Olap transcript. OLAP in financial management

The aim of the course work is to study OLAP technology, the concept of its implementation and structure.

In the modern world, computer networks and computing systems make it possible to analyze and process large amounts of data.

A large amount of information greatly complicates the search for solutions, but makes it possible to obtain much more accurate calculations and analysis. To solve this problem, there is a whole class of information systems that perform analysis. Such systems are called decision support systems (DSS) (DSS, Decision Support System).

To perform the analysis, the DSS should accumulate information, having the means of its input and storage. In total, there are three main tasks solved in the DSS:

· data input;

· data storage;

· data analysis.

Data entry into the DSS is carried out automatically from sensors that characterize the state of the environment or process, or by a human operator.

If the data is entered automatically from sensors, then the data is accumulated by a readiness signal that appears when information appears or by cyclic polling. If the input is carried out by a person, then they should provide users with convenient means for entering data, checking them for correct input, as well as performing the necessary calculations.

When entering data simultaneously by several operators, it is necessary to solve the problems of modification and parallel access of the same data.

DSS provides analytics with data in the form of reports, tables, graphs for study and analysis, which is why such systems provide decision support functions.

In data entry subsystems called OLTP (On-linetransactionprocessing), operational data processing is implemented. For their implementation, conventional database management systems (DBMS) are used.

The analysis subsystem can be built on the basis of:

· Subsystems of information retrieval analysis based on relational DBMS and static queries using the SQL language;

· Subsystems of operational analysis. To implement such subsystems, the OLAP online analytical data processing technology is used, using the concept of multidimensional data presentation;

· Subsystems of intellectual analysis. This subsystem implements DataMining methods and algorithms.

From the user's point of view, OLAP systems provide a means of flexible viewing of information in various slices, automatic obtaining of aggregated data, performing analytical operations of convolution, detailing, comparison over time. Thanks to all this, OLAP systems are a solution with great advantages in the field of data preparation for all types of business reporting, involving the presentation of data in different sections and different levels of hierarchy, such as sales reports, various forms of budgets, and others. OLAP systems have great advantages of such a presentation in other forms of data analysis, including forecasting.

1.2 Definition OLAP-systems

The technology for complex multivariate data analysis is called OLAP. OLAP is a key component of an HD organization.

OLAP functionality can be implemented in various ways, both the simplest, such as data analysis in office applications, and more complex - distributed analytical systems based on server products.

OLAP (On-LineAnalyticalProcessing) is a technology for on-line analytical data processing using tools and methods for collecting, storing and analyzing multidimensional data and to support decision-making processes.

The main purpose of OLAP systems is to support analytical activities, arbitrary requests from analyst users. The purpose of OLAP analysis is to test emerging hypotheses.

data warehouses are formed on the basis of snapshots of databases of operational information system and possibly various external sources. Data warehouses use database technologies, OLAP, deep data analysis, data visualization.

The main characteristics of data warehouses.

• contains historical data;
• stores detailed information, as well as partially and completely aggregated data;
• the data is mostly static;
• ad-hoc, unstructured and heuristic way of data processing;
• medium and low intensity of transaction processing;
• unpredictable way of using data;
• intended for analysis;
• focused on subject areas;
• support for strategic decision making;
• serves a relatively small number of executives.

The term OLAP (On-Line Analytical Processing) is used to describe the data presentation model and, accordingly, the technology for their processing in data warehouses. OLAP uses a multidimensional view of aggregated data to provide quick access to strategic information for in-depth analysis. OLAP applications must have the following basic properties:

• multidimensional data presentation;
• support for complex calculations;
• correct consideration of the time factor.

OLAP advantages:

• the rise productivity production personnel, developers application programs... Timely access to strategic information.
• providing users with ample opportunity to make their own changes to the schema.
• OLAP applications rely on data warehouses and OLTP systems, receiving up-to-date data from them, which allows saving integrity control corporate data.
• reducing the load on OLTP systems and data warehouses.

OLAP and OLTP. Characteristics and main differences

OLAP	OLTP
Data store should include both internal corporate data and external data	the main source of information entering the operational database is the activities of the corporation, and for data analysis it is required to involve external sources of information (for example, statistical reports)
The volume of analytical databases is at least an order of magnitude larger than the volume of operational ones. for reliable analysis and forecasting in data store you need to have information about the activities of the corporation and the state of the market for several years	For operational processing, data for the last few months is required
Data store should contain uniformly presented and agreed information that best matches the content of operational databases. A component is needed to extract and "clean" information from different sources. Many large corporations simultaneously have several operational ISs with their own databases (for historical reasons).	Operational databases can contain semantically equivalent information presented in different formats, with different indication of the time of its arrival, sometimes even contradictory
The set of queries against an analytical database is impossible to predict. data warehouses exist to respond to ad hoc analyst requests. You can only count on the fact that requests will not come too often and involve large amounts of information. The size of the analytical database stimulates the use of queries with aggregates (sum, minimum, maximum, mean etc.)	Data processing systems are created with a view to solving specific problems. Information from the database is selected frequently and in small portions. Usually, the set of queries to the operational database is known already during design.
With low variability of analytical databases (only when loading data), the ordering of arrays, faster indexing methods for mass sampling, storage of pre-aggregated data turn out to be reasonable.	Data processing systems by their nature are highly volatile, which is taken into account in the used DBMS (normalized database structure, rows are stored in an unordered manner, B-trees for indexing, transactional)
Analytical database information is so critical for a corporation that a large granulation of protection is required (individual access rights to certain rows and / or columns of a table)	For data processing systems, usually enough information protection at the table level

Codd rules for OLAP systems

In 1993, Codd published a work titled OLAP for Analytic Users: The Way It Should Be. In it, he outlined the basic concepts of online analytical processing and identified 12 rules that must be met by products that enable online analytical processing.

1. Conceptual multidimensional view. The OLAP model must be multidimensional at its core. A multidimensional conceptual schema or custom view facilitates modeling and analysis as well as computation.
2. Transparency. The user is able to get all the necessary data from the OLAP machine, without even knowing where it comes from. Whether the OLAP product is part of the user's tools or not, this fact should be invisible to the user. If OLAP is provided by client-server-side computing, then this fact should also, if possible, be invisible to the user. OLAP should be presented in the context of a truly open architecture, allowing the user, wherever they are, to communicate with the server using an analytic tool. In addition to this, transparency must also be achieved when the analytical tool interacts with homogeneous and heterogeneous database environments.
3. Availability. OLAP must provide its own logic diagram for access in a heterogeneous database environment and perform appropriate transformations to provide data to the user. Moreover, it is necessary to think in advance about where and how, and what types of physical data organization will actually be used. An OLAP system should only access the data that is actually required, and not apply the general principle of the "kitchen funnel", which entails unnecessary input.
4. Constant performance when developing reports. Performance reporting should not drop significantly with the increase in the number of dimensions and the size of the database.
5. Client-server architecture. The product is required to be not only client-server, but also that the server component is smart enough so that different clients can connect with a minimum of effort and programming.
6. General multidimensionality. All dimensions must be equal, each dimension must be equivalent both in structure and in operational capabilities. True, additional operational capabilities for individual dimensions are allowed (apparently, time is implied), but such additional functions must be provided to any dimension. It shouldn't be so that basic data structures, computational or reporting formats were more specific to any one dimension.
7. Dynamic control sparse matrices... OLAP systems should automatically adjust their physical schema based on model type, data volumes and database sparseness.
8. Multiplayer support. OLAP tool must provide capabilities sharing(request and addition), integrity and security.
9. Unlimited crossovers. All kinds of operations must be allowed for any measurements.
10. Intuitive data manipulation. Data manipulation was carried out through direct actions on cells in the view mode without using menus and multiple operations.
11. Flexible reporting options. Measurements should be placed in the report as the user needs.
12. Unlimited

The conditions of high competition and the growing dynamics of the external environment dictate increased requirements for enterprise management systems. The development of management theory and practice was accompanied by the emergence of new methods, technologies and models focused on improving the efficiency of activities. Methods and models, in turn, contributed to the emergence of analytical systems. The demand for analytical systems in Russia is high. These systems are most interesting from the point of view of application in the financial sector: banks, insurance business, investment companies. The results of the work of analytical systems are necessary primarily for people, on whose decision the development of the company depends: managers, experts, analysts. Analytical systems allow solving the problems of consolidation, reporting, optimization and forecasting. Until now, the final classification of analytical systems has not developed, as well as there is no general system of definitions in terms used in this direction. The information structure of an enterprise can be represented by a sequence of levels, each of which is characterized by its own way of processing and managing information, and has its own function in the management process. Thus, analytical systems will be located hierarchically at different levels of this infrastructure.

Transactional systems layer

Data warehouse tier

Data mart layer

OLAP level - systems

Analytical Application Layer

OLAP - systems - (OnLine Analytical Processing, analytical processing in real time) - are a technology for complex multidimensional data analysis. OLAP - systems are applicable where there is a task of analyzing multifactor data. They are an effective tool for analysis and report generation. The data warehouses, data marts and OLAP systems discussed above are classified as business intelligence systems (Business Intelligence, BI).

Very often, information and analytical systems, created with the expectation of direct use by decision-makers, are extremely easy to use, but severely limited in functionality. Such static systems are called Executive Information Systems (EIS) in the literature. They contain predefined sets of queries and, being sufficient for day-to-day review, are unable to answer all the questions about the available data that may arise when making decisions. The result of the work of such a system, as a rule, is multi-page reports, after a thorough study of which the analyst has a new series of questions. However, each new request not foreseen in the design of such a system must first be formally described, coded by the programmer, and only then executed. The waiting time in this case can be hours and days, which is not always acceptable. Thus, the external simplicity of static DSSs, for which most customers of information-analytical systems are actively fighting, turns into a catastrophic loss of flexibility.

Dynamic DSS, on the other hand, is focused on processing ad hoc analyst requests for data. The requirements for such systems were considered most deeply by E. F. Codd in the article that laid the foundation for the concept of OLAP. Analysts work with these systems in an interactive sequence of forming queries and studying their results.

But dynamic DSS can operate not only in the area of ​​online analytical processing (OLAP); support for making management decisions based on accumulated data can be carried out in three basic areas.

Detailed data sphere. This is the domain of most information retrieval systems. In most cases, relational DBMSs do an excellent job with the tasks that arise here. The generally accepted standard for the language of relational data manipulation is SQL. Information retrieval systems that provide an end-user interface in the tasks of searching for detailed information can be used as add-ons both over separate databases of transactional systems and over a common data warehouse.

Scope of aggregates. A comprehensive look at the information collected in the data warehouse, its generalization and aggregation, hypercube representation and multidimensional analysis are the tasks of online analytical data processing (OLAP) systems. Here you can either focus on special multidimensional DBMS, or stay within the framework of relational technologies. In the second case, pre-aggregated data can be collected in a star-shaped database, or the aggregation of information can be performed on the fly in the process of scanning detailed tables of a relational database.

The sphere of regularities. Intellectual processing is carried out by methods of data mining (IAD, Data Mining), the main tasks of which are the search for functional and logical patterns in the accumulated information, the construction of models and rules that explain the found anomalies and / or predict the development of some processes.

Prompt analytical data processing

The OLAP concept is based on the principle of multidimensional data presentation. In a 1993 article by EF Codd examined the shortcomings of the relational model, primarily pointing out the impossibility "to combine, view and analyze data in terms of multiple dimensions, that is, in the most understandable way for corporate analysts", and identified the general requirements for OLAP systems that extend functionality of relational DBMS and includes multivariate analysis as one of its characteristics.

Classification of OLAP products according to the way data is presented.

Currently, there are a large number of products on the market that provide OLAP functionality to one degree or another. About 30 of the most famous are listed on the overview Web server http://www.olapreport.com/. By providing a multidimensional conceptual view from the user interface to the source database, all OLAP products are divided into three classes, similar to the source database type.

The earliest on-line analytical processing systems (for example, Essbase from Arbor Software, Oracle Express Server from Oracle) belonged to the MOLAP class, that is, they could work only with their own multidimensional databases. They are based on proprietary multidimensional DBMS technologies and are the most expensive. These systems provide a full cycle of OLAP processing. They either include, in addition to the server component, their own integrated client interface, or use external spreadsheet programs to communicate with the user. To maintain such systems, a special staff of employees is required to install, maintain the system, and form data representations for end users.

Relational online analytical processing (ROLAP) systems allow you to represent data stored in a relational database in multidimensional form, providing information transformation into a multidimensional model through an intermediate metadata layer. ROLAP systems are well suited to work with large storage facilities. Like MOLAP systems, they require significant IT maintenance and are multi-user.

Finally, hybrid systems (Hybrid OLAP, HOLAP) are designed to combine the advantages and minimize the disadvantages inherent in the previous classes. This class includes Speedware's Media / MR. According to the developers, it combines the analytical flexibility and responsiveness of MOLAP with the constant access to real data inherent in ROLAP.

Multidimensional OLAP (MOLAP)

In specialized DBMSs based on multidimensional data representation, data is organized not in the form of relational tables, but in the form of ordered multidimensional arrays:

1) hypercubes (all cells stored in the database must have the same dimension, that is, be in the most complete basis of measurements) or

2) polycubes (each variable is stored with its own set of measurements, and all associated processing difficulties are shifted to the internal mechanisms of the system).

The use of multidimensional databases in on-line analytical processing systems has the following advantages.

In the case of using a multidimensional DBMS, the search and selection of data is much faster than with a multidimensional conceptual view of a relational database, since the multidimensional database is denormalized, contains pre-aggregated indicators and provides optimized access to the requested cells.

Multidimensional DBMSs easily cope with the tasks of including various built-in functions in the information model, while objectively existing limitations of the SQL language make it quite difficult and sometimes impossible to perform these tasks on the basis of relational DBMSs.

On the other hand, there are significant limitations.

Multidimensional DBMSs do not allow working with large databases. In addition, due to denormalization and previously performed aggregation, the amount of data in a multidimensional database, as a rule, corresponds (according to Codd) to 2.5-100 times less than the volume of the original detailed data.

Multidimensional DBMSs use external memory very inefficiently compared to relational ones. In the overwhelming majority of cases, the information hypercube is highly sparse, and since the data is stored in an ordered form, undefined values ​​can be removed only by choosing the optimal sort order that allows organizing the data into the largest possible contiguous groups. But even in this case, the problem is only partially solved. In addition, the sort order that is optimal for storing sparse data is likely to be different from the order that is most often used in queries. Therefore, in real systems, you have to find a compromise between performance and redundancy of disk space occupied by the database.

Therefore, the use of multidimensional DBMS is justified only under the following conditions.

The volume of initial data for analysis is not too large (no more than several gigabytes), that is, the level of data aggregation is quite high.

The set of information dimensions is stable (since any change in their structure almost always requires a complete restructuring of the hypercube).

The system response time to ad hoc requests is the most critical parameter.

Extensive use of complex built-in functions is required to perform cross-dimensional calculations on cells of a hypercube, including the ability to write custom functions.

Relational OLAP (ROLAP)

Direct use of relational databases in online analytical processing systems has the following advantages.

In most cases, corporate data warehouses are implemented using relational DBMS tools, and ROLAP tools allow you to perform analysis directly on them. At the same time, the storage size is not such a critical parameter as in the case of MOLAP.

In the case of a variable dimension of the problem, when changes in the measurement structure have to be made quite often, ROLAP systems with a dynamic representation of the dimension are the optimal solution, since in them such modifications do not require a physical reorganization of the database.

Relational DBMSs provide a significantly higher level of data protection and good opportunities for differentiating access rights.

The main disadvantage of ROLAP compared to multidimensional DBMS is lower performance. Relational systems require extensive database schema and index tuning to achieve performance comparable to MOLAP, which means a lot of effort on the part of DBAs. Only by using star schemas can the performance of well-tuned relational systems be close to the performance of multidimensional database systems.

The concept of OLAP technology was formulated by Edgar Codd in 1993.

This technology is based on the construction of multidimensional datasets - the so-called OLAP cubes (not necessarily three-dimensional, as one might conclude from the definition). The purpose of using OLAP technologies is to analyze data and present this analysis in a form that is convenient for management personnel to perceive and make decisions based on them.

Key requirements for multivariate analysis applications:

• - providing the user with the analysis results in a reasonable time (no more than 5 s.);
• - multi-user access to data;
• - multidimensional data presentation;
• - the ability to access any information regardless of its storage location and volume.

OLAP systems tools provide the ability to sort and select data according to specified conditions. Various qualitative and quantitative conditions can be specified.

The main data model used in numerous tools for creating and maintaining databases - DBMS, is the relational model. The data in it is presented in the form of a set of two-dimensional relationship tables linked by key fields. To eliminate duplication, inconsistency, reduce labor costs for maintaining databases, a formal apparatus for normalizing entity tables is used. However, its use is associated with additional time spent on generating responses to database queries, although memory resources are saved.

A multidimensional data model represents the investigated object in the form of a multidimensional cube, more often a three-dimensional model is used. Dimensions or attribute attributes are plotted along the axes or faces of the cube. The base attributes are the filling of the cube cells. A multidimensional cube can be represented by a combination of three-dimensional cubes in order to facilitate perception and presentation in the formation of reporting and analytical documents and multimedia presentations based on the materials of analytical work in the decision support system.

Within the framework of OLAP technologies, based on the fact that the multidimensional presentation of data can be organized both by means of relational DBMSs and multidimensional specialized tools, there are three types of multidimensional OLAP systems:

• - multidimensional OLAP-MOLAP;
• - relational (Relation) OLAP-ROLAP;
• - mixed or hybrid (Hibrid) OLAP-HOLAP.

In multidimensional DBMSs, data is organized not in the form of relational tables, but in the form of ordered multidimensional arrays in the form of hypercubes, when all stored data must have the same dimension, which means the need to form the most complete basis of measurements. Data can be organized in the form of polycubes, in this version the values ​​of each indicator are stored with their own set of measurements, data processing is performed by the system's own tool. The storage structure in this case is simplified, since there is no need for a multidimensional or object-oriented storage area. The huge labor costs for creating models and systems for transforming data from a relational model to an object model are reduced.

The advantages of MOLAP are:

• - faster, than with ROLAP, receiving responses to requests - the time spent is one to two orders of magnitude less;
• - Many built-in functions are difficult to implement due to SQL limitations.

MOLAP restrictions include:

• - relatively small size of databases;
• - due to denormalization and preliminary aggregation, multidimensional arrays use 2.5-100 times more memory than the original data (memory consumption grows exponentially with an increase in the number of measurements);
• - there are no standards for the interface and data manipulation tools;
• - there are restrictions when loading data.

The effort required to create multidimensional data increases dramatically. in this situation, there are practically no specialized means of objectifying the relational data model contained in the information storage. The response time to queries often cannot meet the requirements for OLAP systems.

The advantages of ROLAP systems are:

• - the possibility of online analysis of the data directly contained in the data warehouse, since most source databases are relational;
• - with a variable dimension of the problem, RO-LAP win, because no physical reorganization of the database is required;
• - ROLAP systems can use less powerful client stations and servers, and the servers bear the bulk of the burden of processing complex SQL queries;
• - the level of information protection and differentiation of access rights in relational DBMSs is incomparably higher than in multidimensional ones.

The disadvantages of ROLAP systems are lower performance, the need for careful elaboration of database schemas, special tuning of indexes, analysis of query statistics and consideration of the analysis findings when updating database schemas, which leads to significant additional labor costs.

Fulfillment of these conditions allows, when using ROLAP systems, to achieve indicators similar to MOLAP systems in terms of access time, as well as surpass in memory savings.

Hybrid OLAP systems are a combination of tools that implement a relational and multidimensional data model. This allows you to dramatically reduce the cost of resources for the creation and maintenance of such a model, the response time to requests.

This approach takes advantage of the advantages of the first two approaches and compensates for their disadvantages. This principle is implemented in the most developed software products for this purpose.

The use of hybrid architecture in OLAP systems is the most appropriate way to solve the problems associated with the use of software tools in multivariate analysis.

The pattern detection mode is based on intelligent data processing. The main task here is to identify patterns in the processes under study, interrelationships and interactions of various factors, search for large "unusual" deviations, forecast the course of various essential processes. This area belongs to data mining.

The main difference between facts and information is that we receive and take note of the data, and we can use the information to our advantage. Roughly speaking, information is analyzed and systematized data. Thanks to the information received on time, many firms manage to withstand both the financial crisis and the fiercest competition. It is not enough to collect facts and have all the data you need. You also need to be able to analyze them. Various support systems have been developed to make it easier for people to make important business decisions. It is for this purpose that various complex systems have been developed that allow you to analyze large arrays of heterogeneous data and turn them into information useful for a business user. The new field of business intelligence seeks to improve the process management of business systems through the use of data warehouses and technologies.

The market of information systems for business today offers a diverse range of solutions that help an enterprise organize management accounting, ensure operational management of production and sales, and effectively interact with customers and suppliers.

A separate niche in the business systems market is occupied by analytical software products designed to support decision-making at the strategic level of enterprise management. The main difference between such tools and operational management systems is that the latter provide enterprise management in a "mode of operation", that is, the implementation of a well-defined production program, while analytical systems at a strategic level help the management of the enterprise to work out solutions in a "development mode".

The scale of the changes carried out can vary from deep restructuring to partial renewal of technologies at certain production sites, but, in any case, decision-makers are considering development alternatives, on which the fate of the enterprise depends in the long term.

No matter how powerful and developed the information system of the enterprise is, it cannot help in solving these issues, firstly, because it is tuned to stationary, well-established business processes, and secondly, it does not, and cannot be, information for making decisions regarding new areas of business, new technologies, new organizational decisions.

Thanks to OLAP (On-Line Analytical Processing) data processing technology, any organization can almost instantly (within five seconds) get the data it needs to work. OLAP can be summarized in five keywords.

FAST (Fast) - this means that the search and delivery of the necessary information takes no more than five seconds. The simplest queries are processed in a second, and only a few complex queries have a processing time of more than twenty seconds. To achieve this result, various methods are used, from special forms of data storage to extensive pre-computation. Thus, you can get a report in a minute, which previously took days to prepare.

ANALYSIS (Analytical) says that the system can do any analysis, both statistical and logical, and then stores it in an accessible form.

SHARED means the system provides the required privacy, down to the cell level

MULTIDIMENSIONAL - This is the main characteristic of OLAP. The system must fully support hierarchies and multiple hierarchies, since this is the most logical way to analyze both the business and the activities of organizations.

INFORMATION The information you need must be delivered where it is needed.

During the work of an organization, data related to the field of its activity always accumulates, which are sometimes stored in completely different places, and it is not easy and time-consuming to bring them together. It is in order to accelerate the acquisition of data to test emerging business hypotheses that the technology of interactive analytical data processing or OLAP was developed. The main purpose of such OLAP systems is to quickly respond to arbitrary user requests. Such a need often arises when developing some important business project, when the developer needs a working hypothesis that has arisen. Most often, the information the user needs should be presented in the form of some kind of dependence - for example, how the sales volume depends on the product category, on the sales region, on the season, and so on. Thanks to OLAP, he has the ability to immediately receive the necessary data in the desired layout for the selected period.

Interactive OLAP technology transforms huge piles of reports and tons of data into useful and accurate information that will help an employee make an informed business or financial decision at the right time.

In addition, thanks to OLAP, processing efficiency increases, and a user can get large amounts of sorted (aggregated) information almost instantly. Thanks to OLAP, the user can clearly see how effectively his organization is working, has the ability to quickly and flexibly respond to external changes, and has the ability to minimize the financial losses of his organization. OLAP provides accurate information that improves decision making.

The only drawback of business intelligence systems is their high cost. Building a personal information warehouse takes both time and a lot of money.

The use of OLAP technology in business allows you to quickly obtain the necessary information, which, at the request of the user, can be presented in the usual form - reports, graphs or tables.

System integration procedures for business structures are based on the use of joint ERP, CRM and SCM solutions. In many cases, systems are supplied by different manufacturers, and the imported data must go through a process of data reconciliation and presentation in the form of heterogeneous data. In a business environment, an unambiguous requirement is assumed - a complete data analysis, which involves viewing the consolidated reports from different points of view.

Different vendors have different presentation mechanisms. The heterogeneous representation procedure involves extraction, transformation, and loading (ETL). For example, in Microsoft SQL Server 2005 Analysis Services, the data consolidation problem is implemented using Data Source Views, which are types of data sources that describe analytical view models.

Business applications based on OLAP technologies, product examples. The most common applications of OLAP technologies are:

Data analysis.

The task for which OLAP tools were originally used and still remain the most popular. A multidimensional data model, the ability to analyze significant amounts of data and a quick response to requests make such systems indispensable for analyzing sales, marketing activities, distribution and other tasks with a large amount of initial data.

Product examples: Microsoft Excel Pivot Tables, Microsoft Analysis Services, SAP BW, Oracle Essbase, Oracle OLAP, Cognos PowerPlay, MicroStrategy, Business Objects.

Financial planning and budgeting.

A multidimensional model allows you to simultaneously enter data and easily analyze it (for example, plan-fact analysis). Therefore, a number of modern products of the CPM (Corporate Performance Management) class use the OLAP% model. An important task is the multidimensional back calculation (backsolve, breakback, writeback), which allows you to calculate the required changes in detailed cells when the aggregated value changes. It is a what-if analysis tool. to play various variants of events when planning.

Product examples: Microsoft PerformancePint, Oracle EPB, Oracle OFA, Oracle Hyperion Planning, SAP SEM, Cognos Enterprise Planning, Geac.

Financial consolidation.

Consolidation of data in accordance with international accounting standards, taking into account ownership shares, various currencies and internal turnovers, is an urgent task in connection with the tightening requirements of auditing bodies (SOX, Basel II) and companies entering IPOs. OLAP technologies make it possible to speed up the calculation of consolidated reports and increase the transparency of the entire process.

Product examples: Oracle FCH, Oracle Hyperion FM, Cognos Controller.

Data warehouses and On-Line Analytical Processing (OLAP) technologies
are essential elements of business decision support, which are increasingly becoming an integral part of any industry. The use of OLAP technologies as a tool for business intelligence gives more control and timely access to strategic
information that contributes to effective decision-making.
This provides the ability to simulate real-world forecasts and more efficient use of resources. OLAP enables an organization to respond more quickly to market demands.

Bibliography:

1. Erik Thomsen. OLAP Solutions: Building Multidimensional Information Systems Second Edition. Wiley Computer Publishing John Wiley & Sons, Inc., 2002.

2. OLAP council white paper, http://www.olapcouncil.org/research/whtpaply.htm

3. Gerd Stumme and Bernhard Ganter. Formal Concept Analysis _ Mathematical Foundations.