and the ability to quickly generate reports in any context, using any information available in the company for this. In other words, a task that people will cope with in a day (for example, calculate the dependence of a store's sales of certain models of clothing on the demographic composition of the population and the transport infrastructure of the area) will be solved by the system in minutes.

For a long time, BI solutions were based on the so-called OLAP cubes. The use of such systems continues to this day. They represent the information stored in the repository in such a way that at any time you can take any available indicators as the axes of the "cube" and make an analysis on the necessary sections by building a flat table or graph of the dependence of one indicator on another. Importantly, the analysis takes place in real time, which is what the abbreviation OLAP – online analytical processing – means.

Among other features, we highlight the presence of metadata management functions, development tools, tools for collaboration and process management, reporting tools, advanced visualization, predictive modeling and data mining functions, scorecards.

Now the market continues to grow sales of BI-systems that implement іn-memory technologies. The main idea of ​​in-memory is in principle permanent storage data in RAM. This gives users the ability to get answers instantly - in a fraction of a second - even when they work with huge amounts of data. However, such solutions are not suitable for everyone from a technical point of view, and many customers continue to use OLAP technology.

The presence of online analytical processing is one of the features of an analytical system that allows it to be called a full-fledged BI platform according to Gartner.

In addition to different technological architecture, BI systems differ in a set of tools for different categories of business users.

For example, full-fledged BI platforms differ greatly in terms of functionality from BI modules that are embedded in some corporate information systems and have limited presentation capabilities.

Each user role has its own dashboards that represent the information that these employees need. key indicators business in the form of tables or infographics. The BI toolkit also provides reporting tools and an interface for viewing them: in the system window, via the web or on mobile device user. Tools for determining the correlation of data help in building reports.

One of the dominant trends in the last five years in the BI market is the growth in demand for mobile analytics. Users of BI-systems, who appreciated their value for business, also understood the value of constant access to such tools. Almost every major BI vendor today is ready to provide users with online analytics tools. At the same time, mobile workplaces are focused not only on top managers, but also on a number of other categories of users who need to constantly have up-to-date information about the state of certain business processes. So, from a “boss privilege,” BI mobility has become a means of rapid response to events for line managers and analysts. Since the BI system provides for working with large amounts of data coming into the warehouse from various information systems and in an unstructured form, it can be used to work with "big data" (big data), which business has been so interested in in recent years. This is not surprising, since the volume of stored and processed information is growing at a faster pace, therefore, companies are forced to think about acquiring additional computing power. At the same time, in real business Usually, up to 30% of all stored information is used, while the rest becomes only a source of costs for its storage.

The presence of large volumes of unstructured and potentially useful information in companies, as well as the great opportunities provided to analysts by BI systems, have become one of the drivers of progress in this area. Today, more and more analysts are looking for more flexible tools that would allow them to study any data and build business hypotheses. This led to the emergence of a new class of tools - data discovery. They are based on a flexible data model and interactive user interfaces that are more convenient for business users than analysts. On the example of data discovery, we see how the toolkit is gradually growing into an independent direction of IT systems for analytics.

Since BI is not only about analyzing the current situation, but also about forecasting, advanced tools have been developed for analysts and managers to test their hypotheses. And notification of the achievement of threshold values ​​by them will help to control key indicators during the analysis.

How will the BI system toolkit and its use develop Russian companies in future? Will there be new user roles, new interfaces, will top managers work more with business intelligence? Maria Golikova, a consultant in the analytical department of Softline, is convinced that one of the development vectors is associated with the growing demand for clouds and visualization tools: “With the development of cloud technologies, many large BI developers began to offer additional features available specifically in the cloud.

If there is a “tradition” in the company to prepare reports in the form of static Excel tables, then many employees will find it difficult to abandon this. However, it is hoped that over time the number of companies that will receive comprehensive information using informative dashboards will grow.”

Also, according to the expert, the growing popularity of BI will be facilitated by the attentive attitude of developers to the friendliness of interfaces and the creation of mobile jobs: “BI tools are now moving towards independent analysis - solutions are becoming as easy to use as possible. This allows business users to independently change current reports or create new ones due to an intuitive interface. Top management today is also attracted by the possibility of using mobile BI solutions. A manager can go on a business trip, but at the same time see the main indicators of his business on the screen of a portable device.

Speaking about which tools provided by BI platforms are most in demand by Russian customers, Alexander Gerasimov, Director of the IT and Cloud Services Department at J'son & Partners Consulting, notes: post factum reporting based on data analysis of transactional systems, such as ERP, OSS / BSS (billing in particular), automated banking systems, etc.

What has good prospects is big data analysis technologies: not only structured information of transactional systems, but also weakly (or complexly) structured data, such as, for example, logs and geodata of smartphone users and much more. Now such information is mainly used to enrich and improve the quality of reporting ex post. In the future, they can be used directly in control systems for the purpose of their intellectualization”.

Some BI systems suggest using more infographics instead of classic tabular reports. But not everyone is ready to perceive graphic information.

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, is there “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 the original definitions, BI is the process of analyzing information, generating intuition and understanding for improved and informal decision making by business users, as well as 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 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. Other applications were used to obtain additional information and further analysis, or custom SQL queries or reports were created.

First generation DSS applications were packages application programs with dynamic generation of SQL scripts according to 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 the 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 can 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 use different data warehouse solutions for the second. 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. Due to RDBMS vendors creating OLAP extensions to their RDBMS, many platform vendors that provided multidimensional DBMS 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 various tools, we see that EBIS are highly functional tools, but they do not have such of great importance like 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 organizational function 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 mining repeatedly performs various operations and transformations on raw data (feature selection, stratification, clustering, visualization, and regression) that are designed to: 1) find representations that are intuitive to people who, in turn, better understand the business -processes underlying their 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. It can be any mixture of desktop clients with network connection, desktop clients and servers, 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 information 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. To use BI in operating systems may require an operational data store (ODS), possibly linked to corporate structures workflow. 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. The new BAM technology 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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Valery Artemiev(avi @cbr.ru) - Advisor to the Director of the Main Informatization Center of the Bank of Russia (Moscow).

In 2007, the market for BI platforms experienced major changes due to its significant consolidation. Major vendors have made strategic acquisitions: Oracle has completed its acquisition of Hyperion, SAP has announced its acquisition of Business Objects, Cognos has completed its acquisition of Applix and agreed to a merger with IBM.

How did these events affect the BI platform market? The clearest answer to this question can be obtained by looking at Gartner's magic square (Figure 1), which shows the distribution of top BI platform vendors in early 2007 and 2008.

Rice. 1. Position of leading vendors in the market of BI platforms (Source: Gartner)

Before commenting on the above changes, let's take a quick look at Gartner's methodology for selecting and presenting BI platform vendors on the "magic square" plane. First of all, let's clarify what Gartner understands by the term "BI platform".

What is a BI platform according to Gartner

In the very general plan Gartner defines a BI platform as a tool that enables organizations to build applications that enable them to learn and understand their business. According to a more detailed interpretation, Gartner defines the BI platform (BI platform) as a software platform that provides 12 functions, which, in turn, are divided into three groups: integration, information delivery tools, and information analysis tools.

Integration

GeneralBI infrastructure- all platform tools should use the same security tools, common metadata, common administrative tools, common query generation tools, and also have the same type of interfaces.

Metadata management- all application tools should not only rely on the same metadata, but should also provide fast search, storage, use and publication of metadata objects such as dimensions, hierarchies, performance evaluation parameters and reporting parameters.

Development tools- along with the tools for creating separate BI applications, the BI platform should provide tools software development for integrating applications into a common business process or for embedding them in another application. The BI platform should allow developers to create BI applications without coding, based on the use of wizards (wizard-like components) for visual editing.

Collaboration and workflow management - this opportunity allows BI users to share and discuss information using shared folders and discussion threads. In addition, BI applications can assign and track events or tasks assigned to individual users based on some predefined business rules. Typically, this functionality is provided through integration with a separate workflow tool.

Means of providing information

Reporting tools(Reporting) - make it possible to create formatted interactive reports. In addition to this, BI platform vendors should provide a wide range of report types (financial, operational, etc.) in the form of dashboards.

Dashboards(Dashboards) - one of constituent parts reports, presentation of information in the form of an intuitive graphic image, including diagrams, dials, traffic lights, etc. These indicators show the state of the analyzed parameter against the background of its intended purpose (Fig. 2).

Rice. 2. An example of an information panel (Dashboard)

A manager or analyst, like an airplane pilot, sees a "dashboard" in front of him and controls the system, focusing on the values ​​of the indicators. At the same time, the key factors necessary for managing the enterprise must be somehow measured and presented in the form of indicators. The motto of the concept is “If you can’t measure it, you can’t manage it”.

Ad Hoc Request Generator(Ad hoc query) - Also known as self-service reporting, this feature allows users to get answers to their questions. The system provides a means of navigating through the available data resources.

Integration with Microsoft Office- in some cases, BI platforms are used as an intermediate link in the information analysis chain, and Microsoft Office (in particular Excel) acts as a BI client. In these cases, it is critical that the BI vendor provide integration with Microsoft Office, including support for document formats, formulas, and pivot tables.

Information analysis tools

OLAP(Online Analytical Processing - analytical processing in real time) - information processing technology, including the compilation and dynamic publication of reports and documents. Used to quickly process complex database queries. OLAP technology provides high speed request processing. It takes a snapshot of a relational database and structures it into a spatial model for queries. The fact is that relational databases store entities in separate tables and complex multi-table queries are executed relatively slowly in them, while a spatial database is more successful model for requests. The claimed query processing time in OLAP is about 0.1% of similar queries in a relational database.

Advanced Visualization- advanced visualization tools allow you to present data for more effective perception through the use of interactive pictures and charts instead of tables (Fig. 3). Typically, users can dynamically change the graphical representation, use scaling, combine data, change colors.

Rice. 3. An example of using visualization in providing data
on the Cognos dashboard

Predictive modeling and data mining. Predictive Modeling is the process of creating (or selecting) a model to predict the likelihood of an event occurring. Data Mining is the process of discovering previously unknown, non-trivial, useful and accessible knowledge in raw data that is necessary for decision making. The information found in the process of using Data Mining methods should describe new relationships between properties, predict the values ​​of some features based on others, etc. Found knowledge should be applicable to new data with some degree of certainty. When the extracted knowledge is not transparent to the user, there should be post-processing methods to bring it to an interpretable form. Tasks solved by Data Mining methods include:

• classification - assignment of objects (observations, events) to one of the previously known classes;
• regression, including forecasting tasks; establishing the dependence of continuous output on input variables;
• clustering - grouping of objects (observations, events) based on data (properties) that describe the essence of these objects. Objects within a cluster must be similar to each other and different from objects in other clusters. The more similar objects within a cluster and the more differences between clusters, the more accurate the clustering;
• association - identifying patterns between related events. An example of such a pattern is a rule indicating that from an event X event follows Y. Such rules are called associative. This problem was first proposed to find typical patterns of purchases made in supermarkets, so it is sometimes also called market basket analysis;
• sequential patterns - establishing patterns between events related in time, that is, detecting a relationship according to which if an event occurs X, then after a given time, an event will occur Y;
• deviation analysis - identifying the most uncharacteristic patterns.

Scorecards(Scorecards) use the benchmarks displayed on the dashboard for deeper analysis by overlaying them on some kind of strategic map that links key performance parameters to strategic objectives. This concept is illustrated in Fig. 4. Technology involves further analysis based on the application of performance management methodology, such as Six Sigma.

Rice. 4. Comparison of key performance parameters
with strategic objectives

After we have explained the term BI platform, let's return to the analysis of the "magic square" in Fig. 1.

Criteria for selection and evaluation of companies

The Gartner study (see Figure 1) included companies selected according to the following criteria:

• providing at least 8 of the 12 features inherent in the BI platform;
• occupying a significant share of the BI platform market, as evidenced by sales of at least $20 million;
• solutions on platforms that work at the enterprise level, and not just at the departmental level.

On fig. 1, a number of terms are used, according to which vendors are located on the plane of the square. Let's explain their meaning:

• the possibility of implementation is determined by the following factors:
  • how competitive and successful are the products,
  • what is the probability that the vendor will continue to invest in the product/service,
  • How successful is the vendor's pricing policy,
  • how resistant the vendor is to changes in the market,
  • how informed customers are about the vendor's offerings,
  • how vendors are able to fulfill marketing promises,
  • how satisfied customers are with the vendor's service support;
• completeness of vision is the ability of a vendor to exploit market trends to create additional services for customers and the corresponding benefits for themselves. The completeness of vision can be assessed based on the quality of:
  • forecasts of customer needs,
  • marketing strategy,
  • sales strategies,
  • development strategies in vertical market segments,
  • strategies for entering remote markets;
• leaders are vendors that ensure the wide functionality of their products, their successful implementation and provide high-quality support at the global level;
• applicants - have limitations that may be related not only to the breadth of the spectrum technological solutions, but also with market indicators, such as quality sales network and so on.;
• visionaries are vendors with a strong strategy for promoting BI platforms, which is manifested in the openness of standards, the flexibility of solution architecture, and the depth of functionality of the applications they create. They are leaders in the field innovation activities;
• niche players - occupy a leading position in some limited product or technology area.

Trends in the BI platform market

As can be seen from fig. 1, megavendors are starting to dominate the BI market. Indeed, in less than a year, Microsoft, Oracle, SAP and IBM have gone from owning a quarter of the market to owning two-thirds.

When comparing the squares for 2007 and 2008, it is clear that Microsoft has risen to take first place in terms of implementation opportunities. SAP is not yet in the lead, apparently because the merger with Business Objects has not yet been completed. Oracle has moved into second place behind SAS in terms of completeness of vision.

Thus, the magic square of BI platforms for 2008 reflects the fact that leadership is moving from independent BI vendors such as Business Objects and Cognos to mega-vendors.

In July 2007, Oracle completed the acquisition of Hyperion. This has resulted in two competing platforms - Hyperion System 9 and Oracle Business Intelligence Enterprise Edition - merging under Oracle's leadership and thus expanding Oracle's BI resources in both technology and human resources.

In October 2007, SAP announced the acquisition of Business Objects in order to expand its market presence. This merger (which was completed in January 2008) closes a significant gap in SAP's query and report generators.

Cognos has completed the takeover of Applix, which has powerful OLAP technology, and in turn has agreed to be taken over by IBM.

Over the same period, factors such as the maturation of the Microsoft BI portfolio, development Web technologies 2.0, development of BI products with open source, the evolution of Software as a Service (SaaS) offerings, have made BI functionality more accessible than ever before.

OpenSource BI solutions have made significant progress in their development, but the turnover from their implementation is still insignificant. JasperSoft, one of the largest vendors in the field, claims to have over 7,000 commercial customers and over 70,000 active deployments.

There is also a growing interest in providing BI solutions in the form of SaaS. In particular, Business Objects is a leader in the business of providing BI applications on demand (OnDemand), but there are smaller firms such as Seatab, Oco and LucidEra that provide BI solutions as a service. The use of BI solutions in the form of an OnDemand service is not suitable for all organizations; it is of little use for organizations that work with classified data. Nevertheless, every year more and more companies choose the SaaS model as more economical and reliable enough.

Analysis of the position of leading vendors

business objects

Among companies that specialize exclusively in BI solutions, Business Objects offers the most complete platform with well-developed report and query generation technology.

About 90% of organizations that have implemented this solution note that it is standard for their organization.

Business Objects expanded its BI offering in 2007 with the addition of Inxight.

The rapid growth of Business Objects' on-demand (OnDemand) BI offerings to over 70,000 users makes it the de facto leader in SaaS-BI.

Business Objects will have to adjust its strategy after acquiring a new status as a result of the transition to the ownership of SAP, that is, it will have to spend some time on changing sales channels, support systems, etc.

According to customer reviews, OLAP is weak side in Business Objects solutions.

Cognos

Cognos has an exceptionally high adoption rate of its BI platform as a standard solution for enterprises. More than 90% of those surveyed consider Cognos the standard for their organization.

Cognos is actively investing in efforts to improve the architecture of the platform. With the advent of version 8.2 and the future version 8.3, Cognos 8 BI has almost got rid of problems with insufficient stability and poor technical support. At present, most of the clients operate latest version CognosBI.

Once the merger of Cognos with IBM is completed, the Cognos BI platform will benefit from its ability to integrate with IBM technologies.

Another benefit for Cognos will come as it embraces Applix TM1 OLAP technology.

Data mining technology Cognos is still weaker than the offerings of its competitors.

Microsoft

lucky price policy and integration with MS Office makes Microsoft solutions especially attractive for organizations that are based on the company's infrastructure solutions.

When promoting its BI solutions, Microsoft can rely on a large audience of developers. Microsoft estimates that this is 2 thousand OEM / ISV partners for the implementation of its BI solutions.

According to customer reviews, BI solutions from Microsoft cause minimal complaints.

Microsoft's BI solutions were created by Microsoft, not purchased with an affiliated firm.

Microsoft belatedly joined the race to promote BI platforms and therefore now its strategy is to "catch up and overtake." Customers estimate that Microsoft still lags behind traditional BI platform companies, especially in terms of metadata management, reporting, and dashboarding.

microstrategy

Instead of an affiliation tactic, MicroStrategy built the technology entirely in-house. This provides a high degree of integration within the platform.

MicroStrategy has positive reviews customers across all 12 criteria assessed by Gartner.

The development of new technologies can lead to a weakening of MicroStrategy's position, which it currently occupies in the field of processing extra-large amounts of data.

MicroStrategy has a reputation for offering expensive solutions that are hard to get a discount on.

MicroStrategy focuses exclusively on BI platforms and pays little attention to related technologies - CPM (Corporate Performance Management - corporate performance management) and data integration.

MicroStrategy has a small sales volume in the Asia-Pacific region.

Oracle

Even before Hyperion joined in mid-2007, Oracle's position in the BI market was quite strong: its combination of BI platform and analytical applications (Oracle BI Enterprise Edition (OBIEE) and Oracle Analytic Applications) was a very successful offering.

Customers give E positive feedback on OBIE. They note the wide range of solutions for organizing teamwork, as well as advanced visualization tools, which, according to them, are among the best on the market.

The strengths of the Essbase OLAP engine and Hyperion's integration with Microsoft Office enhance Oracle's potential in the BI market.

Oracle is in a good position to promote its BI technologies to a variety of clients, not just Oracle platform enthusiasts.

The process of integrating the BI solutions resulting from the merger will take a long time in 2008.

There is evidence that among installations of Hyperion BI Base, the percentage of the latest version is low, which indicates that customers are in no hurry to upgrade to the latest version of the product.

Oracle should improve technical support.

SAP

With over 13,000 deployments, SAP has made great strides in moving forward with NetWeaver BI. More than 75% of SAP customers surveyed by Gartner testified that BI solutions from SAP are standard in their organizations.

With the integration of SAP and Business Objects complete, SAP will become the largest BI platform vendor, twice the size of any other competitor.

The strengths of Business Objects, primarily formatted report generation and self-service report creation, successfully fill in the gaps in SAP BI solutions.

In a Gartner study, SAP customers using the latest version of SAP BI noted implementation difficulties.

The inclusion of Business Objects somewhat reduces SAP's score, which Gartner loosely refers to as implementability. This is due to the inevitable uncertainty for customers who have relied on SAP's already in-house BI products.

Despite the fact that implemented NetWeaver BI solutions are capable of importing data from non-SAP applications, SAP can name no more than 25 large enterprises who have adopted NetWeaver BI wherever they dominate accounting systems SAP. To achieve market leadership, SAP needs to demonstrate that it can implement its platform in enterprises where SAP applications are not dominant.

SAS

SAS is a leader in advanced analytics (Advanced Analytic Solutions).

SAS offers analytics solutions that not only provide basic functionality at the KPI analysis level, but also offer advanced analytics for detecting business problems such as fraud detection.

SAS is famous brand, SAS solutions have worldwide service support.

SAS applications are considered difficult to learn. Many advanced analytics applications require the use of a special SAS programming language - this is an advantage for programmers and a significant limitation for people who do not have such skills.

In conclusion, we list the main trends in the BI platform market:

• the relevance of the task of optimizing the performance of companies at all levels stimulates the demand for BI solutions;
• the capabilities of BI platforms are expanding, and, in addition to traditional report and query generators, as well as OLAP functionality, "dashboards" (dashboards), scorecards (scorecards) and advanced visualization have been actively developed;
• mega-vendors begin to dominate the BI market;
• BI solutions in the form of SaaS are being actively promoted by many vendors;
• the process of mergers and standardization is the engine of the market.

business intelligence

business intelligence or abbreviated BI- business analysis, business analytics. This concept most often means software created to help a manager in analyzing information about his company and its environment. There are several ways to understand this term.

• Business analytics are methods and tools for building informative reports on the current situation. In this case, the purpose of business intelligence is to provide the right information to the right person who needs it at the right time. This information can be vital for making managerial decisions.
• Business intelligence is the tools used to transform, store, analyze, model, deliver, and trace information while working on evidence-based decision-making tasks. At the same time, with the help of these tools, decision makers should receive the right information at the right time using the right technologies.

Thus, BI in the first sense is only one of the business intelligence sectors in the broader second sense. In addition to reporting, it includes data integration and cleansing (ETL) tools, analytical data warehouses, and Data Mining tools.

BI technologies make it possible to analyze large amounts of information, focusing users' attention only on key performance factors, modeling the outcome various options actions, tracking the results of making certain decisions.

The history of the term

The term first appeared in a 1958 paper by IBM researcher Hans Peter Lun. Hans Peter Luhn). He defined the term as: "The ability to understand the connections between presented facts."

BI as we know it today evolved from decision-making systems that emerged in the early 1960s and were developed in the mid-1980s.

In 1989, Howard Dresner (later an analyst at Gartner) defined Business intelligence as general term describing "concepts and methods for improving business decision making using business data-driven systems".

Notes

Links

• Is Business Analytics replacing Business Intelligence? (j-l PC Week/RE No. 41 (599) November 6 - November 12, 2007)
• BI as a Marketing Campaign Optimization Tool (PC Week Review: Business Intelligence, May 2010)
• Business Intelligence: Today and Tomorrow (Intelligent Enterprise Magazine No. 2 (212), February 2010)
• Business Intelligence on Russian Soil (J-l PC Week Review: Business Intelligence, May 2010)

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