Creating an olap application. Practical examples of using OLAP for reporting. From the point of view of an IT specialist

Creating an olap application

The Kontur Standard system is part of the Kontur Analytical Platform (APK), which is developed by Intersoft Lab. The Kontur analytical platform is designed for analyzing financial, statistical and other business information and issuing reports.

Kontur systems use modern technology for operational analytical processing of business data OLAP (On-line Analytical Processing). Today, OLAP is the most powerful type of tabular report generator. Kontur systems allow you to receive arbitrary on-screen reports for interactive analysis and print reports on paper.

"Kontur Standard" is offered to industrial and commercial enterprises, banks and other organizations as a means of analyzing corporate data and preparing reports. The system can be used for analysis in various subject areas: supply management based on ERP system data, sales analysis based on CRM system data, financial analysis based on mandatory reporting indicators, etc.:

managers will be able to quickly obtain the indicators of interest.

managers of sales and purchasing departments - monitor sales dynamics, the current state of client accounts and warehouse stocks.

marketing specialists - solve problems such as segmentation customer base, demand analysis, assessment of consumer interest based on traffic data to the company’s website (click-stream analysis).

analytics - implement an individual model for assessing various aspects of the organization’s activities, etc.

"Contour Standard" can be used in almost any area where it is necessary to analyze numerical data and obtain multiple views of the same data set in different reports. OLAP analysis tools allow huge amounts of business information to be shown in analytical reports in the form of a clear picture of the state of the business. A variety of graphs and diagrams will help you compare indicators, see their dynamics, etc.

Work with the “Contour Standard” system is organized in two stages:

Creation of an OLAP application - a package of analytical reports for the end user: manager, analyst, marketer, etc. For this purpose, the “Contour Standard” system in the “Designer” edition is used.

Analyze and issue reports using a ready-made OLAP application.

For this purpose, the “Contour Standard” system can be used in the “Designer”, “Analyst”, “Observer” or “Inspector” editions. OLAP application

(*.caf file format) is a separate software resource (metadata repository) that stores settings for data sources, queries to data sources and analytical reports. Creating an OLAP application consists of a sequential description of these settings. Data sources – local tables and relational databases of automated systems that store corporate data for analysis (MS Excel tables, databases of accounting or ERP systems, data warehouses, etc.). "Contour Standard" supports direct access to local tables (Dbase and Paradox) and to tables and stored procedures relational DBMS

(MS SQL, Oracle, Sybase, MS Access, etc.). Requests

– SQL queries to data sources, described in the system at the level of rules for merging data source tables by key fields, data filtering conditions and a set of returned fields. The result of the query is a flat selection of data displayed in reports. Reports

• – user interfaces for data analysis. In “Contour Standard” you can create 4 types of reports:

  Cluster analysis.

OLAP report.

All custom reports can be illustrated with graphs, printed, uploaded to office applications (MS Excel, MS Word, html format) or saved in csv format. Tables

– flat list reports accompanied by graphs. The user can quickly redraw graphs by filtering dimension values ​​and manipulating facts and dimensions along the axes. This allows you to quickly change the “point of view” on the analyzed data in search of dependencies and trends. Trends

– reports reflecting changes in indicators over time and the dynamics of these changes. It is especially effective to use trends to study seasonal variations and forecast trends. Cluster analysis

– an interface that allows you to combine objects into groups (clusters) according to given characteristics, compare groups, and identify large (most influential) and small (least influential) among them. This interface is useful, for example, when creating marketing analysis applications. OLAP reports

– managed dynamic OLAP tables, which are accompanied by a synchronous diagram (graph). An OLAP report can contain data obtained upon request from both the database and a local microcube.– a table that automatically summarizes data (facts) in various sections (dimensions) and allows you to interactively manage calculations and report form. Dimensions are displayed in the names of the rows and columns of the table, the corresponding facts and totals (aggregated facts) are displayed in the table cells. Columns and rows are table management tools. The user can move them, filter, sort, drill down/summarize and perform other OLAP operations. In this case, the table automatically calculates new totals (aggregates). By managing an OLAP table, you can generate multiple reports from one set of data.

In an OLAP table, you can roughly distinguish several work areas:

Active measurement area– rows and columns of an OLAP table.

Inactive measurement area(filters) – an area containing dimensions that are not displayed in the OLAP table, but affect the data presented in it.

Fact area– a table with numerical data on which calculations are performed.

Diagram– graphical representation of data synchronous with the OLAP table. The diagram is built according to the elements of the dynamic table located in the leftmost position, that is, according to the leftmost row dimension, column dimension and facts (leftmost

or for all). To change the dimension (row or column) that the chart is based on, you need to move it to the far left position in the table. You can change the order in which facts are displayed using a special filter.

Data displayed in an OLAP report can be saved to Microcube.

Microcube(*.cube file format) is a local multidimensional database that contains data downloaded from relational sources and a description of their presentation in OLAP reports (metadata). When placed in a microcube, the volume of original information is compressed tens of times. The compact size allows microcubes to be transferred via Internet protocols and sent via e-mail. Microcubes can contain information for various specialists and departments, industry indicators, analytical reviews and ratings, etc. In fact, a microcube is a mobile container for analytical reports and data for analysis.

Practical task

Let's look at the main stages of creating an OLAP application. For each stage, the user's technology is illustrated using the example of setting up a package of reports for analyzing sales of a network of office supply stores.

The source data for analysis is stored in local tables:

1. Table “Sales data” (Sales.dbf) with fields:

Date (DATE), Store code (ID_SHOP), Product code (ID_PRODUCT), Plan (PLAN),

2. Table “Product Directory” (Product.dbf) with fields:

Product code (ID), Product (PRODUCT), Unit of measurement (MEASURE).

3. Table “Shop Directory” (Shop.dbf) with fields:

Store code (ID), Store (ADDRESS).

Copy the folder Sale_shop to the catalog < WITH>:\Program Files\IntersoftLab\ContourStandart2\App

Perhaps for some, the use of OLAP technology (On-line Analytic Processing) when creating reports will seem somewhat exotic, so the use of OLAP-CUBE for them is not at all one of the most important requirements when automating budgeting and management accounting.

In fact, it is very convenient to use a multidimensional CUBE when working with management reporting. When developing budget formats, you may encounter the problem of multivariate forms (you can read more about this in Book 8, “Technology for setting up budgeting in a company,” and in the book, “Setting up and automating management accounting”).

This is due to the fact that effective management of a company requires increasingly detailed management reporting. That is, the system uses more and more different analytical sections (in information systems, analytics are determined by a set of reference books).

Naturally, this leads to the fact that managers want to receive reporting in all analytical sections that interest them. This means that the reports need to be made to “breathe” somehow. In other words, we can say that in this case we are talking about the fact that the meaning of the same report should provide information in different analytical aspects. Therefore, static reports no longer suit many modern managers. They need the dynamics that a multidimensional CUBE can provide.

Thus, OLAP technology has already become a mandatory element in modern and future information systems. Therefore, when choosing a software product, you need to pay attention to whether it uses OLAP technology.

Moreover, you need to be able to distinguish real CUBES from imitation ones. One such simulation is pivot tables in MS Excel. Yes, this tool looks like a CUBE, but in fact it is not one, since these are static, not dynamic tables. In addition, they have a much worse implementation of the ability to build reports using elements from hierarchical directories.

To confirm the relevance of using CUBE when constructing management reporting, we can give a simple example with a sales budget. In the example under consideration, the following analytical sections are relevant for the company: products, branches and sales channels. If these three analytics are important for the company, then the sales budget (or report) can be displayed in several versions.

It should be noted that if you create budget lines based on three analytical sections (as in the example under consideration), this allows you to create quite complex budget models and create detailed reports using CUBE.

For example, a sales budget can be compiled using only one analytics (directory). An example of a sales budget built on the basis of one analytics "Products" is presented at Figure 1.

Rice. 1. An example of a sales budget built on the basis of one analytics “Products” in OLAP-CUBE

The same sales budget can be compiled using two analytics (directories). An example of a sales budget built on the basis of two analytics “Products” and “Branches” is presented at Figure 2.

Rice. 2. An example of a sales budget built on the basis of two analytics “Products” and “Branches” in the OLAP-CUBE of the INTEGRAL software package

.

If there is a need to build more detailed reports, then you can draw up the same sales budget using three analytics (directories). An example of a sales budget built on the basis of three analytics “Products”, “Branches” and “Sales Channels” is presented at Figure 3.

Rice. 3. An example of a sales budget built on the basis of three analytics “Products”, “Branches” and “Sales Channels” in the OLAP-CUBE of the INTEGRAL software package

It should be recalled that the CUBE used to generate reports allows you to display data in different sequences. On Figure 3 The sales budget is first “expanded” by product, then by branch, and then by sales channel.

The same data can be presented in a different sequence. On Figure 4 the same sales budget is “expanded” first by product, then by sales channel, and then by branch.

Rice. 4. An example of a sales budget built on the basis of three analytics “Products”, “Distribution Channels” and “Branches” in the OLAP-CUBE of the INTEGRAL software package

On Figure 5 the same sales budget is “unfolded” first by branches, then by products, and then by sales channels.

Rice. 5. An example of a sales budget built on the basis of three analytics “Branches”, “Products” and “Sales Channels” in the OLAP-CUBE software package “INTEGRAL”

Actually that's not all possible options withdrawal of the sales budget.

In addition, you need to pay attention to the fact that KUB allows you to work with the hierarchical structure of directories. In the examples presented, the hierarchical directories are “Products” and “Distribution Channels”.

From the user's point of view, he is in this example receives several management reports (see Rice. 1-5), and from the point of view of settings in software product- this is one report. Just using the CUBE you can view it in several ways.

Naturally, in practice it is very possible a large number of options for outputting various management reports if their articles are based on one or more analysts. And the set of analytics itself depends on the users’ needs for detail. True, we should not forget that, on the one hand, the larger the analyst, the more detailed reports can be built. But, on the other hand, this means that the financial budgeting model will be more complex. In any case, if there is a KUB, the company will have the opportunity to view the necessary reports in various options, in accordance with the analytical sections of interest.

It is necessary to mention several more features of the OLAP-CUBE.

In a multidimensional hierarchical OLAP-CUBE there are several dimensions: row type, date, rows, directory 1, directory 2 and directory 3 (see. Rice. 6). Naturally, the report displays as many buttons with reference books as there are in the budget line containing maximum amount reference books. If there is not a single reference book in any budget line, then the report will not have a single button with reference books.

Initially, the OLAP-CUBE is built along all dimensions. By default, when the report is initially built, the dimensions are located in exactly the areas shown in Figure 6. That is, a dimension such as “Date” is located in the area of ​​vertical dimensions (dimensions in the column area), dimensions “Rows”, “Directory 1”, “Directory 2” and “Directory 3” - in the area of ​​horizontal dimensions (dimensions in the area rows), and the “Row Type” dimension is in the area of ​​“unexpanded” dimensions (dimensions in the page area). If a dimension is in the last area, then the data in the report will not "expand" on that dimension.

Each of these dimensions can be placed in any of the three areas. Once measurements are transferred, the report is instantly rebuilt to match the new measurement configuration. For example, you can swap the date and lines with reference books. Or you can move one of the reference books to the vertical measurement area (see. Rice. 7). In other words, you can “twist” the report in the OLAP-CUBE and select the report output option that is most convenient for the user.

Rice. 7. An example of rebuilding a report after changing the measurement configuration of the INTEGRAL software package

The measurement configuration can be changed either in the main CUBE form or in the change map editor (see. Rice. 8). In this editor, you can also drag and drop measurements from one area to another with the mouse. In addition, you can swap measurements in one area.

In addition, in the same form you can configure some measurement parameters. For each dimension, you can customize the location of totals, the sorting order of elements, and the names of elements (see. Rice. 8). You can also specify which element name to display in the report: abbreviated (Name) or full (FullName).

Rice. 8. Measurement map editor of the INTEGRAL software package

You can edit measurement parameters directly in each of them (see. Rice. 9). To do this, click on the icon located on the button next to the measurement name.

Rice. 9. Example of editing directory 1 Products and services in

Using this editor, you can select the elements that you want to show in the report. By default, all elements are displayed in the report, but if necessary, some elements or folders can be omitted. For example, if you need to display only one product group in the report, then you need to uncheck all the others in the measurement editor. After that, the report will contain only one product group (see. Rice. 10).

You can also sort elements in this editor. In addition, elements can be rearranged different ways. After such a regrouping, the report is instantly rebuilt.

Rice. 10. Example of output in a report of only one product group (folder) in the INTEGRAL software package

In the dimension editor, you can quickly create your own groups, drag and drop elements from directories there, etc. By default, only the Other group is automatically created, but other groups can be created. Thus, using the dimension editor, you can configure which elements of the reference books and in what order should be displayed in the report.

It should be noted that all such rearrangements are not recorded. That is, after closing the report or after its recalculation, all directories will be displayed in the report in accordance with the configured methodology.

In fact, all such changes could have been made initially when setting up the lines.

For example, using restrictions you can also specify which elements or groups of directories should be displayed in the report and which should not.

Note: the topic of this article is discussed in more detail at workshops "Budget management of an enterprise" And "Organization and automation of management accounting" conducted by the author of this article, Alexander Karpov.

If the user almost regularly needs to display only certain elements or directory folders, then it is better to make such settings in advance when creating report lines. If various combinations of directory elements in reports are important to the user, then there is no need to set any restrictions when setting up the methodology. All such restrictions can be quickly configured using the measurement editor.

In 1993, the founder of the relational approach to database construction, Edgar Codd and his partners (Edgar Codd, a mathematician and IBM fellow), published an article initiated by Arbor Software (today the famous company Hyperion Solutions), entitled "Providing OLAP (online analytical processing) for analytical users", which formulated 12 features of OLAP technology, which were subsequently supplemented by six more. These provisions became the main content of a new and very promising technology.

Main features of the technology OLAP (Basic):

• multidimensional conceptual representation of data;
• intuitive data manipulation;
• availability and detail of data;
• batch data extraction against interpretation;
• OLAP analysis models;
• client-server architecture (OLAP accessible from the desktop);
• transparency (transparent access to external data);
• multi-user support.

Special Features(Special):

• processing of unformalized data;
• saving OLAP results: storing them separately from the source data;
• exclusion of missing values;
• Handling missing values.

Features of reporting(Report):

• flexibility in reporting;
• standard reporting performance;
• automatic configuration of the physical data extraction layer.

Dimension management(Dimension):

• universality of measurements;
• unlimited number of dimensions and aggregation levels;
• unlimited number of operations between dimensions.

Historically, today the term "OLAP" implies not only a multidimensional view of data from the end user, but also a multidimensional view of the data in the target database. This is precisely why the emergence as independent terms is connected "Relational OLAP"(ROLAP) and "Multidimensional OLAP"(MOLAP).

OLAP service is a tool for analyzing large volumes of data in real time. By interacting with the OLAP system, the user will be able to flexibly view information, obtain arbitrary data slices and perform analytical operations of detailing, convolution, end-to-end distribution, and comparison over time using many parameters simultaneously. All work with the OLAP system occurs in terms of the subject area and allows you to build statistically sound models of the business situation.

OLAP software - it's a tool operational analysis data contained in the repository. Main feature is that these tools are intended for use by non-specialists in the field information technologies, not an expert statistician, but a professional in the applied field of management - a manager of a department, department, management, and, finally, a director. The tools are designed for analyst communication with the problem, not with the computer. In Fig.

Figure 6.14 shows an elementary OLAP cube that allows you to evaluate data along three dimensions. Multidimensional OLAP cube and system of corresponding mathematical algorithms statistical processing

allows you to analyze data of any complexity at any time interval.

Rice. 6.14.

Having at his disposal flexible mechanisms for data manipulation and visual display (Fig. 6.15, Fig. 6.16), the manager first examines data from different angles that may (or may not) be related to the problem being solved. Next, he compares various business indicators with each other, trying to identify hidden relationships; can look at the data more closely, in detail, for example, breaking it down into components by time, region or customer, or, conversely, further generalize the presentation of information to remove distracting details. After that, using the module statistical evaluation and simulation modeling

Several options for the development of events are constructed, and the most acceptable option is selected.

A company manager, for example, may have a hypothesis that the spread of asset growth in various branches of the company depends on the ratio of specialists with technical and economic education in them. To test this hypothesis, the manager can request from the warehouse and display on a graph the ratio of interest for those branches whose asset growth in the current quarter decreased by more than 10% compared to last year, and for those which increased by more than 25%. He should be able to use a simple selection from the menu provided. If the results obtained significantly fall into two corresponding groups, then this should become an incentive for further testing of the hypothesis put forward.

Currently, a direction called dynamic modeling(Dynamic Simulation), which fully implements the above FASMI principle.

Using dynamic modeling, the analyst builds a model of a business situation that develops over time, according to a certain scenario. Moreover, the result of such modeling can be several new business situations that generate a tree possible solutions with an assessment of the likelihood and prospects of each.

Rice. 6.16.

Table 6.3 shows the comparative characteristics of static and dynamic analysis.

Table 6.3.

Characteristic	Static analysis	Dynamic analysis
Types of questions	Who? What? How many? How? When? Where?	Why is that? What would happen if...? What happens if…?
Response time	Not regulated	Seconds
Typical data operations	Regulated report, chart, table, drawing	Sequence of interactive reports, charts, screen forms. Dynamic change aggregation levels and data slices
Level of analytical requirements	Average	High
Type of screen forms	Basically predetermined, regulated	User defined, customizable
Data aggregation level	Detailed and summary	User defined
"Age" of data	Historical and current	Historical, current and projected
Types of requests	Mostly predictable	Unpredictable - from case to case
Purpose	Regulated Analytical Processing	Multi-pass analysis, modeling and forecasting

Almost always, the task of building an analytical system for multidimensional data analysis is the task of building a single, consistently functioning information system based on heterogeneous software and solutions. And the very choice of means for implementing IP becomes extremely challenging task. Many factors must be taken into account here, including the mutual compatibility of various software components, ease of their development, use and integration, operational efficiency, stability and even forms, level and potential prospects of relationships between various manufacturing companies.

OLAP is applicable wherever there is a task of analyzing multivariate data. In general, if you have a data table that has at least one descriptive column and one column with numbers, an OLAP tool will be an effective tool for analyzing and generating reports. As an example of the use of OLAP technology, consider a study of the results of the sales process.

Key questions: “How much was sold?”, “For what amount was sold?” expand as the business becomes more complex and historical data accumulates to a certain number of factors, or sections: “..in St. Petersburg, Moscow, the Urals, Siberia...”, “.. in the last quarter, compared to the current one,” “ ..from supplier A compared to supplier B...", etc.

Answers to such questions are necessary for making management decisions: on changing the assortment, prices, closing and opening stores, branches, terminating and signing contracts with dealers, carrying out or terminating advertising campaigns etc.

If you try to highlight the main figures (facts) and sections (measurement arguments) that the analyst manipulates, trying to expand or optimize the company’s business, you will get a table suitable for sales analysis as a kind of template that requires appropriate adjustments for each specific enterprise.

Time. As a rule, these are several periods: Year, Quarter, Month, Decade, Week, Day. Many OLAP tools automatically calculate the highest periods from a date and calculate totals for them.

Product category. There can be several categories, they differ for each type of business: Variety, Model, Type of packaging, etc. If only one product is sold or the assortment is very small, then a category is not needed.

Product. Sometimes the name of the product (or service), its code or article number are used. In cases where the assortment is very large (and some enterprises have tens of thousands of items in their price list), the initial analysis for all types of goods may not be carried out, but generalized to some agreed categories.

Region. Depending on the globality of the business, you can mean Continent, Group of countries, Country, Territory, City, District, Street, Part of the street. Of course, if there is only one outlet, then this dimension is missing.

Salesman. This measurement also depends on the structure and scale of the business. This could be: Branch, Store, Dealer, Sales Manager. In some cases, there is no measurement, for example, when the seller does not influence sales volumes, there is only one store, and so on.

Buyer. In some cases, such as retail, the buyer is impersonal and there is no measurement; in other cases, information about the buyer is available and is important for sales. This dimension may contain the name of the purchasing company or many groups and characteristics of clients: Industry, Group of Enterprises, Owner, and so on. Analysis of the sales structure to identify the most important components in the context of interest. For this, it is convenient to use, for example, a “Pie” type diagram in complex cases when 3 dimensions are examined at once - “Columns”. For example, in the Computer Equipment store for the quarter, sales of computers amounted to $100,000, photographic equipment - $10,000, and consumables - $4,500. Conclusion: the store’s turnover depends to a large extent on the sale of computers (in fact, perhaps consumables are necessary to sell computers, but this is an analysis of internal dependencies).

Dynamics analysis ( regression analysis- identifying trends). Identification of trends and seasonal fluctuations. The dynamics are clearly displayed by a “Line” type graph. For example, sales of Intel products fell during the year, while Microsoft's sales grew. Perhaps the welfare of the average customer has improved, or the image of the store has changed, and with it the composition of customers. The assortment needs to be adjusted. Another example: for 3 years, sales of video cameras decrease in winter.

Dependency Analysis(correlation analysis). Comparison of sales volumes of different products over time to identify the required assortment - “basket”. It is also convenient to use a “Line” chart for this purpose. For example, when printers were removed from the range during the first two months, sales of powder cartridges were found to fall.

Dispersion, mean deviation, higher order modes - the most sophisticated types of analytical reports can be obtained.

OLAP systems are part of the more general concept of “intellectual enterprise resources” or “intelligent business analysis tools” (Business Intelligence - BI), which includes, in addition to the traditional OLAP service, tools for organizing the sharing of data and information arising in the process of user work storages. Business Intelligence technology provides electronic exchange reporting documents, differentiation of user rights, access to analytical information from the Internet and Intranet.

Purpose course work is the study of 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) (Decision Support System).

To perform analysis, the DSS must accumulate information, having means for its input and storage. In total, three main tasks can be identified that are solved in the DSS:

· data input;

· data storage;

· data analysis.

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

If data input is carried out automatically from sensors, then the data is accumulated by a readiness signal that occurs when information appears or by cyclic polling. If the input is carried out by a person, then they must provide users with convenient means for entering data, checking them for correctness of 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 to the same data.

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

Data entry subsystems, called OLTP (On-line transaction processing), implement operational data processing. To implement them use conventional systems database management (DBMS).

The analysis subsystem can be built on the basis of:

· information retrieval analysis subsystems based on relational DBMS and static queries using SQL language;

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

· intellectual analysis subsystems. 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 sections, automatic receipt 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 various sections and different levels hierarchy, such as sales reports, various forms of budgets and others. OLAP systems have great advantages of such a representation in other forms of data analysis, including forecasting.

1.2 Definition OLAP -systems

The technology for complex multidimensional data analysis is called OLAP. OLAP is a key component of a data warehouse organization.

OLAP functionality can be implemented in various ways, as simple as analyzing data in office applications, and more complex - distributed analytical systems based on server products.

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

The main purpose of OLAP systems is to support analytical activities, arbitrary requests user analysts. The purpose of OLAP analysis is to test emerging hypotheses.

Purpose of the report

This report will focus on one of the categories of intelligent technologies that are a convenient analytical tool - OLAP technologies.

The purpose of the report: to reveal and highlight 2 issues: 1) the concept of OLAP and their applied significance in financial management; 2) implementation of OLAP functionality in software solutions: differences, opportunities, advantages, disadvantages.

I would like to note right away that OLAP is a universal tool that can be used in any application area, and not just in finance (as can be understood from the title of the report), which requires data analysis various methods.

Financial management

Financial management is an area in which analysis is more important than any other. Any financial and management decision arises as a result of certain analytical procedures. Today, financial management is becoming important for the successful functioning of an enterprise. Despite the fact that financial management is an auxiliary process in an enterprise, it requires special attention, since erroneous financial and managerial decisions can lead to large losses.

Financial management is aimed at providing the enterprise with financial resources in the required amounts, in right time and in the right place in order to receive maximum effect from their use through optimal distribution.

It is perhaps difficult to define the level of “maximum resource efficiency”, but in any case,

The CFO should always know:

• How many financial resources are available?
• Where will the funds come from and in what quantities?
• where to invest more effectively and why?
• and at what points in time does all this need to be done?
• how much is needed to ensure normal operation of the enterprise?

To obtain reasonable answers to these questions, it is necessary to have, analyze and know how to analyze a sufficiently large number of performance indicators. In addition, FU covers a huge number of areas: analysis of cash flows (movements Money), analysis of assets and liabilities, profitability analysis, marginal analysis, profitability analysis, assortment analysis.

Knowledge

Therefore, a key factor in the effectiveness of the financial management process is the availability of knowledge:

• Personal knowledge in the subject area (one might say theoretical and methodological), including experience, intuition of a financier/finance director
• General (corporate) knowledge or systematic information about the facts of financial transactions in an enterprise (i.e. information about the past, present and future state of the enterprise, presented in various indicators and measurements)

If the first lies in the scope of actions of this financier (or the HR director who hired this employee), then the second should be purposefully created at the enterprise by the joint efforts of employees of financial and information services.

What is there now

However, now a paradoxical situation is typical in enterprises: there is information, there is a lot of it, too much. But it is in a chaotic state: unstructured, inconsistent, fragmented, not always reliable and often erroneous, it is almost impossible to find and obtain. A lengthy and often useless generation of mountains of financial statements is carried out, which is inconvenient for financial analysis, is difficult to understand, since it is created not for internal management, but for provision to external regulatory authorities.

According to the results of a study conducted by the company Reuters Among 1,300 international managers, 38% of respondents say they spend a lot of time trying to find the information they need. It turns out that a highly qualified specialist spends highly paid time not on data analysis, but on collecting, searching and systematizing the information necessary for this analysis. At the same time, managers are overloaded with data that is often irrelevant, which again reduces the effectiveness of their work. The reason for this situation: excess information and lack of knowledge.

What to do

Information must be turned into knowledge. For modern business, valuable information, its systematic acquisition, synthesis, exchange, use is a kind of currency, but in order to receive it, it is necessary to manage information, like any business process.

The key to information management is delivering the right information in the right form to stakeholders within the organization at the right time. The goal of such management is to help people work better together using increasing amounts of information.

Information technology in this case acts as a means by which it would be possible to systematize information in an enterprise, provide certain users with access to it and give them the tools to transform this information into knowledge.

Basic concepts of OLAP technologies

OLAP technologies (from the English On-Line Analytical Processing) is the name not of a specific product, but of an entire technology for the operational analysis of multidimensional data accumulated in a warehouse. In order to understand the essence of OLAP, it is necessary to consider the traditional process of obtaining information for decision making.

Traditional decision support system

Here, of course, there can also be many options: complete information chaos or the most typical situation when the enterprise has operational systems with the help of which the facts of certain operations are recorded and stored in databases. To extract data from databases for analytical purposes, a system of queries for specific data samples has been built.

But this method of decision support lacks flexibility and has many disadvantages:

• negligible amount of data is used that can be useful for decision making
• sometimes complex multi-page reports are created, of which 1-2 lines are actually used (the rest is just in case) - information overload
• slow response of the process to changes: if a new data representation is needed, the request must be formally described and coded by the programmer, only then executed. Waiting time: hours, days. Or perhaps a solution is needed now, immediately. But after receiving new information, a new question will arise (clarifying)

If query reports are presented in a one-dimensional format, then business problems are usually multidimensional and multifaceted. If you want to get a clear picture of a company's business, then you need to analyze data from various perspectives.

Many companies create excellent relational databases data, ideally sorting out mountains of unused information, which in itself does not provide either a quick or sufficiently competent reaction to market events. YES - relational databases were, are and will be the most suitable technology for storing corporate data. It's about not about new technology DB, but rather about analysis tools that complement the functions of existing DBMSs and are flexible enough to provide and automate various types of intellectual analysis inherent in OLAP.

Understanding OLAP

What does OLAP provide?

• Advanced storage data access tools
• Dynamic interactive data manipulation (rotation, consolidation or drill-down)
• Visual visual display data
• Fast – analysis is carried out in real mode time
• Multidimensional data presentation - simultaneous analysis of a number of indicators along several dimensions

To get the effect of using OLAP technologies, you must: 1) understand the essence of the technologies themselves and their capabilities; 2) clearly define what processes need to be analyzed, what indicators they will be characterized by and in what dimensions it is advisable to see them, i.e. create an analysis model.

The basic concepts that OLAP technologies operate on are as follows:

Multidimensionality

To understand the multidimensionality of the data, you should first present a table showing, for example, the performance of Enterprise Costs by economic elements and business units.

This data is presented in two dimensions:

• article
• business unit

This table is not informative, as it shows sales for one specific period of time. For different time periods, analysts will have to compare several tables (for each time period):

The figure shows a 3rd dimension, Time, in addition to the first two. (Article, business unit)

Another way to show multidimensional data is to represent it in the form of a cube:

OLAP cubes allow analysts to obtain data at various slices to obtain answers to questions posed by the business:

• Which costs are critical in which business units?
• How do business unit costs change over time?
• How do cost items change over time?

Answers to such questions are necessary for making management decisions: on the reduction of certain cost items, the impact on their structure, identifying the reasons for changes in costs over time, deviations from the plan and their elimination - optimizing their structure.

In this example, only 3 dimensions are considered. It's difficult to depict more than 3 dimensions, but it works in the same way as with 3 dimensions.

Typically, OLAP applications allow you to obtain data on 3 or more dimensions, for example, you can add one more dimension - Plan-Actual, Cost Category: direct, indirect, by Orders, by Months. Additional dimensions allow you to obtain more analytical slices and provide answers to questions with multiple conditions.

Hierarchy

OLAP also allows analysts to organize each dimension into a hierarchy of groups, subgroups, and totals that reflect the measure across the entire organization—the most logical way to analyze a business.

For example, it is advisable to group costs hierarchically:

OLAP allows analysts to obtain data into a common summary measure (actually upper level), and then drill down to the lowest and subsequent levels, and thus discover the exact reason for the change in the indicator.

By allowing analysts to use multiple dimensions in a data cube, with the ability to hierarchically construct dimensions, OLAP provides a picture of the business that is not compressed by the information warehouse structure.

Changing directions of analysis in a cube (rotating data)

As a rule, they operate in concepts: dimensions specified in columns, rows (there may be several of them), the rest form slices, the contents of the table form dimensions (sales, costs, cash)

Typically, OLAP allows you to change the orientation of cube dimensions, thereby presenting the data in different views.

The display of cube data depends on:

• dimension orientations: which dimensions are specified in rows, columns, slices;
• groups of indicators, highlighted in rows, columns, sections.
• Changing dimensions is within the scope of the user's actions.

Thus, OLAP allows you to carry out various types of analysis and understand their relationships with their results.

• Deviation analysis is an analysis of plan implementation, which is supplemented by factor analysis of the causes of deviations by detailing the indicators.
• Dependency analysis: OLAP allows you to identify various dependencies between various changes, for example, when beer was removed from the assortment during the first two months, a drop in roach sales was discovered.
• Comparison (comparative analysis). Comparison of the results of changes in an indicator over time, for a given group of goods, in different regions, etc.
• Analysis of dynamics allows us to identify certain trends in changes in indicators over time.

Efficiency: we can say that OLAP is based on the laws of psychology: the ability to process information requests in “real time” - at the pace of the process of analytical understanding of the data by the user.

If a relational database can read about 200 records per second and write 20, then a good OLAP server, using calculated rows and columns, can consolidate 20,000-30,000 cells (equivalent to one record in a relational database) per second.

Visibility: It should be emphasized that OLAP provides advanced tools graphical representation data to the end user. The human brain is capable of perceiving and analyzing information that is presented in the form of geometric images, in a volume that is several orders of magnitude greater than information presented in alphanumeric form. Example: Let's say you need to find a familiar face in one of a hundred photographs. I believe this process will take you no more than a minute. Now imagine that instead of photographs you will be offered a hundred verbal descriptions of the same persons. I think that you will not be able to solve the proposed problem at all.

Simplicity: The main feature of these technologies is that they are intended for use not by a specialist in the field of information technology, not by an expert statistician, but by a professional in the applied field - a credit department manager, a budget department manager, and finally a director. They are designed for the analyst to communicate with the problem, not with the computer..

Despite the great capabilities of OLAP (in addition, the idea is relatively old - the 60s), its actual use is practically never found in our enterprises. Why?

• there is no information or the possibilities are not clear
• habit of thinking two-dimensionally
• price barrier
• excessive technological content of articles devoted to OLAP: unusual terms are frightening - OLAP, “data mining and slicing”, “ad hoc queries”, “identification of significant correlations”

Our approach and Western ones to the use of OLAP

In addition, we also have a specific understanding of the application utility of OLAP even while understanding its technological capabilities.

Our and Russian authors of various materials devoted to OLAP express the following opinion regarding the usefulness of OLAP: most perceive OLAP as a tool that allows you to expand and collapse data simply and conveniently, carrying out the manipulations that come to the analyst’s mind during the analysis process. The more “slices” and “sections” of data the analyst sees, the more ideas he has, which, in turn, require more and more “slices” for verification. It is not right.

The Western understanding of the usefulness of OLAP is based on a methodological analysis model that must be incorporated when designing OLAP solutions. The analyst should not play with the OLAP cube and aimlessly change its dimensions and levels of detail, data orientation, graphical display of data (and this really takes!), but clearly understand what views he needs, in what sequence and why (of course, the elements " there may be discoveries here, but it is not fundamental to the usefulness of OLAP).

Applications of OLAP

• Budget
• Flow of funds

One of the most fertile areas of application of OLAP technologies. It is not for nothing that no modern budgeting system is considered complete without the presence of OLAP tools for budget analysis. Most budget reports are easily built on the basis of OLAP systems. At the same time, the reports answer a very wide range of questions: analysis of the structure of expenses and income, comparison of expenses for certain items in different divisions, analysis of the dynamics and trends of expenses for certain items, analysis of costs and profits.

OLAP will allow you to analyze cash inflows and outflows in the context of business operations, counterparties, currencies and time in order to optimize their flows.

• Financial and management reporting (with analytics that management needs)
• Marketing
• Balanced Scorecard
• Profitability Analysis

If you have the appropriate data, you can find various applications of OLAP technology.

OLAP products

This section will discuss OLAP as a software solution.

General requirements for OLAP products

There are many ways to implement OLAP applications, so no particular technology should have been required, or even recommended. At different conditions and circumstances, one approach may be preferable to another. The implementation techniques include many different proprietary ideas that vendors are so proud of: variations of client-server architecture, time series analysis, object orientation, data storage optimization, parallel processes, etc. But these technologies cannot be part of the definition of OLAP.

There are characteristics that must be observed in all OLAP products (if it is an OLAP product), which is the ideal of the technology. This is 5 key definitions that characterize OLAP (the so-called FASMI test): Fast Analysis of Shared Multidimensional Information.

• Fast(FAST) means that the system should be able to provide most responses to users within approximately five seconds. Even if the system warns that the process will take significantly longer, users may become distracted and lose their thoughts, and the quality of the analysis will suffer. This speed is not easy to achieve with large amounts of data, especially if special on-the-fly calculations are required. Vendors resort to a wide variety of methods to achieve this goal, including specialized forms of data storage, extensive preliminary calculations, or tightening hardware requirements. However, there are currently no fully optimized solutions. At first glance, it may seem surprising that when receiving a report in a minute that not so long ago took days, the user very quickly becomes bored while waiting, and the project turns out to be much less successful than in the case of an instant response, even at the cost of less detailed analysis.
• Shared means that the system makes it possible to fulfill all data protection requirements and implement distributed and simultaneous access to data for different levels of users. The system must be able to handle multiple data changes in a timely, secure manner. This is a major weakness of many OLAP products, which tend to assume that all OLAP applications are read-only and provide simplified security controls.
• Multidimensional is a key requirement. If you had to define OLAP in one word, you would choose it. The system must provide a multidimensional conceptual view of data, including full support for hierarchies and multiple hierarchies, as this determines the most logical way to analyze the business. There is no minimum number of measurements to be processed as this is also application dependent and most OLAP products, has sufficient dimensions for the markets they target. Again, we do not specify what underlying database technology should be used if the user is to obtain a truly multidimensional conceptual view of the information. This feature is the heart of OLAP
• Information. Necessary information must be obtained where it is needed, regardless of its volume and storage location. However, a lot depends on the application. The power of various products is measured in terms of how much input data they can process, but not how many gigabytes they can store. The power of the products varies widely - the largest OLAP products can handle at least a thousand times more data than the smallest. There are many factors to consider in this regard, including data duplication, RAM requirements, disk space usage, performance metrics, integration with information warehouses, etc.
• Analysis means that the system can cope with any logical and statistical analysis, specific to a given application, and ensures that it is stored in a form accessible to the end user. The user should be able to define new custom calculations as part of the analysis without the need for programming. That is, all required analysis functionality must be provided in an intuitive way to end users. Analysis tools could include certain procedures, such as time series analysis, cost allocation, currency transfers, target searches, etc. Such capabilities vary widely among products, depending on the target orientation.

In other words, these 5 key definitions are the goals that OLAP products are designed to achieve.

Technological aspects of OLAP

An OLAP system includes certain components. There are various schemes for their operation that this or that product can implement.

Components of OLAP systems (what does an OLAP system consist of?)

Typically, an OLAP system includes the following components:

• Data source
  The source from which data for analysis is taken (data warehouse, database of operational accounting systems, set of tables, combinations of the above).
• OLAP server
  Data from the source is transferred or copied to the OLAP server, where it is systematized and prepared for faster generation of responses to queries.
• OLAP client
  User interface to the OLAP server in which the user operates

It should be noted that not all components are required. There are desktop OLAP systems that allow you to analyze data stored directly on the user's computer and do not require an OLAP server.

However, what element is required is the data source: the availability of data is important question. If they exist, in any form, like an Excel table, in a database accounting system, in the form of structured branch reports, the IT specialist will be able to integrate with the OLAP system directly or with intermediate conversion. OLAP systems have special tools for this. If this data is not available, or it is of insufficient completeness and quality, OLAP will not help. That is, OLAP is only a superstructure over the data, and if there is none, it becomes a useless thing.

Most data for OLAP applications originates in other systems. However, in some applications (for example, planning or budgeting), data can be created directly in OLAP applications. When data comes from other applications, it is usually necessary for the data to be stored in a separate, duplicate form for the OLAP application. Therefore, it is advisable to create data warehouses.

It should be noted that the term “OLAP” is inextricably linked with the term “data warehouse” (Data Warehouse). A data warehouse is a domain-specific, time-based, and immutable collection of data to support management decision-making. Data enters the storage from operating systems(OLTP systems), which are designed to automate business processes, the storage can be replenished from external sources, such as statistical reports.

Despite the fact that they contain obviously redundant information that is already in databases or operating system files, data warehouses are necessary because:

• fragmentation of data, storing it in various DBMS formats;
• data retrieval performance improves
• if in an enterprise all data is stored on a central database server (which is extremely rare), the analyst will probably not understand their complex, sometimes confusing structures
• complex analytical queries to operational information slow down the current work of the company, blocking tables for a long time and taking over server resources
• ability to clean and harmonize data
• it is impossible or very difficult to directly analyze data from operating systems;

The purpose of the repository is to provide the “raw materials” for analysis in one place and in a simple, clear structure. That is, the concept of Data Warehousing is not a concept of data analysis, rather it is a concept of preparing data for analysis. It involves the implementation of a single integrated data source.

OLAP products: architectures

When using OLAP products, two questions are important: how and where keep And process data. Depending on how these two processes are implemented, OLAP architectures are distinguished. There are 3 ways to store data for OLAP and 3 ways to process this data. Many manufacturers offer several options, some try to prove that their approach is the single most prudent one. This is, of course, absurd. However, very few products can operate in more than one mode efficiently.

OLAP data storage options

Storage in this context means keeping data in a constantly updated state.

• Relational databases: This is a typical choice if an enterprise stores accounting data in a RDB. In most cases, data should be stored in a denormalized structure (the most suitable is a star schema). A normalized database is not acceptable due to the very low query performance when generating aggregates for OLAP (often the resulting data is stored in aggregate tables).
• Database files on the client computer (kiosks or data marts): This data can be pre-distributed or created by queries on client computers.

Multidimensional Databases: This assumes that data is stored in a multidimensional database on a server. It can include data extracted and summarized from other systems and relational databases, end-user files, etc. In most cases, multidimensional databases are stored on disk, but some products allow you to use RAM, calculating the most frequently used data on the fly " Very few products based on multidimensional databases allow multiple editing of data; many products allow single editing but multiple readings of data, while others are limited to reading only.

These three data storage locations have various possibilities by storage volumes, and they are arranged in descending order according to capabilities. They also have different query performance characteristics: relational databases are much slower than the latter two options.

Options for processing OLAP data

There are 3 of the same data processing options:

• Using SQL: This option is, of course, used when storing data in a RDB. However, SQL does not allow multidimensional calculations in a single query, so it requires writing complex SQL queries to achieve more than basic multidimensional functionality. However, this doesn't stop developers from trying. In most cases, they perform a limited number of relevant calculations in SQL, with results that can be obtained from multidimensional data processing or from the client machine. It is also possible to use random access memory, which can store data using more than one query: this has dramatically improved response.
• Multidimensional processing on the client: The client OLAP product does the calculations itself, but such processing is only available if users have relatively powerful PCs.

Server-side multidimensional processing: This is a popular place to perform multidimensional calculations in client-server OLAP applications and is used in many products. Performance is usually high because most of the calculations have already been done. However, this requires a lot of disk space.

Matrix of OLAP architectures

Accordingly, by combining storage/processing options, it is possible to obtain a matrix of OLAP system architectures. Accordingly, theoretically there can be 9 combinations of these methods. However, since 3 of them lack common sense, in reality there are only 6 options for storing and processing OLAP data.

Multidimensional storage options data Options multidimensional data processing	Relational database	Server-side multidimensional database	Client computer
	Cartesis Magnitude
Multidimensional server processing	Crystal Holos (ROLAP mode) IBM DB2 OLAP Server CA EUREKA:Strategy Informix MetaCube Speedware Media/MR Microsoft Analysis Services Oracle Express (ROLAP mode) Pilot Analysis Server Applix iTM1	Crystal Holos Comshare Decision Hyperion Essbase Oracle Express Speedware Media/M Microsoft Analysis Services PowerPlay Enterprise Server Pilot Analysis Server Applix iTM1
Multidimensional processing on the client computer	Oracle Discoverer Informix MetaCube	Dimensional Insight Hyperion Enterprise	Cognos PowerPlay Personal Express iTM1 Perspectives

Since it is storage that determines processing, it is customary to group by storage options, that is:

• ROLAP products in sectors 1, 2, 3
• Desktop OLAP - in sector 6

MOLAP products – in sectors 4 and 5

HOLAP products (allowing both multidimensional and relational data storage options) – in 2 and 4 (in italics)

Categories of OLAP products

There are more than 40 OLAP vendors, although they cannot all be considered competitors because their capabilities are very different and, in fact, they operate in different market segments. They can be grouped into 4 fundamental categories, the differences between which are based on the following concepts: complex functionality - simple functionality, performance - disk space. It is useful to represent the categories in the shape of a square because it clearly shows the relationships between them. The distinctive feature of each category is represented on its side, and the similarities with others are represented on the adjacent sides, therefore, the categories on opposite sides are fundamentally different.

	Peculiarities	Advantages	Flaws	Representatives
Applied OLAP	Complete applications with rich functionality. Almost all require a multidimensional database, although some work with a relational one. Many of this category of applications are specialized, such as sales, manufacturing, banking, budgeting, financial consolidation, sales analysis	Possibility of integration with various applications High level of functionality High level of flexibility and scalability	Application complexity (need for user training) High price	Hyperion Solutions Crystal Decisions Information Builders
	The product is based on a non-relational data structure that provides multidimensional storage, processing and presentation of data. During the analysis process, data is selected exclusively from a multidimensional structure. Despite high level openness, suppliers persuade buyers to purchase their own tools	High performance (fast calculations of summary indicators and various multidimensional transformations for any of the dimensions). The average response time to an ad hoc analytical query when using a multidimensional database is usually 1-2 orders of magnitude less than in the case of an RDB High level of openness: a large number of products with which integration is possible Easily cope with the tasks of including various built-in functions in the information model, conducting specialized analysis and so on.	The need for large disk space to store data (due to redundancy of data that is stored). This is an extremely inefficient use of memory - due to denormalization and pre-executed aggregation, the volume of data in a multidimensional database corresponds to 2.5-100 times less than the volume of the original detailed data. In any case, MOLAP does not allow you to work with large databases data. The real limit is a database of 10-25 gigabytes The potential for a database “explosion” is an unexpected, sharp, disproportionate increase in its volume Lack of flexibility when it comes to modifying data structures. Any change in the structure of dimensions almost always requires a complete rebuild of the hypercube For multidimensional databases, there are currently no uniform standards for the interface, languages ​​for describing and manipulating data	Hyperion (Essbase)
DOLAP (Desktop OLAP)	Client OLAP products that are fairly easy to implement and have a low cost per seat We are talking about such analytical processing where hypercubes are small, their dimension is small, the needs are modest, and for such analytical processing a personal machine on a desktop is sufficient The goal of the producers of this market is to automate hundreds and thousands of jobs, but users must perform a fairly simple analysis. Buyers are often encouraged to buy more jobs than necessary	Good integration with databases: multidimensional, relational Possibility of making complex purchases, which reduces the cost of implementation projects Ease of use of applications	Very limited functionality(not comparable in this regard with specialized products) Very limited power (small data volumes, small number of measurements)	Cognos (PowerPlay) Business Objects Crystal Decisions
	This is the smallest sector of the market. Detailed data remains where it was originally - in the relational database; some aggregates are stored in the same database in specially created service tables	Capable of handling very large amounts of data (cost-effective storage) Provide a multi-user mode of operation, including editing mode, and not just reading Higher level of data protection and good options for differentiating access rights Frequent changes to the measurement structure are possible (do not require physical reorganization of the database)	Low performance, significantly inferior in terms of response speed to multidimensional ones (response to complex queries is measured in minutes or even hours rather than in seconds). These are better report builders than interactive analytics tools Complexity of products. Requires significant maintenance costs from information technology specialists. To provide performance comparable to MOLAP, relational systems require careful study of the database schema and setting up indexes, that is, a lot of effort on the part of database administrators Expensive to implement The limitations of SQL remain a reality, which prevents the implementation in RDBMS of many built-in functions that are easily provided in systems based on a multidimensional representation of data	Information Advantage Informix (MetaCube)

It should be noted that consumers of hybrid products that allow the choice of ROLAP and MOLAP mode, such as Microsoft Analysis Services, Oracle Express, Crystal Holos, IBM DB2 OLAPServer, almost always select MOLAP mode.

Each of the presented categories has its own strengths and weaknesses; there is no single optimal choice. The choice affects 3 important aspects: 1) performance; 2) disk space for data storage; 3) capabilities, functionality and especially the scalability of the OLAP solution. In this case, it is necessary to take into account the volume of data being processed, the power of equipment, user needs and seek a compromise between speed and redundancy of disk space occupied by the database, simplicity and multi-functionality.

Classification of Data Warehouses in accordance with the volume of the target database

Disadvantages of OLAP

Like any OLAP technology also has its drawbacks: high requirements for hardware, training and knowledge of administrative staff and end users, high costs for the implementation of the implementation project (both monetary and time, intellectual).

Selecting an OLAP product

Choosing the right OLAP product is difficult, but very important if you want the project to not fail.

As you can see, product differences lie in many areas: functional, architectural, technical. Some products are very limited in settings. Some are designed for specialized subject areas: marketing, sales, finance. There are products for general purposes, which do not have an application specific use, which must be quite flexible. As a rule, such products are cheaper than specialized ones, but the implementation costs are higher. The range of OLAP products is very wide - from the simplest construction tools pivot tables and charts included in office products, to data analysis and pattern-finding tools that cost tens of thousands of dollars.

As in any other field, in the field of OLAP there cannot be clear guidelines for choosing tools. We can only focus on a number of key points and match the software capabilities offered to the needs of the organization. One thing is important: without properly thinking about how you are going to use OLAP tools, you risk creating a major headache for yourself.

During the selection process, there are 2 questions to consider:

• assess the needs and capabilities of the enterprise
• evaluate the existing offer on the market, development trends are also important

Then compare all this and, in fact, make a choice.

Needs assessment

You can't make a rational product choice without understanding what it will be used for. Many companies want the “best possible product” without a clear understanding of how it should be used.

In order for the project to be successfully implemented, the financial director must, at a minimum, competently formulate his wishes and requirements to the manager and automation service specialists. Many problems arise due to insufficient preparation and awareness for the choice of OLAP; IT specialists and end users experience communication difficulties simply because they manipulate different concepts and terms during conversation and put forward conflicting preferences. There needs to be consistency in goals within the company.

Some factors have already become obvious after reading the overview of OLAP product categories, namely:

Technical aspects

• Data sources: corporate data warehouse, OLTP system, table files, relational databases. Possibility of linking OLAP tools with all DBMS used in the organization. As practice shows, the integration of heterogeneous products into a stable operating system is one of the most important issues, and its solution in some cases can be associated with big problems. It is necessary to understand how simply and reliably it is possible to integrate OLAP tools with the DBMS existing in the organization. It is also important to evaluate the possibilities of integration not only with data sources, but also with other applications to which you may need to export data: email, office applications
• Variability of data taken into account
• Server platform: NT, Unix, AS/400, Linux - but don't insist that OLAP-spec products run on questionable or dying platforms you're still using
• Client-side and browser standards
• Deployable architecture: the local network and PC modem connection, high-speed client/server, intranet, extranet, Internet
• International features: multi-currency support, multi-lingual operations, data sharing, localization, licensing, Windows update

Amounts of input information that are available and that will appear in the future

Users

• Area of ​​application: sales/marketing analysis, budgeting/planning, performance analysis, accounting report analysis, qualitative analysis, financial condition, generation of analytical materials (reports)
• Number of users and their location, requirements for the division of access rights to data and functions, secrecy (confidentiality) of information
• User type: senior management, finance, marketing, HR, sales, production, etc.
• User experience. User skill level. Consider providing training. It is important that the OLAP client application is designed so that users feel confident and can use it effectively.

Key features: need for write back data, distributed computing, complex currency conversions, report printing needs, spreadsheet interface, complexity of application logic, required dimensionality, types of analysis: statistical, target search, what-if analysis

Implementation

• Who will be involved in implementation and operation: external consultants, internal IT function or end users
• Budget: software, hardware, services, data transmission. Remember that paying for OLAP product licenses is only a small part of the total cost of the project. Implementation and hardware costs may be more than the license fee, and long-term support, operation, and administration costs are almost certainly significantly more. And if you made the wrong decision to buy the wrong product just because it's cheaper, you may ultimately end up with a higher overall project cost due to the higher cost. high costs maintenance, administration and/or hardware costs while you are likely to receive a lower level of business benefits. When estimating total costs, be sure to ask the following questions: How broad are the sources of implementation, training, and support available? Is the potential general fund (employees, contractors, consultants) likely to grow or shrink? How widely can you use your industrial professional experience?

Despite the fact that the cost of analytical systems remains quite high even today, and the methodologies and technologies for implementing such systems are still in their infancy, today the economic effect they provide significantly exceeds the effect of traditional operational systems.

The effect of proper organization, strategic and operational planning of business development is difficult to quantify in advance, but it is obvious that it can exceed the costs of implementing such systems by tens and even hundreds of times. However, one should not be mistaken. The effect is ensured not by the system itself, but by the people working with it. Therefore, declarations like: “a system of Data Warehousing and OLAP technologies will help the manager make the right decisions” are not entirely correct.” Modern analytical systems are not systems artificial intelligence and they can neither help nor hinder in making a decision. Their goal is to promptly provide the manager with all the information necessary to make a decision in a convenient form. And what information will be requested and what decision will be made based on it depends only on the specific person using it.

All that remains to be said is that these systems can help solve many business problems and can have a far-reaching positive effect. It remains to be seen who will be the first to realize the benefits of this approach and be ahead of the others.