The supportive role of Business Intelligence tools for the analysis of budget balance in EU countries in turbulent environments

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LUDOSŁAW DRELICHOWSKI, HUBERT ZARZYCKI, REMIGIUSZ LEWANDOWSKI, GRZEGORZ OSZUCIK

THE SUPPORTIVE ROLE OF BUSINESS INTELLIGENCE TOOLS FOR THE ANALYSIS OF BUDGET BALANCE IN EU COUNTRIES

IN TURBULENT ENVIRONMENTS

Summary

The adjustment processes of a global economy cause a number of disturbances, which have impacted several countries in the European Union. The two last years of economic turbulence reveal that a high standard of living in less prosperous EU member countries cannot be maintained due to imbalanced budget expenditures. The authors are convinced that monitoring is the most efficient tool for the EU member countries’ economic balance control. For the purpose of this monitoring study, a data warehouse was created with Business Intelligence tools and methods application (OLAP) to country positioning within the group members in subsequent years. In order to rank countries with respect to years, we utilized several World Bank indicators, e.g. GDP per capita, foreign investment value, grow to GDP ratio, unemployment, and inflation.

Keywords: economic development indicators, data warehouses, Business Intelligence tools 1. Introduction

The intensification of the threats for EUROZONE maintenance and the essence of functioning and effectiveness of the European Union, which have taken place over the last two months of the 2012, have tended to begin research on the EU member countries’ economic diagnosis methods. This can be a basis for the early warning system of the economic instability threat for EU members.

The authors believe that the results of the conducted research, which has been based on other evidence and have been presented in this paper, can state the starting point for the creation of an early warning system against the statistical data distortion risk (i.e. Hungary, Greece), requiring the integration of the EUROSTAT database and Business Intelligence methods application. The methods should also support the monitoring of the critical values of the key economic indicators. This article is a snapshot of a broader study reported in [8].

The idea that led to the search for methods of dynamic analysis of comparative economic development, which allows the objective assessment of accomplishments achieved by the governing parties within a specified period of time.

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Ludosław Drelichowski, Hubert Zarzycki, Remigiusz Lewandowski, Grzegorz OszuĞcik The supportive role of Business Intelligence tools for the analysis of economic development

in EU countries in turbulent environments 34

The immediate objective of the research was to make a more substantive than subjective assessment of government effectiveness (according to the governing party) by trying to undermine the opposition parties’ most often undisputed economic performance.

The choice of maximum standardized, and possibly automated analysis methods, are very important, included in the class of tools known as Business Intelligence and their application in the public administration was presented in the [19], [12] through the extensive computerization of this sector [3].

OLAP (online analytical processing) and data mining tools are the most popular and tested methods. The authors believe that they should provide the solution to the problem. The knowledge which has been acquired in this way plays a key role in the knowledge management process in public administration [21].

Another example of the multitude of data-mining application capabilities, according to the economic requirements, is presented by the investing risk assessment in certain countries based on economic indicators [2] or by the detection of companies’ fraudulent financial statements [11]. An excellent starting point for the identification of the country’s information structure in the global economy has been presented in the paper [13], which allows for the designation of criteria for the selection of research factors required for analysis. This paper [5] also corresponds to these issues by the education and personnel training problems according to the creation of a knowledge-based economy. The natural complement to this work has been presented in the publication [18], which carefully presents a step-change processes that involves the implementation of digital technology only to obtain information on the statistical reporting of the economic operators within the CSO system in Poland.

In the publication [1], an extremely important substantive experience of the comparative analysis of determinants of economic development for the EU member countries conductions have been presented. The wider context in the analysis of the impact of various aspects of contemporary reality of the strategic processes of creativity and innovation, education, and the impact of developmental challenges and crises have been presented in the following papers [14,15,16,17].

Among the social issues that have been considered, the results of successive election campaigns in Poland have been presented in papers [9] and [10].

The concepts of knowledge management in the conditions of occurrence of the phenomenon of the financial crisis in the economy as a methodological support for the decision makers who represent central and local governmental institutions have been discussed in [6, 7, 9].

We would find remarkable analysis in paper [20] of Bernard Madoff’s role and his ethical stance, according to the progression of the crisis phenomenon, which are representative of the many pathological phenomenons which occur in very important financial services sector.

The goals of this paper are inspired by the escalation of the crisis phenomena in the Greek economy, combined with the socially negative attitudes to refuse lower standards of living adequate for the productivity of the economy. This problem is a signal of demanding attitudes of societies in many EU countries that do not want to negotiate their own standard of living; this approach is supported by irresponsible propaganda usually created by the populist (perhaps not only) political parties and organizations. This condition indicates the need to use the latest tools to build models and decision support systems that allow for the creation of simplified models of planning budgets for individual EU countries directly supplied from the national statistical offices by quarterly statements of planned execution, which allows for assessment of the direction and

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occurring scope of deviations of planned execution. This type of monitoring approach, combined with expert interpretations – presented in an accessible way, would allow for future improvements to public awareness of the perceived negative effects of the socio-economic standard of living of citizens.

Creating model solutions containing the aggregated plans, budgets, and standards algorithmically related to the structure of income and expenditure, which together with selected synthetic performance indicators would offer plans available in high-quality corporate financial management monitoring systems. With awareness of the extraordinary complexity of the implementation of the above-formulated goals and challenges of modern economics, we can conclude that the creation of an instrument to objectively assess the achieved results, verified by the state of the economy of individual countries, is an essential starting point for building awareness of socio-economic conditions.

It is also necessary be aware of the inevitable current and future state of demographic processes, changes in economic conditions, and supply and commodity prices as an indicator of the standard of living of citizens. Methodological solutions presented in paragraph 2 and paragraph 3 of the selected parts of the compilation of results of the research, aim to identify some possible methodological and graphic presentations of the results. This will illustrate that it occurs over a longer period of time and will cause a rebalancing of the results of economic activities of countries included in the European Union.

The hypothesis of the authors assume the possibility of building a data warehouse and specialist business intelligence tools applicable within cooperation with academic economists, computer scientists, and computer companies, who specialize in software development and information processing systems to exchange data, for demanding changes in the implementation of monitoring systems in economy.

The goal achievement supports the objectification of the decision-making processes through the democratic electoral procedure and increases the awareness of the voting choice to select the governing party simply without subjective emotion or propaganda, which is an extremely popular practice by political parties in Poland.

Those premises initiated the development of a methodology for objectification of the governing parties’ assessment. The results should be published and should be available to society as an outcome of the quantitative methods approach, which constitutes the essence of building a data warehouse database of economic statistics comparing the countries and the principles of the ranking for each characteristic for all compared countries within the specified period of time.

2. Methodological assumptions of the studies

Presentation of analyzed problems is based on the results of research aimed at finding methodical solutions to allow a more objective assessment of the results of the parties (coalition) ruling the country. This process should be analyzed not only in terms of absolute values of economic indicators, but also based on the confrontation with varying ambient conditions. This result was achieved through the building of data warehouse and an evaluation of the effects obtained by the position of a country within the compared groups of the 27 EU countries.

The aim of this presentation will determine the application of business methodology intelligence according to a more objective comparison of the EU countries’ development.

Studies & Proceedings of Polish Association for Knowledge Management No. 58, 2012

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The source data, which is available and standardized on a global scale, shouldn’t be derived from EUROSTAT sources. According to the significant gaps in this database obtained from EUROSTAT, the data was acquired from World Bank and especially from WDI (World Development Indicators) and GDF (Global Development Finance).

The indicators that have been downloaded from WDI summaries are able to be transformed into the OLAP cubes.

After analysis of the diagnostic value of the applicable parameters, certain indicators have been proposed to be included in the data structure of the OLAP cube:

1. Annual GDP growth rate in the surveyed countries (%), 2. GDP per capita (including purchasing power),

3. Quantity of unemployed individuals in relation to the labor force (%), 4. The dynamics of trade balance per capita,

5. Foreign investment to GDP ratio (%), 6. Governmental debt to GDP ratio (%),

7. The dynamics of the value of fixed assets in the industry, 8. The inflation rate in the economy (%).

In the first phase, we include the parameterization of the OLAP cube database source, according to country parameters, economic performance, and data origin date. For the proper determinationing and analysis, the exported data (indicators) from MS Excel have been transferred into the individually-prepared MS SQL Server database. The database can use MS SQL Server versions from 2005. Construction of the database includes the ability to create unlimited analysis from the presentation of descriptive information in different languages. MS Excel sheets have a flat data structure (index, country, and the values for the various periods), which have been separated into tables for each type of data (indicators, countries, periods, the values of the indicators for individual countries and periods) and indexed. The total number of imported indicators was 1, 160 and the total number of imported values of those indicators was 590, 217 for each country and period.

In the second phase, we include a software simulation of the model calculations variants process and print the summaries (reports) that are also in the form of graphic illustrations. The standard tools, which were used for this purpose, allowed us to construct and present pivot tables and pivot charts based on OLAP cubes. The results were presented in the form of printouts and published in the websites which allowed both off-line and on-line communication with the database. The following tools (software) were used: MS Excel, MS Access, and MS Visual Studio. These tools can be replaced by other software that has features to use data sources such as MS SQL Server Crystal Reports, InfoMaker, or more advanced Business Intelligence presentation tools.

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3. Presentation of the statistical data and the results of the graphical synthesis of BI applications

The next four tables present selected economic parameters, starting from the level of GDP per capita, which illustrates the scale of changes of this important parameter in countries with the European integration processes. Distinctive beneficiaries of change have become only the countries in successive integration and a changing system of command economy-to-market economic model. The next three parameters of the rank illustrates the position of the country ordered in ascending or descending values of indicators presented in Table 2, 3. and 4. The header of Tables 1, 3, 5, and 7 shows the governing coalition parties in Poland to easier to visualize the results of governments during the term.

The four tables that are presented below summarize the values of GDP per capita of the EU countries (Table 1), which is the starting point for assessment changes in the years 1994–2009, according to the economic development.

Three tables include the position obtained by each country in the comparative assessment executed for the analyzed years. The authors decided to present the approach, which was used to analyze rank places based on data calculation from the ranked position of each EU country that may provide the benefit to find the solutions which can be applied on a global scale. Figure 1 is a graphic illustration of the synthetic results of the country positioning calculation for eight comparable economic parameters discussed in Paragraph 2 of this paper. In order to obtain a homogenous direction of positioned results for inverse correlated traits to the development, we multiplied the rank by minus one to assess the position of the country – for example, the position of country regarding the inflation level. Table 4 includes synthetic relative values (rank position of the countries for the 8 parameters within the range from 6 (most favorable) to 23 (lowest) (lowest score – Bulgaria, 1996 and Poland, 2001). The best value was reached by the Netherlands, Luxembourg, and Sweden, which reflects the level of their economic stability in the studied years. It is worthy to note that the same value of the synthetic measurement in a given year can have a number of countries, and the dispersion of the values contained in a much smaller range of values, which increases the stability assessment. Figure 1 presents the changes of Poland’s position [8], where the value was calculated by a synthetic measurement of development. The specified name in the graph’s columns represents the governing coalition. The investigation was undertaken in order to illustrate the concept of the application of BI tools to monitor the risks of the EU budget balance and to provide an objective comparative analysis of Polish economic development gained during several governmental coalitions.

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T a b le 2 . GDP p er c a p it a ( in cl u d in g p u rc h a si n g p o w er ) in 2 7 UE c o u n tr ie s fr o m 1 9 9 6 – 2 0 0 9 In d ic at o r C ount ry 1994/ S L D 1995/ S L D 1996/ S L D 1997/ S L D / A W S 1998/ A W S 1999/ A W S 2000/ A W S 2001/ A W S / S L D 2002/ S L D 2003/ S L D 2004/ S L D 2005S L D / P IS 2006/ P IS 2007/ P IS / P O 2008/ P O 2009/ P O GD P p er c apit a (i ncl ud in g p urc has in g p owe r) P o la n d 6 185. 7 7 422. 39 8 051. 21 8 788. 78 9 373. 90 9 905. 21 10 513. 94 10 953. 05 11 563. 13 11 980. 50 13 011. 50 13 784. 16 15 067. 96 16 695. 50 18 120. 72 19 058. 67 It a ly 20 113. 95 21 111. 15 21 801. 01 22 581. 22 23 72 3. 99 24 194. 21 25 594. 55 27 130. 08 26 803. 97 27 134. 14 27 411. 18 28 144. 01 30 232. 46 31 749. 28 32 694. 70 31 908. 63 B ul g a ri a 5 267. 57 5 553. 69 4 653. 49 4 862. 86 5 469. 73 5 665. 98 6 301. 36 6 854. 33 7 578. 57 8 212. 64 8 876. 49 9 809. 00 11 077. 24 12 287. 30 13 748. 49 13 332. 74 C ze ch R epu b li c 11 152. 87 12 812. 82 13 643. 99 13 828. 05 13 962. 01 14 312. 20 14 993. 03 16 176. 52 16 865. 57 17 979. 60 19 283. 28 20 362. 30 22 350. 49 24 534. 74 25 827. 72 25 231. 96 E st o ni a 5 796. 30 6 328. 86 6 939. 07 7 992. 67 8 462. 1 1 8 774. 88 9 881. 61 10 717. 48 11 989. 29 13 392. 98 14 778. 66 16 547. 99 18 925. 30 21 118. 90 21 643. 51 19 451. 43 Ir e la n d 16 139. 04 17 870. 13 19 491. 97 21 665. 09 23 982. 35 25 884. 36 28 638. 73 30 454. 97 33 000. 73 34 471. 74 36 434. 73 38 577. 83 42 143. 86 45 432. 00 43 657. 52 41 278. 25 G re ec e 14 193. 38 14 679. 40 15 177. 30 16 041. 82 16 5 06. 03 17 031. 27 18 412. 40 19 931. 84 21 597. 62 22 698. 88 24 154. 98 24 640. 49 26 994. 65 28 450. 61 30 285. 21 29 663. 38 S pa in 15 279. 73 15 989. 39 16 704. 75 17 696. 10 18 89 1. 35 19 824. 88 21 323. 21 22 595. 51 24 066. 53 24 745. 22 25 953. 54 27 376. 80 30 322. 99 32 209. 35 32 994. 37 32 544. 79 F ra n ce 19 466. 45 20 220. 85 20 806. 03 21 746. 24 22 793. 72 23 634. 84 25 328. 19 26 753. 96 27 888. 36 27 438. 45 28 339. 02 29 808. 72 31 649. 25 33 240. 28 34 619. 51 33 655. 49 L at vi a 5 215. 17 5 342. 27 5 702. 74 6 390. 91 6 929. 33 7 414. 93 8 030. 94 8 903. 17 9 869. 39 10 622. 46 11 7 39. 78 13 040. 17 14 989. 07 17 080. 47 18 399. 76 15 412. 84 L it hu a ni a 5 854. 16 6 216. 41 6 674. 91 7 312. 94 7 997 .41 7 999. 63 8 602. 29 9 554. 38 10 566. 26 12 032. 12 12 977. 16 14 196. 91 16 051. 04 18 185. 50 19 877. 28 16 747. 10 H unga ry 8 492. 19 8 809. 76 9 135. 39 9 761. 47 10 434. 39 11 011. 08 12 265. 65 13 517. 56 14 694. 25 15 485. 23 16 214. 57 16 955. 16 18 369. 19 19 261. 09 20 740. 13 19 764. 30 H o ll a n d 20 580. 87 21 552. 05 22 654. 55 24 095. 52 25 487. 18 26 939. 71 29 402. 81 30 787. 22 31 939. 76 31 694. 16 33 191. 21 35 104. 53 38 062. 16 40 520. 82 43 022. 05 40 714. 66 A u st ri a 22 509. 60 23 487. 99 24 298. 83 24 903. 16 26 057. 25 26 977. 28 28 773. 13 28 801. 19 30 225. 42 31 078. 40 32 571. 25 33 376. 79 36 215. 43 37 881. 67 39 846. 51 38 363. 11 P or tug a l 12 696. 51 13 475. 90 14 011. 17 14 895. 75 15 686. 79 16 699. 43 17 750. 86 18 467. 77 19 088. 30 19 389. 90 19 792. 74 21 294. 08 22 876. 27 23 981. 66 25 206. 21 24 569. 42 S w ede n 20 705. 54 21 855. 20 22 649. 04 23 482. 08 24 416. 27 25 978. 10 27 961. 35 28 242. 24 29 281. 05 30 421. 00 32 507. 07 32 723. 03 35 692. 03 38 427. 61 39 434. 51 37 904. 58 B e lgi u m 21 526. 61 22 450. 21 22 792. 44 23 825. 13 24 348. 37 25 323. 40 27 611. 66 28 476. 71 30 005. 04 30 228. 94 31 133. 73 32 126. 66 34 153. 02 35 487. 19 37 017. 90 36 249. 03 D an m a rk 21 960. 28 23 001. 38 24 051. 54 25 263. 00 26 149. 36 26 935. 35 28 829. 40 29 452. 75 30 766. 32 30 439. 64 32 306. 45 33 214. 41 36 034. 07 37 174. 80 38 566. 02 36 761. 67 C y p ru s 14 534. 00 15 457. 72 15 827. 78 16 284. 14 17 182. 01 17 982. 48 19 412. 12 20 931. 29 21 374. 10 21 803. 55 23 224. 57 24 407. 42 26 317. 81 28 189. 59 30 223. 42 R o m a ni a 4 914. 35 5 387. 33 5 739. 50 5 508. 75 5 341. 8 0 5 343. 39 5 653. 87 6 418. 74 7 012. 74 7 680. 57 8 737. 08 9 361. 16 11 131. 47 12 666. 38 15 100. 40 14 199. 11 L uxe m b u rg 38 146. 05 38 944. 25 40 228. 55 40 862. 63 43 254. 97 49 075. 15 53 652. 28 53 913. 55 57 549. 53 60 698. 86 64 968. 17 68 319. 19 79 005. 24 84 406. 48 88 775. 51 83 758. 81 M al ta 12 706. 76 13 623. 76 13 067. 46 14 022. 38 14 70 3. 71 15 550. 75 18 291. 06 17 941. 56 19 044. 22 19 208. 90 19 768. 72 20 831. 88 22 218. 75 23 256. 38 S lo v e ni a 11 891. 61 12 957. 41 13 728. 79 14 743. 76 15 579. 48 16 578. 55 17 474. 05 18 345. 32 19 712. 35 20 451. 62 22 199. 63 23 497. 72 25 451. 71 27 194. 32 29 212. 12 27 004. 41 S lo v ak ia 7 767. 17 8 307. 63 9 024. 81 9 739. 59 10 316 .72 10 399. 99 10 996. 94 12 064. 85 12 955. 51 13 585. 91 14 645. 51 16 163. 60 18 389. 52 20 746. 04 23 201. 68 22 356. 26 F inl a n d 17 811. 23 18 787. 00 19 243. 75 20 934. 94 22 551. 62 23 594. 39 25 653. 16 26 522. 53 27 511. 23 27 588. 75 29 845. 22 30 684. 13 33 104. 51 36 190. 69 37 625. 41 34 719. 74 U K 18 796. 50 19 718. 06 20 937. 83 22 420. 97 23 300. 02 24 249. 60 26 071. 61 27 584. 68 28 885. 32 29 839. 07 31 767. 31 32 730. 65 34 900. 59 35 780. 42 37 317. 32 36 495. 76 G er m a ny 21 664. 96 22 497. 80 23 051. 67 23 573. 68 24 244. 71 25 141. 67 25 945. 29 26 861. 68 27 578. 46 28 556. 03 29 889. 76 31 363. 52 33 718. 12 35 563. 35 36 922. 32 36 267. 41

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T a b le 3 . P o li sh p o si ti o n a cc o rd in g t o 2 7 E U co u n tr ie s in 1 9 9 6 – 2 0 0 9 f o r GDP p er c a p it a v a lu e (i n cl u d in g p u rc h a si n g p o w er ) GD P p er ca pit a r an k ( in clu din g p urc ha sin g p ow er ) P o la n d 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 4 2 3 2 4 2 4 2 5 2 4 2 2 It al y 8 8 8 8 9 1 0 1 1 9 1 2 1 2 1 2 1 2 1 3 1 3 1 3 1 4 B u lg ar ia 2 5 2 5 2 7 2 7 2 6 2 6 2 6 2 6 2 6 2 6 2 6 2 6 2 7 2 7 2 7 2 7 C ze ch R ep u b li c 1 9 1 9 1 8 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 8 1 7 1 7 1 7 E st o n ia 2 4 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 2 2 2 2 1 2 1 2 0 2 0 2 0 2 1 Ir el an d 1 2 1 2 1 1 1 1 8 6 5 3 2 2 2 2 2 2 2 2 G re ec e 1 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 5 S p ai n 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 2 1 2 1 2 1 2 F ra n ce 9 9 1 0 1 0 1 1 1 1 1 2 1 1 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 L at v ia 2 6 2 7 2 6 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 4 2 3 2 4 L it h u an ia 2 3 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 3 2 4 2 3 2 3 2 3 2 2 2 3 H u n g ar y 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 2 2 2 2 1 2 0 H o ll an d 7 7 6 4 4 3 2 2 3 3 3 3 3 3 3 3 A u st ri a 2 2 2 3 3 2 4 5 5 4 4 4 4 5 4 4 P o rt u g al 1 7 1 7 1 6 1 6 1 6 1 6 1 7 1 6 1 7 1 7 1 7 1 7 1 7 1 8 1 8 1 8 S w ed en 6 6 7 7 5 5 6 7 7 6 5 7 6 4 5 5 B el g iu m 5 5 5 5 6 7 7 6 6 7 8 8 8 1 0 9 9 D an m ar k 3 3 3 2 2 4 3 4 4 5 6 5 5 6 6 6 C y p ru s 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 5 1 5 1 5 1 5 1 5 1 5 1 5 R o m an ia 2 7 2 6 2 5 2 6 2 7 2 7 2 7 2 7 2 7 2 7 2 7 2 7 2 6 2 6 2 5 2 5 L u x em b u rg 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 M al ta 1 6 1 6 1 9 1 8 1 8 1 8 1 6 1 8 1 8 1 8 1 8 1 8 1 9 1 9 S lo v en ia 1 8 1 8 1 7 1 7 1 7 1 7 1 8 1 7 1 6 1 6 1 6 1 6 1 6 1 6 1 6 1 6 S lo v ak ia 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 2 2 2 2 1 2 1 1 9 1 9 F in la n d 1 1 1 1 1 2 1 2 1 2 1 2 1 0 1 2 1 1 1 0 1 0 1 0 1 0 7 7 1 0 U K 1 0 1 0 9 9 1 0 9 8 8 8 8 7 6 7 8 8 7 G er m an y 4 4 4 6 7 8 9 1 0 1 0 9 9 9 9 9 1 0 8

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T a b le 4 . P o li sh p o si ti o n a cc o rd in g t o 2 7 E U co u n tr ie s in 1 9 9 6 – 2 0 0 9 f o r GDP p er c a p it a g ro w th ( % ) In d ic at o r C o u n tr y 1 9 9 4 /S L D 1 9 9 5 /S L D 1 9 9 6 /S L D 1 9 9 7 /S L D / A W S 1 9 9 8 /A W S 1 9 9 9 /A W S 2 0 0 0 /A W S 2 0 0 1 /A W S / S L D 2 0 0 2 /S L D 2 0 0 3 /S L D 2 0 0 4 /S L D 2 0 0 5 /S L D / _PIS 2 0 0 6 /P I S 2 0 0 7 /P IS / _PO 2 0 0 8 /P O 2 0 0 9 /P O GD P p er c apit a gr owth (% ) P o la n d 7 3 3 5 9 9 1 4 2 3 2 0 1 0 6 1 3 8 6 4 1 It al y 2 2 1 8 2 4 2 3 2 5 2 2 1 9 2 0 2 5 2 4 2 5 2 7 2 6 2 6 2 3 1 6 B u lg ar ia 2 4 1 7 2 7 2 6 1 0 2 1 7 7 6 5 5 5 7 8 2 1 5 C ze ch R ep u b li c 1 8 6 6 2 5 2 6 2 3 2 2 1 5 1 7 1 1 1 1 6 6 9 8 1 0 E st o n ia 2 6 8 5 2 4 2 5 1 2 1 2 3 2 2 4 2 7 2 4 Ir el an d 4 1 1 1 1 1 2 4 3 8 1 0 7 1 1 1 0 2 4 2 0 G re ec e 2 3 2 3 1 7 1 6 2 1 1 6 1 1 6 1 1 4 9 2 0 1 2 1 3 1 0 2 S p ai n 1 7 1 9 1 6 1 4 1 2 5 9 1 0 1 2 1 2 1 7 1 4 1 6 1 8 1 5 6 F ra n ce 1 9 2 2 2 3 2 1 1 9 1 8 1 8 1 9 2 2 1 9 2 1 2 3 2 5 2 4 1 8 4 L at v ia 2 1 2 7 9 3 1 1 6 4 1 4 3 1 1 1 2 2 5 2 7 L it h u an ia 2 7 1 3 4 4 2 2 6 2 4 3 2 1 4 3 5 3 7 2 5 H u n g ar y 1 5 2 5 2 5 1 2 5 1 0 6 8 8 9 8 1 2 1 7 2 7 1 6 1 9 H o ll an d 1 4 1 4 1 3 1 3 1 5 7 1 5 1 8 2 6 2 3 2 3 2 2 2 1 1 7 1 1 8 A u st ri a 2 0 2 0 1 8 2 2 1 7 1 7 2 1 2 6 1 9 2 0 2 0 1 8 1 8 1 6 9 7 P o rt u g al 2 5 9 1 0 1 1 6 1 1 1 6 1 7 2 3 2 7 2 4 2 5 2 7 2 3 2 0 3 S w ed en 9 1 1 2 0 1 9 1 4 8 1 2 2 1 1 4 1 5 1 4 1 6 1 4 1 9 2 1 1 7 B el g iu m 1 3 2 1 2 2 1 5 2 4 1 4 2 0 2 4 2 1 2 1 1 8 2 4 2 4 2 0 1 2 5 D an m ar k 5 1 5 1 5 1 8 2 2 1 9 2 3 2 5 2 4 2 2 2 2 1 9 2 0 2 5 2 2 1 2 C y p ru s 2 5 1 9 2 0 7 4 1 0 9 1 5 1 7 1 5 1 1 1 5 1 4 5 R o m an ia 1 0 2 7 2 7 2 7 2 7 2 6 5 5 6 2 1 0 4 1 1 1 2 3 L u x em b u rg 1 1 2 6 2 1 7 3 2 3 1 3 9 1 8 1 2 8 1 0 7 1 9 9 M al ta 3 4 8 1 0 2 0 1 2 5 2 7 1 3 2 6 2 7 1 5 1 9 1 5 S lo v en ia 6 1 2 1 2 9 1 8 3 1 3 1 2 1 0 1 3 1 3 9 9 5 6 2 1 S lo v ak ia 1 7 2 8 1 3 2 4 2 7 1 1 7 7 7 4 3 1 3 1 8 F in la n d 1 2 1 0 1 1 6 8 1 3 8 1 6 1 8 1 6 1 6 1 7 1 3 1 2 1 4 2 2 U K 8 1 6 1 4 1 7 1 6 1 5 1 7 1 4 1 6 1 4 1 9 2 1 2 3 2 2 1 7 1 4 G er m an y 1 6 2 4 2 6 2 4 2 3 2 0 2 5 2 2 2 7 2 5 2 6 2 6 2 2 2 1 1 3 1 1

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T a b le 4 . P o li sh p o si ti o n a cc o rd in g t o 2 7 E U co u n tr ie s in 1 9 9 6 – 2 0 0 9 f o r th e se le ct ed i n d ic a to rs c re a ti n g fr o m a r a n g e o f se ve n p a ra me te rs C o u n tr y 1 9 9 4 / S L D 1 9 9 5 / S L D 1 9 9 6 / S L D 1 9 9 7 / S L D / A W S 1 9 9 8 / A W S 1 9 9 9 / A W S 2 0 0 0 / A W S 2 0 0 1 / A W S / S L D 2 0 0 2 / S L D 2 0 0 3 / S L D 2 0 0 4 / S L D 2 0 0 5 / S L D / P IS 2 0 0 6 / P IS 2 0 0 7 / P IS / P O 2 0 0 8 / P O 2 0 0 9 / P O P o la n d 1 5 1 5 1 4 1 5 1 7 1 8 2 1 2 3 2 2 1 9 1 8 1 8 1 5 1 7 1 6 1 3 It al y 1 7 1 6 1 8 1 7 1 8 1 6 1 6 1 6 1 7 1 8 1 7 1 8 1 9 1 8 1 7 1 3 B u lg ar ia 2 1 2 0 2 3 2 1 1 7 1 8 1 7 1 7 1 8 1 6 1 6 1 6 1 7 1 6 1 4 1 8 C ze ch R ep u b li c 1 2 9 1 1 1 7 1 7 1 5 1 6 1 4 1 2 1 3 1 4 1 2 1 3 1 2 1 3 1 1 E st o n ia 1 3 1 5 1 3 1 1 1 1 1 9 1 3 1 3 1 3 1 0 1 4 1 2 1 1 1 3 1 7 1 5 Ir el an d 1 1 1 0 9 9 7 6 7 8 7 7 8 7 1 1 1 0 1 4 9 G re ec e 2 0 1 9 1 8 1 8 1 8 1 9 1 7 1 7 1 8 1 7 1 9 2 2 1 8 2 0 1 9 1 5 S p ai n 1 9 1 9 2 0 1 9 1 8 1 7 1 7 1 6 1 7 1 7 1 9 1 8 1 8 1 9 1 8 1 5 F ra n ce 1 4 1 4 1 6 1 5 1 5 1 3 1 5 1 3 1 5 1 4 1 7 1 7 1 8 1 8 1 6 1 0 L at v ia 1 6 1 6 1 5 1 4 1 5 1 7 1 4 1 5 1 4 1 4 1 4 1 5 1 3 1 5 2 0 2 3 L it h u an ia 2 1 1 9 1 5 1 4 1 3 1 8 1 9 1 2 1 2 1 3 1 3 1 4 1 2 1 3 1 6 2 1 H u n g ar y 1 6 1 9 1 8 1 5 1 5 1 6 1 5 1 5 1 5 1 7 1 5 1 6 1 8 1 9 1 7 1 9 H o ll an d 9 9 7 9 8 6 7 9 1 1 1 0 1 1 8 9 6 6 8 A u st ri a 1 2 1 3 1 2 1 4 1 3 1 2 1 3 1 5 1 3 1 0 1 3 1 1 1 3 1 0 8 9 P o rt u g al 1 5 1 5 1 5 1 2 1 2 1 4 1 4 1 5 1 7 1 7 1 8 1 8 1 9 2 0 1 6 1 2 S w ed en 7 7 1 0 1 1 9 6 7 1 1 1 0 1 1 8 9 8 1 0 9 1 0 B el g iu m 1 3 1 3 1 4 1 2 1 4 1 2 1 2 1 3 1 3 1 3 1 1 1 4 1 5 1 3 1 4 1 1 D an m ar k 5 7 1 1 1 0 1 0 1 0 8 1 1 1 2 1 3 1 1 8 9 1 2 1 1 1 3 C y p ru s 1 4 1 2 1 3 1 4 1 3 1 1 1 3 9 1 1 1 3 1 0 1 3 1 0 1 1 9 1 7 R o m an ia 1 4 1 4 1 5 1 6 1 9 1 8 1 9 1 5 1 7 1 5 1 5 1 8 1 6 1 6 1 4 1 9 L u x em b u rg 9 1 2 1 0 8 9 6 9 8 8 1 0 1 0 1 0 1 0 7 1 0 1 0 M al ta 1 3 1 3 1 2 1 5 1 5 1 3 1 1 1 6 1 7 1 4 1 6 1 5 1 5 1 4 1 6 1 8 S lo v en ia 1 4 1 3 1 4 1 3 1 6 1 4 1 8 1 6 1 3 1 5 1 3 1 3 1 2 1 3 1 1 1 8 S lo v ak ia 1 5 1 7 1 3 1 6 1 8 2 2 2 1 1 7 1 6 2 0 1 7 1 5 1 6 1 5 1 4 1 8 F in la n d 1 4 1 2 1 2 1 1 1 0 1 3 1 1 1 3 1 2 1 2 1 2 1 2 1 3 1 1 1 3 1 4 U K 1 4 1 5 1 3 1 4 1 3 1 3 1 4 1 2 1 3 1 4 1 3 1 4 1 4 1 5 1 4 1 4 G er m an y 1 2 1 3 1 4 1 4 1 4 1 2 1 2 1 4 1 4 1 5 1 6 1 6 1 4 1 6 1 2 9

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in EU countries in turbulent environments

Figure 4. The average value of Poland compared to 17 EU countries in 1996–2009 for selected indicators

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4. The summary and analysis results of implementation. Threats and opportunities for the effects of research in the field of comparative analysis support for the economic development of countries with the BI methods

The comparison of the economic development of countries may be a basis of interesting analysis in the field of economic dynamics on a regional scale, such as: Central and Eastern Europe, South America, Central America, etc. Another variant of this analysis can be realized in the area of the political and economic Union, which can be observed in the structures of the constantly expanding European Union. The intensification of the global crisis phenomenon that increases the stability risk of the EU monetary system (Euro zone) requires the use of constantly-improving tools for comparative analysis and indicators balancing, which would support the biased statistical information identification (The last EU Commission Summit clearly confirms that priority). The gaps in Belgium’s statistical data in recent years (caused by the governmental crisis of the last 3 years) are an example of unacceptable defects in the functioning of such an important mechanism for supplying information about the EU economy. It directly causes the inability to provide a current flow and inter-branch balance sheets, as well as the other fully-equivalent methods of comparative analysis. The objectification of economic information which is usually biased during political campaigns by many political parties (which seems to be a widespread practice in Poland – but maybe and not only) was the main premise of this research.

The analysis of the developed approach provided very interesting results at the international level. Ireland, which has excellent results for GDP growth and GDP per capita level and obtained the top position in both rankings multiple times, couldn’t protect itself from the crisis and required the World Bank’s support. The advancement of the Greece from 8th place in 2007 and 2008 to 2nd place in 2009 in the rank of GDP growth can be surprising in reference to the subsequent EU support requirements. It can be explained by the biases of statistical information that was provided by Greece to EUROSTAT and the World Bank which was indicated by the experts. The comparative analysis of the ranking of the 8 important parameters of the economic development of EU countries, according to the methodology which was proposed in this research, can play an important role in the development of economic awareness of all entities that influence the assessment of economic development. It also can be an important tool for diagnosis of the crisis phenomenon which threatens the countries that function in different economic unions. The EU countries’ support (in the amounts of hundreds of billions of Euro) that is provided for the countries which are affected by the financial crisis (i.e. Greece, Ireland, Portugal) will require the improvement of the diagnostic engine.

All of the entities have to be aware of negative consequences that can be caused by the exchange of an efficiently governing party, to a group that declares the well-being of society, without their competence guarantee (populists). An equally important threat is the decision to retain the governing party that does not comply with the parameters of the maintenance of good developmental trends – Polish voters decided in 2011 to retain the governing party for the first time since 1990). The responsibility awareness of all the entities of the parliamentary democracy system for their decisions, in terms of occurrence of the crisis, is also very important (Greece, Ireland, and Portugal can be a warning). The supporting amounts reached limits of 120 billion Euros and can increase in the near future. The repayment will cause unpleasant consequences for the quality of life for citizens.

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It seems reasonable to take broader action research on the possibility of using different methods and tools supporting Business Intelligence for the analysis of the socio-economic development of different countries and different economic unions. Complex processes occur in the global economy that is affected by the significant scale of changes associated with the expansion of the virtualization process resulting from the growing share of financial instruments in its functioning. This is the area which states one of the factors which contributed to the impact of the suspension of critical regulators of economic equilibrium of countries but on the other hand has become a graceful instrument of international financial speculation. The complexity level of the problem is further complicated by the process of making a global rearrangement of the world economic centers with the growing share of Chinese, Indian, and Brazilian economies in GDP product and in the supply creation of consumer goods for the world market.

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ZASTOSOWANIE NARZĉDZI BUSINESS INTELLIGENCE DO ANALIZY RÓWNOWGI BUDĩETOWEJ W KRAJACH UE

W WARUNKACH WYSTĉPOWANIA ZAKŁÓCEē

Streszczenie

Procesy dostosowawcze globalizacji gospodarki powodują zakłócenia, które zaistniały ostatnio w krajach Unii Europejskiej. Dwa ostatnie lata zakłóceĔ gospodarki sprawiają, Īe wysoki standard Īycia społeczeĔstw niĪej rozwiniĊtych członków UE, moĪe powodowaü naruszenie równowagi budĪetowej. Autorzy są przekonani, Īe monitoring z zastosowaniem zaawansowanych narzĊdzi, moĪe stanowiü podstawĊ sterowania równowagą budĪetową krajów członkowskich UE. PodstawĊ do konstruowania rozwiązaĔ monitoringu równowagi budĪetowej mogą stanowiü doĞwiadczenia autorów uzyskane z badaĔ zastosowaĔ hurtowni danych oraz narzĊdzi OLAP do analizy porównawczej dynamiki rozwoju krajów UE w ostatnich latach. Do wyznaczania pozycji kraju w poszczególnych latach wykorzystano nastĊpujące informacje z banku danych Banku ĝwiatowego: poziom PKB na osobĊ, wartoĞü inwestycji zagranicznych, wskaĨnik wzrostu PKB na osobĊ, WskaĨnik bezrobocia, inflacja.

Słowa kluczowe: wska:niki rozwoju gospodarczego, hurtownia danych, narzdzia Business

Intelligence

Ludosław Drelichowski Remigiusz Lewandowski Hubert Zarzycki

Management Information Systems

University of Technology and Life Sciences in Bydgoszcz, Poland ul. Fordo%ska 430, 85-790 Bydgoszcz, Poland

e-mail: lu.drel@utp.edu.pl Grzegorz Oszucik SofTeam Sp. z o.o., Poland