Business Analytics Case Study

The ESIF is carried out by the Directorate-General for Regional and Urban Policy and recommends large financial assistance to member states of the European Union for regional and infrastructural development. The ESIF functions in the various sectors to address the issues affecting competitiveness innovation and the economic socio-balance towards focusing on the developmental regions.

The company selected for the analysis, although does not function as a typical commercial business, is instrumental in the delivery of the EU funding to support projects cutting across the sectors such as SMEs, innovation, and low-carbon economy. The data under consideration refers to some using information from the ESIF concerning the funding period of 2014-2020 and displaying forecasted and implemented values of these types of projects, which would shed light upon the effectiveness of financial support turning into real outcomes.

The key importance is placed on business analytics to determine the measures which can be used to optimize the usage of the ESIF funds and find out which areas require concentrated efforts to increase the performance of the projects. To achieve this the analysis will seek to identify the regions or sectors that require financial support through the number of enterprises supported, the funds invested as well as the progress made towards achieving targets. Analyzing the data using business analytics provides important data on resource management, and project status, and to pinpoint inefficiencies in the application of the project. Such findings described above can be useful for the Directorate-General for Regional and Urban Policy to readjust its funding approach so that future investments would better cater to regional development and sustainability objectives.

Problem Identification

The Directorate-General for Regional and Urban Policy acts in front of the challenge of managing and distributing the ESIF and using them as suitable instruments for the conception of a variety of socio-economic effects. One has regularly observed financial frictions in that forecasted values are often significantly different from the values that are implemented for funded projects. Some of the programs under the ESIF especially those that focus on funding SMEs, innovation, and regional competitiveness always miss their set objectives within the planned timelines.

This difference between input/output and actual results is very relevant to the Directorate-General, for it determines the success of EU fund investment in the region's development. So this problem may cause underutilization of funding, delay in fulfilling the goals and objectives on the regional level and lack of quality of resource distribution, particularly in the strategic areas of interest in the region including low carbon economy and SMEs.

From the analysis of the data, it was revealed that some projects fail to achieve their goals and objectives with the number of enterprises targeted and research and innovation activities. Also important is rectifying this reality so that EU funding results in genuine improvements in regions’ economic competitiveness, social inclusion, and sustainability. The above challenge is pivotal to determining the success of future investment cycles to enable funding to have its optimum impact.

Objectives

Enhance Fund Utilization Efficiency: Detect which areas are underutilising the ESIF funds and ensure the best matching with projects and geographical locations that may need ESIF funds more intensively.

Improve Target Achievement Rates: Increase delivery of projects with a percentage of growth to their target forecast especially in areas like SME innovation.

Refine Project Monitoring and Reporting: Improve the establishment of data collection and monitoring cycles which give up-to-date information on the performance of projects.

Literature Review

Business Analytics in Decisions for the Allocation of Resources in Strategic Planning

According to Kurpiela and Teuteberg, (2023), business analytics plays a central function in improving strategic decision-making, concerning the allocation of funds. They state that the integration of BA tools into the fund management processes enhances the decision-making quality since it helps in the apt prediction of the result of investments and modification of the implemented strategies for enhanced effect. According to Ibeh et al., (2024), the study established that BA tools enhance the openness of an organisation, unite the departments of the organisation, and develop procedural approaches whenever decisions are being made. According to the BA approach, it is possible to maintain the reduction of funds traceable regarding certain work plans systematically, following and contrasting the goals’ and achievements’ targets, as well as making adequate business changes to ensure goals’ accomplishment in time. This approach is quite helpful especially when it comes to the management of the ESIF since it might help arrive at better outcomes in the utilisation of the funds for the support of different economic developmental projects (Kurpiela and Teuteberg, 2023).

Predicting the financial data in public sector projects by machine learning

According to Wang, Shao and Robert, (2021), the business analytics system to forecast the financial performance of public sector projects. They demonstrate in the course of their research that by using the algorithms of machine learning, they are capable of predicting future returns and, in addition, controlling the effectiveness of previous investments through the analysis of datasets such as the ESIF (Bouchetara et al., 2024). By using predictive models, can filter down the most favoured projects, thus reducing human interference and biasing. It does so through the distribution of funds in the most efficient way possible, with the agency bearing responsibility for supporting sustainable economic growth; setting the priorities which correspond to regional developmental goals and excluding political factors which often interfere with the most rational, evidence-based funding decisions (Wang et al., 2021).

Business Analytics in Public Investment Programs

According to Yin and Fernandez, (2020) The application of Business Analytics in the administration of big-scale public investment, including ESIF. They underscore that the adoption of analytics in the management of the public sector enhances the accountability of ongoing projects. Real-time analysis of big data enables decision-making in a way that will help project sponsors detect the projects which are underperforming, and as a result, redirect their funds to areas such as infrastructure for small and medium businesses for maximum returns (Prodi et al., 2017). From their research, they postulate that Business Analytics provides sustainable advantages and enables governments to respond effectively to changes in the economy and citizens’ requirements hence improving the effectiveness of investment programs (Yin and Fernandez, 2020).

Selective Investment to be Driven by Data for Regional Growth

According to Cataldo and Monastiriotis, (2018), there are advantages to using data analysis techniques on the fund for the development of the regional economy. It also illustrates their research about how the analysis of trends and patterns of the performance of a project is useful in determining which investment is more profitable. When applied in the prioritisation of investment decisions, the BA methods may help in the direct injection of funding to projects that will yield the most value, in optimising resources especially in areas of the globe that are still developing. They agree with their work helping improve decision-making under programmes such as the European Cohesion Policy by extending focus on KPIs or performance of investment against strategic economic objectives (Cataldo and Monastiriotis, 2018).

Data Collection

Data Sources

The data was drawn from the European Structural and Investment Funds (ESIF) 2014-2020 Achievement dataset. This dataset we have obtained from the Directorate-General for Regional and Urban Policy of the European Union contains specific data about various programs funded in Europe including the results. Most of the cop funds include the European Regional Development Fund (ERDF), the European Social Fund (ESF), and the Cohesion Fund (CF). The dataset records the forecasted and the actual outcomes attained through these funding programs and hence can be of immense value in assessing the effectiveness of the funding programs (Europa. eu, 2024).

Rationale for Data Selection

The suitability of the ESIF dataset for the business problem arises from the fact that it provides encompassing and detailed information. It also contains time series data covering more than one year, which can be used as a sound framework for evaluating the level of fund utilisation and the level of achievement of the set outcomes (Europa. eu, 2024). The analysis is made possible based on the frequent update of the data set which enhances the reliability of the result obtained from the analysis. It is concise and has detailed records to enable an assessment to be made about the extent to which the funding program achieves its intended goals and supports regional growth and development.

Data Collection and Processing

The information was collected from the official data source of the European Union which was open to the public. As for the source of data, the data in the present study has been obtained in raw format and to that some preprocessing was done. This includes pre-processing of the data where disparate data had to be made consistent and formatted correctly as well as the removal of noise from the data set (Europa. eu, 2024). Further PIF processing activity included grinding and seasoning data to enable analytics. The last data set was designed in such a way that it could compare predicted goals and/or objectives with actual outcomes to facilitate the evaluation of the efficiency of the EU funding programmes. Such an approach helps ensure that the data collected and processed will allow the analysis to provide a comprehensive evaluation of the outcome of using EU funding for regional development as well as the strengths and weaknesses that have been observed.

Data Analysis

Target Value Distribution by Member States

Figure 1 Target Value Distribution by Member States

Figure 2 Table for value Distribution

The distribution of the target values across member states reveals a high degree of variation, Cyprus and Poland show high spikes which perhaps means they get more portions or have bigger targets. On the other hand, the countries with lower target values assigned to them include Austria, Ireland and Lithuania. The dips for Cyprus and Poland are suggestive of much larger projects or of other more important funding attachments. Though it is seen that there is inequality in providing funding to countries, most of the countries fall in moderate type of funding. Such difference might be attributed evolved from the difference in national approach or project scale.

Analysis of Fund Allocation

Figure 3 Analysis of Fund Allocation

The pie chart shows the total target value achieved in the dispersion between the organizational units, specifically the ERDF and the ESF. The largest share goes to the ERDF, which has a value of four above, besides, as already mentioned, the ERDF is a source of multifunctional cooperation. We have estimated the funding to be at 58E+11 since it is primarily responsible for facilitating the development of developmental projects within the member states. On the other hand, the ESF is funded less but still rather generously at per cent 138 million mainly concentrating its activities in the sphere of social projects and the provision of workplaces.

Figure 4 Table for Target value of fund

Number of entries by Region

Figure 5 Table for number of entries by region

The plot shows a higher count in more and less developed regions across all classes of the indicators with 64755 and 61317 entries in OESF respectively. Following them, transition regions present themselves with massive numbers in OESF as well as RESF. REACT-EU is even lower and the Outermost/Northern Sparsely Populated regions with very minimal representation in almost all the categories. Hence, OESF is the most prominent ID across regions owing much to the most massive entries of data.

Figure 6Number of entries by region

Geographical fund Comparisons

Figure 7 Geographical fund comparisons

This table shows the financial allocations to European countries from two EU funds (ERDF and ESF). In this file, a country Part Mark receives and provides (separately For Gordon) funding, with subsequent columns showing the amount each fund gives to that country individually and combined. Austria obtained €120.55 billion in ERDF and €182.7 million in ESF which add up to €120.56 billion. The ERDF Germany received €333.49 billion and from ESF €222.13 billion, a total of €555.1 billion. The grand totals across the bottom are for all countries together €4.582 trillion from ERDF €1.387 trillion from ESF and €4.596 trillion. Overall.

Figure 8 Table for geographical fund comparisons

Comparison of investment Priorities

Figure 9 Comparison of investment Priorities

The investment priorities are presented by the count of measure codes in the same array; there are three major peaks with values over 20,000 located at priorities 10, 27, and 95. Other priorities have far less representation, many falling significantly below 10,000, indicating an unclear level of emphasis. The steep rise at some of the points proves the focused spending on specific areas while the rest of the activities remain of low concern.

Figure 10 Table for comparisons of investment priorities

A sum of different values by Year wise in different business

Figure 11 the sum of different values by Year wise in different business

Figure 12 the sum of different values by Year wise in different business

The pivot table summarizes various metrics like Enterprises, EUR, CO2 emissions, energy, population, etc., from 2015 to 2023. Each row represents a different metric whereas each column represents a specific yearly metric. Values within the table show the sum of target values Depending on the metric and year, respectively. The last column, "Grand Total," represents the sum of the values of overall years for each metric. The table gives a better representation of the time-series trend to be followed in different categories, namely those involving enterprises, energy usage, environmental data, and population data, among others.

Recommendations

Targeted Fund Allocation: Redistribute the portions due to the country's peculiarities. Initially, there are better prospects for further adjustment of investment in large projects, Cyprus and Poland need such funding to ensure the success of projects for several billion dollars in investment, countries with limited funding should receive adjusted funding to achieve balanced development.

Strategic Project Focus: As seen in the above analysis funding should be focused on areas with the highest summed values which include, Education employment and social inclusion and should assist in focusing efforts and thus enhancing the effects on regional development and societal welfare (Koller, 2024).

Diversify Investment Priorities: The salient attributes of the Geddes proposals show that they are not balanced. Expanding prospects to different sectors can help achieve more profound and diverse growth and avoid reliance on a few key initiatives (Stash.com, 2024).

Performance Monitoring: Undertake periodic evaluations of activities in an organisation to determine which projects yield the greatest outcomes. This will enable one to devise flexible strategies that match regions or projects that are most productive.

Conclusion

The examination of ESIF funding for the 2014-2020 period shows the extent to which discrepancies between the forecasted and the actual outcomes exist, to determine inefficiency in fund expenditure. Significant disparities have been observed regarding balances of member states’ funding and investment priorities to require strategic reorientation. The alterations are suggested as a way to enhance the EU funds efficiency, equalization of the EU financial resources distribution, concentration on the priority areas including education and social integration, shifting of the investment objectives, and enhancing the productivity of the performance. Fundamentally, to achieve the improvement of the EU funds all the following alterations have to be made, equalization of the EU financial resources distribution; concentration within such essential spheres as the education sphere and the It is expected that these measures will contribute positively towards future regional development strategies and thus increase the overall effectiveness of future EU investments for goal-oriented and strategic development.

References

?Ibeh, CV, Asuzu, OF, Olorunsogo, T, Elufioye, OA, Nduubuisi, NL and Daraojimba, AI, (2024) ‘Business analytics and decision science: A review of techniques in strategic business decision making’, World Journal of Advanced Research and Reviews, 21(2):1761-1769. https://wjarr.com/sites/default/files/WJARR-2024-0247.pdf.

Bouchetara, M, Zerouti, M and Zouambi, AR, (2024) ‘Leveraging artificial intelligence (AI) in public sector financial risk management: Innovations, challenges, and future directions’, EDPACS, 69(9):124-144. https://www.tandfonline.com/doi/abs/10.1080/07366981.2024.2377351

Di Cataldo, M and Monastiriotis, V, (2020) ‘Regional needs, regional targeting and regional growth: An assessment of EU Cohesion Policy in UK regions’, Regional Studies. https://eprints.lse.ac.uk/89190/1/Monastiriotis_Regional_needs_Accepted.pdf

Europa. Eu (2024) ‘ESIF 2014-2020 Achievement Details’, Directorate-General for Regional and Urban Policy. https://commission.europa.eu/about-european-commission/departments-and-exec...

Koller, T. (2024). Keep calm and allocate capital: Six process improvements. McKinsey & Company. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/keep-calm-and-allocate-capital-six-process-improvements.

Kurpiela, S and Teuteberg, F (2023) ‘Linking business analytics affordances to corporate strategic planning and decision-making outcomes. Information Systems and e-Business Management, 22(1):33–60. https://doi.org/10.1007/s10257-023-00661-z.

Prodi, R and Sautter, C, (2018) ‘Boosting investment in social infrastructure in Europe’, Commission européenne, Discussion Paper. https://educationemployers.eu/wp-content/uploads/2018/02/Boosting-investments-in-social-infrastructure-in-Europe.-Report-of-the-High-Level-Task-Force-on-investing-in-Social-Infrastructure-in-Europe-chaired-by-Romano-Prodi-an.pdf

?Stash.com. (2024). https://www.stash.com/learn/how-to-diversify-investments

Wang, Y, Shao, Z and Tiong, RL, (2021) ‘Data-driven prediction of contract failure of public-private partnership projects’, Journal of Construction Engineering and Management, 147(8):04021089. https://www.researchgate.net/profile/Wang-Yongqi-2/publication/352056170_Data-Driven_Prediction_of_Contract_Failure_of_Public-Private_Partnership_Projects/links/64268719a1b72772e43d702d/Data-Driven-Prediction-of-Contract-Failure-of-Public-Private-Partnership-Projects.pdf

Yin, J and Fernandez, V, (2020) ‘A systematic review on business analytics’, Journal of Industrial Engineering and Management (JIEM), 13(2):283-295. https://www.econstor.eu/bitstream/10419/261719/1/1741210682.pdf

Assessment 2: Business Analytics Case Study - European Structural and Investment Funds (ESIF): A Business Analytics Approach for Enhanced Fund Utilization and Regional Development

Table of Contents

Introduction