Data Analysis for Tableau

CASE STUDY 1

INTRODUCTION

The study of two distinct research cases in great detail to examine data-driven decision making in two very different sectors. In the case study1, they business analyst hats and investigate the banking industry with the specific aim of creating a carefully planned marketing campaign that will entice consumers to open term deposits. Some of the factors that determine the effectiveness of a marketing campaign are customer segmentation, distribution networks, pricing strategies, and promotional tactics.

They assume the role of a sports analytics consultant and work with the manager of a soccer team to find a solution. They explore the Soccer Match Dataset in this post, which is a treasure trove of data encompassing a variety of soccer competitions. Whether it's comparing two legends like Messi and Ronaldo, scouting for a new goalie, or understanding how a player's body influences his performance, our mission is to assist the manager in making smarter decisions.

TASK 1

The table comprises two distinct rows denoted as "no" and "yes," perhaps indicating the division of individuals into two age groups or categories. The variable of interest, which is not specifically designated but is presumably associated with deposits, is denoted in the "age" and "deposit" columns. The column labelled "age" appears to represent the counts or frequencies within each respective category. Similarly, the column labelled "deposit" exhibits equal values, indicating that both age groups have equivalent counts. The total as a result is 11,162.

Based on the statistics, it is evident that two age-related categories of individuals are being assessed: those who responded "no" and those who responded "yes." However, in the lack of any contextual knowledge, the exact definition and traits of these age groups are still unknown (Du, 2018). The observed equality in the counts between the "age" and "deposit" columns implies that the data may represent either the total count of individuals or occurrences in each group, or alternatively, the count of deposits made by individuals within each age category.

The Total sum of the counts for both age groups, shown as the "Grand Total" in the table, reveals that the overall count for the variable under consideration amounts to 11,162. This information is crucial for comprehending the extent of the data.

There are multiple methods to represent this data in a horizontal bar chart by using Tableau. A commonly is used method involves representing the two age groups ("no" and "yes") along the Y-axis, while utilizing the X-axis to depict the count values. The lengths of the bars corresponding to each age group would be uniform, aligning with the counts specified in the table. Consequently, this would offer a straightforward visual depiction of the variable's distribution across the various age groups.

Framework, this particular form of data visualization holds significant value as a component of research or reporting endeavours. In order to comprehend the academic implications, more contextual information and specific facts regarding the variable under consideration, as well as the characteristics of the "no" and "yes" classifications, are necessary. The comprehension and scholarly significance of the results are contingent upon grasping the variables at play, the research inquiry or hypothesis being examined, and any underlying circumstances that could impact the correlation between age and deposits.

The information presented here can serve as a starting point for additional research into a range of topics, including how age influences deposit behaviour or the representation of different age groups in a particular dataset. Creating a connection between this dataset and a more extensive research framework is essential to carrying out an extensive scholarly investigation and offering a more profound contextual comprehension of the results.

Figure 1: Age and deposit

TASK 2

The tabular layout of the dataset presentation includes the columns "Deposit," "Job," and "Age." "Yes" and "no" are the possible values for the "Deposit" variable, which indicate whether or not a deposit has been made. Those who work in different fields are categorized using the "Job" variable. Lastly, "Age" shows the total population for every conceivable pairing of occupational classification and deposit status.

Depending on the value of the variable "Deposit," the data is separated into two separate sets: "yes" and "no." By doing this, the occurrences are separated into two groups: those where deposits were made and those where they weren't. Within each of these overarching divisions, there are other employment classifications.

The study of the "no" category, it becomes apparent that a significant proportion of individuals within this group belong to the "management" and "blue-collar" occupational classifications, with respective counts of 51,271 and 49,344. The employment categories with the highest frequencies exhibit a correlation suggesting that individuals occupying these work responsibilities are comparatively less inclined to engage in deposit activities. On the contrary, it is seen that the employment categories labelled as "unknown" and "student" have the lowest frequencies within the "no" group. This suggests that persons engaged in these particular occupations demonstrate a higher propensity for making deposits.

When examining the "yes" category, it is evident that those employed in the "management" and "retired" employment groups exhibit the highest frequencies of making deposits, with 51,929 and 34,796 instances, respectively (Ghatasheh et al., 2020). This implies that persons occupying these occupational positions exhibit a higher propensity to engage in deposit-making activities. Conversely, the categories labelled as "unknown" and "entrepreneur" have the least frequent occurrences in terms of deposit activity, suggesting a lower propensity among individuals in these occupational fields to engage in bank transactions.

A horizontal bar chart in Tableau, when constructed with the above data, would effectively represent the associations by presenting the variable "Job" along the vertical axis (Y-axis) and the respective quantities of individuals on the horizontal axis (X-axis). The bars would be partitioned into two distinct segments, namely "no" and "yes," for each employment category, so illustrating the distribution of deposit behaviour across different occupational categories.

Within an academic framework, this dataset possesses significant potential for doing research or analysis pertaining to the domains of banking or customer behaviour. Nevertheless, a thorough scholarly analysis necessitates a more profound comprehension of the contextual aspects of the dataset, including its origin and intended use, the specific research inquiries it seeks to answer, and the underlying causes that influence the behaviour of deposits.

The information that is accessible gives an opportunity to delve into different academic perspectives and theories, such as examining the potential relationship between job type and deposit behaviour. In order to derive significant insights, it may be necessary to do further statistical analysis, such squared tests or logistic regression, to ascertain the magnitude and significance of these relationships.

The data could be utilized for the purpose of conducting segmentation analysis, enabling researchers to discern discrete client groupings characterized by unique deposit behaviours. This might facilitate the development of focused marketing campaigns or product offerings tailored to distinct customer segments.

Figure 2: Job or Career

TASK 3

The column labelled "Deposit" comprises two distinct categories, are "no" and "yes." These categories likely signify binary outcomes or decisions, whether pertaining to financial transactions, consumer behaviour, or another context where the options of "deposit" and "no deposit" are applicable.

The column labelled "Age" is most likely indicative of the age of individuals or organizations, whereas the column labelled "Duration" is likely indicative of the length of time for a particular action or occurrence. The "Age" values are represented in a numerical format, potentially denoting years, months, or another temporal unit. Similarly, the "Duration" values are similarly numerical and may indicate a duration of time, potentially in the same temporal unit as "Age" or a distinct one.

The information that is organized in a manner that facilitates the generation of a scatter plot using Tableau. This plot will effectively depict the associations between the variables "Age" and "Duration" for the two distinct categories of "Deposit".

In a scatter plot, it is customary to represent the "Age" values along the horizontal (X) axis, while the "Duration" values are depicted along the vertical (Y) axis. Every data point in the dataset is represented by a point on the scatter plot, where its location is defined by the associated values of "Age" and "Duration" (Kartika et al., 2020). Data points indicating a lack of deposit status would be discerned from those indicating a positive deposit status through the utilization of unique markers or colours.

The scatter plot may vary depending on the particular context and research goals, as viewed from an academic standpoint. This dataset has numerous possibilities for academic insights and interpretations.

The scatter plot is a useful tool for examining the correlation between age and duration. Does a correlation exist between these two variables, and if so, is it positive or negative in nature. The comprehension of this correlation possesses the potential to yield implications for decision-making or serve as a basis for future investigations within a certain field.

Deposit Behaviour The utilization of a scatter plot enables the examination of deposit behaviour in respect to age and length. Do the data exhibit any discernible patterns or clusters that imply a higher likelihood of deposition for specific age groups or durations? This analysis holds significant value in the context of marketing strategies, customer segmentation, and financial planning.

The utilization of scatter plots facilitates the identification of outliers or anomalies within the dataset. Data points that exhibit substantial deviation from the overall pattern may necessitate further scrutiny. These anomalies may indicate atypical or unforeseen behaviours or circumstances.

Corporate Implications the academic examination of this data may give valuable insights into various aspects of corporate operations, such as strategic planning, risk evaluation, customer retention, and other critical decision-making procedures, depending on the specific situation. The comprehension of the correlation among age, duration, and deposit behaviour may be utilized for the purpose of strategic planning.

Hypothesis evaluation is the utilization of collected data by researchers to develop and evaluate hypotheses. An example hypothesis could posit a positive correlation between longer durations and higher deposit rates. To evaluate this hypothesis, statistical analysis may be employed for testing purposes.

TASK 4

The dataset contains row labels that correspond to individual months, commencing with January and concluding with December. The numerical values in the column adjacent to this one represent the magnitude of a specific variable. The value of the variable is not clearly specified however, it seems to denote a type of measurement or count corresponding to each month.

The presence of Tableau, a software renowned for its ability to generate aesthetically engaging and interactive data representations, suggests that the table is likely a component of a larger data visualization or reporting initiative. The row labelled "Grand Total" located at the bottom of the table aggregates all the numbers within the "day" column, resulting in a cumulative value of 11,162. This implies that the dataset encompasses a comprehensive tally or metric of this variable during the entirety of the year.

The generated horizontal bar chart in Tableau would typically present the months on the vertical axis (Y-axis) and the associated variable values on the horizontal axis (X-axis). Each bar in the graph would correspond to a particular month, and its length would be directly proportional to the size of the variable associated with that month (Katz, 2022). The values of the variable exhibit notable fluctuations on a monthly basis, with May and July exhibiting the greatest values, while December demonstrates the lowest value.

A number of patterns and potential insights are revealed by the examination of the given data. For the relevant variable, a seasonal trend is discernible in the data. May and July exhibit the greatest values, suggesting a notable upsurge or expansion in the variable throughout the summer season. There could be a multitude of reasons for this, such as the summer's increased activity and demand.

December exhibits a notable decline in the variable, as it has the lowest value among all the months that are monitored. Seasonal variables such as holidays and lowered activity levels could be the cause of this phenomenon.

February has a noticeable increase, indicating that the month is unique in some way. More digging would be necessary to get to the bottom of things.

Moderate values are seen in January, March, April, June, and November, suggesting that the variable is now quite stable. The reliability that has been observed may be the result of both external and internal corporate policies and practices.

The presence of anomalies within the dataset and the variable being measured necessitates additional contextual information in order to establish conclusive interpretations. It is imperative to conduct investigations into anomalies or unanticipated fluctuations in order to gain a comprehensive understanding of the underlying factors contributing to such occurrences (Smerichevskyi et al., 2019).

In general, the dataset and its related horizontal bar chart in Tableau give a succinct and aesthetically pleasing method for depicting and examining the distribution of a variable throughout the months of the year. However, in order to achieve a comprehensive interpretation and derive practical insights, it is necessary to have other information regarding the characteristics of the variable under consideration and the particular circumstances in which it
is being assessed.

DASHBOARD CASE STUDY 1

Figure 5: Dashboard case study 1

Case study 2

TASK 2.1

The table-based dataset presented here serves as an extensive database of data about professional football (soccer) players from various countries. The given dataset, which includes the given names, surnames, heights, and weights of the athletes under examination, is precisely structured and provides insightful information about these athletes' core characteristics. The players' countries of birth are used as the dataset's basic structuring concept, and each row in the dataset represents a distinct individual. Countries like as Brazil, Burkina Faso, Cameroon, Canada, Chile, Colombia, Congo, Costa Rica, Croatia, Czech Republic, Denmark, Egypt, England, and Finland are among those whose athletes in this sport were born. This is evidence of the talent represented in the sport on a global scale.

The physical attributes of these athletes are examined in the dataset, as they are very relevant to their football accomplishments. The assessment of an athlete's suitability for various positions and their overall effectiveness in the game is largely dependent on the measurement of their height and weight. The given data is organized in a comprehensive manner, with each variable birthplace, first and last name, height, and weight—being assigned to a separate column. Researchers, coaches, and enthusiasts can all benefit from this methodical organization, which makes it easier to examine, compare, and analyses the athletic features of these athletes from different countries.

Such information is highly valuable for several reasons, including talent spotting, in-depth statistical analyses, and learning about regional variances and patterns in football players' physical characteristics. To sum up, this dataset is an extensive and useful tool for anyone looking to study the physical attributes of professional football players across the globe. It provides insights into the complex and diverse character of this well adored sport on a global scale.

Moreover, this data offers not only a fascinating viewpoint on the global professional football landscape but also noteworthy prospects for additional in-depth research and examination. It is possible to identify recurrent patterns, trends, and potentially useful insights by comparing the physical attributes of athletes to those of their respective countries. These insights could be applied to improve player development, team composition, and even our understanding of how a player's cultural background may affect their abilities and playing style (Fialho et al., (2019). Additionally, this dataset can aid in the appraisal of injury risk, the assessment of performance expectations, and the enhancement of the larger conversation about diversity and representation in football. Anyone interested in football, researchers, sports analysts, and those involved in the sport's decision-making can all benefit greatly from the database. It offers a thorough and detailed look at the global football talent scene.

Figure 6 :Top 5 goalkeepers

Task 2.2

The tabulated data set presented herein contains essential information pertaining to Lionel Messi and Cristiano Ronaldo, two of the most renowned and esteemed footballers globally. The information is structured into columns that provide specifics regarding the participants' height, weight, foot preference, short names, and sub-event names. It can be seen that Lionel Messi prefers to use his left foot, whereas Cristiano Ronaldo prefers to use his right foot. The respective abbreviated forms of their names are "L. Messi" and "Cristiano Ronaldo." Additionally, their height and weight are documented in the dataset. Messi is 170 centimeters tall and weighs 72 kilograms, while Ronaldo is 187 centimeters tall and 83 kilograms. These dimensions hold significant value for enthusiasts, educators, and professionals in the field of football analysis, as they are essential for comprehending the physical qualities that influence the playing techniques and overall effectiveness of these legendary players.

Significant particulars such as Messi's left-footedness and Ronaldo's right-footedness may have an influence on their respective on-field strategies and tactics. Right-footed players may demonstrate distinct strengths and strategies, whereas left-footed players frequently contribute distinctive ball control and angles to their gameplay. The divergent physical dimensions Messi is shorter and lighter in weight in contrast to Ronaldo, who is taller and marginally heavier and thus possesses these attributes may offer valuable insights into their agility, strength, and aptitude for excelling in various facets of the game. Moreover, this information provides a momentary depiction of the continuous rivalry between Lionel Messi and Cristiano Ronaldo, enabling enthusiasts and analysts to draw parallels and acknowledge the unique attributes that have contributed to their status as two of the most renowned individuals in the industry.

Figure 7: Comparison of game performance

TASK 2.3

Figure 8: physical characteristics on their game performance

The provided tabular dataset offers an exhaustive synopsis of football players, encompassing pertinent information such as heights, weights, preferred foot, and brief names. The data is systematically arranged in columns that contain distinct information regarding each player, including their respective heights in kilogrammes, the short name that distinguishes them, and the event name (which specifies the foot preference of each player). The nationalities and backgrounds of the players represented in this dataset are quite varied, and their preferred footwear is essential for comprehending their playing techniques and characteristics. Additionally, the dataset is categorised into two primary groups: players with left feet and players with right feet.

It is apparent that left-footed athletes exhibit a predilection for employing their left foot primarily for the purposes of ball control and shooting. An individual is represented in this category by his or her abbreviated name, which may include "A. Abdennour," "A. Abeid," "A. Agolli," "A. Ayew," or "A. Baba." They vary in weight from 70 to 84 kilogrammes and in height from 174 to 187 centimetres. When considered alongside their preferred footwear, these physical characteristics can provide significant insights into their respective duties on the pitch. In aerial duels, taller players such as A. Abdennour might possess a tactical edge, whereas shorter, more dexterous players like A. Abeid might excel at dexterity and manoeuvring.

Right-footed athletes, such as "A. Abedzadeh," "A. Abrashi," "A. Acquah," and "A. Aguilar," prefer to use their right foot. The absence of height and weight data for these athletes is evident in the dataset; however, their foot preference continues to be a crucial factor in discerning their playing methodologies and the extent of their contributions to their respective teams(Hubáček et al., (2022)). Through the comparison of left-footed and right-footed players within this dataset, individuals such as coaches, analysts, and football supporters can gain valuable insights into the ways in which distinct foot preferences can impact player positions, tactics, and the overall performance of teams.

Task 2.4

This tabular dataset's information provides an engrossing look into the world of professional football by disclosing various players' weights and heights in addition to other physical attributes. Every entry in the dataset has a short name, and the participants themselves come from a diverse spectrum of backgrounds and countries. The dimensions described above are essential for understanding these athletes' physical makeup as well as their potential football-field roles and strengths.

The dataset includes statistics on athletes with wide variations in height and weight. The disparity in height is striking, with sportsmen like C. Ünder, who is 173 centimetres, and C. Tătărușanu, who is 196 centimetres tall, standing taller than others. This variability has important ramifications for the roles that these athletes might play during a match. Taller players are typically chosen in positions like goalkeepers or centre backs, where their stature gives them an advantage in aerial duels and shot-stopping. On the other hand, because midfield players are adept at dribbling, changing directions quickly, and close ball control, they are typically shorter and more nimble than other players.

Similarly, a player's weight plays a crucial role in assessing their physical presence on the pitch. Those who are 95 kg in weight, like C. Wood, are anticipated to be extremely strong individuals who may excel as attackers or central defenders capable of physical combat. Conversely, athletes with lower body weights such as C. Ünder, who weighed 66 kg—may be known for their agility and speed, making them the best candidates for winger or forward positions. The ratios of height to weight are important measurements to consider when evaluating a player's suitability for a particular position because each player's unique combination of these attributes affects how they play and how well they perform.

Figure 9: ccompare game statistics

Moreover, the dataset's condensed format provides ample opportunity to explore the intricacies present in football players' careers. Short names like "C. Soyüncü," "C. Stuani," and "C. Wilson" could be recognisable to die-hard football fans because they conjure up unique playing philosophies, team loyalties, and possibly memorable game situations. The players' diverse life stories and narratives enhance the complex fabric of football and serve as symbols for many football cultures. Understanding these athletes' personal journeys and experiences in addition to their physical attributes might help us comprehend their significance in the history of football and on the global scene.

In addition, the dataset's concise structure emphasises how important it is to collect and analyse data in the context of modern football. Scouts, clubs, and coaches rely on thorough player profiles with details like weight and height when making choices on player acquisition and tactical approaches. These indicators provide important insight into a player's ability to progress and adapt within a team's organisational structure, which can have an impact on decisions about transfers, playing roles, and player development programmes.

In conclusion, despite appearing to be merely a collection of numbers, this dataset offers insight into the subtleties and complexity of the professional football market. Not only are height and weight physical characteristics, but they also have an impact on player positions, playing styles, and the rich history of this global sport. The dataset's conciseness stimulates more research because each player's life story, professional background, and contributions to the game are all a part of the bigger picture that enthrals fans. It draws attention to the importance of the interaction between the personal and physical facets of football, which combined create a tapestry as complex and dynamic as the game itself.

Figure 10: Compare games statistics Top of Form

This tabular dataset provides an intriguing synopsis of professional football players, distinguished by their distinct physical characteristics, particularly their body masses and statures. These numerical values are supported by narratives that highlight the sport's dynamic nature, versatility, and diversity. Through the analysis of this data, the complex correlation between the physique of a football player and their position on the pitch can be revealed.

The dataset provides an introduction to several athletes whose abbreviated names include "C. Wilson," "C. Santos," and "C. Soyüncü." These athletes are of various nationalities and cultural origins, testament to the sport's international scope. Although these abbreviated names may seem simplistic in appearance, they function as symbolic identifiers, representing an individual player's distinct experience, playing style, and influence on the game. These individuals are the public figures whom millions of fans around the world avidly observe, be it on the football pitch or via the television screen.

The two essential characteristics outlined in this dataset—height and weight—are critical when determining whether an athlete is qualified for particular positions. From C. Tătărușanu's 196-centimeter stature to C. Ünder's 173-centimeter stature, the dataset demonstrates the remarkable variation in player height. The considerable diversity of these players not only piques our interest numerically, but also profoundly shapes the roles they undertake within the game. In defensive positions, such as central defenders or goalkeepers, where their height can be advantageous in aerial duels and reach, taller players are frequently preferred. On the contrary, midfield is a common location for shorter, more dexterous players, whose capabilities including dribbling, rapid direction changes, and close ball control can substantially influence the strategy of an opposing team.

Weight, which is another facet of physicality discussed in this context, is similarly critical when assessing the on-field presence of a player. Individuals of C. Wood's stature (95 kilogrammes) are possibly formidable beings who utilise their physical prowess to excel either as target men in offensive schemes or as central defenders capable of engaging in physical duels. Conversely, lesser players, exemplified by C. Ünder's 66 kilogrammes, are renowned for their dexterity, quickness, and agility, which render them outstanding contenders for winger or forward roles (Muazu Musa et al., (2020)). The optimal proportions of height and weight are pivotal in determining a player's aptitude for their designated position; the unique amalgamation of these characteristics by each individual player contributes to the development of their distinct playing style.

Moreover, the concise nature of the dataset motivates us to further explore the narratives and experiences of these athletes. In addition to the quantitative aspects of height and weight, every diminutive name is linked to a multitude of experiences, victories, and hardships. The appellations may elicit recollections of exceptional goals, pivotal escapes, or indelible moments experienced on the playing field. These athletes exemplify the unity and fervour that football inspires across the globe, uniting individuals across borders and cultures.

Furthermore, this dataset highlights the criticality of data acquisition and analysis in the context of contemporary football. In order to make informed decisions regarding recruitment and tactical strategies, clubs, coaches, and recruiters depend on comprehensive player profiles that encompass physiological attributes such as height and weight. These metrics possess the capacity to impact transfer determinations, playing roles, and player development initiatives, as they offer indispensable discernment into a player's growth potential and adaptability within the organizational framework of a team.

In conclusion, although this dataset may appear to be a simple compilation of numerical values, it provides insight into the complexities and multifaceted characteristics of the professional football industry (Seweryn et al., (2023)). Height and weight are determinants that influence player roles, playing methods, and the extensive historical background of this worldwide sport. The succinct structure of the dataset encourages further investigation, as it incorporates the personal lives, professional trajectories, and significant contributions of every participant into a more comprehensive narrative that persistently engrosses enthusiasts on an international scale. This highlights the significance of the personal and physical aspects of football, demonstrating how they interweave to form a multifaceted and ever-changing fabric similar to the sport itself.

CONCLUSION

In the first case research, group worked with a financial institution to create a strategic marketing plan. Data research of customer demographics, job classifications, contact durations, and past campaign outcomes allowed them to acquire valuable insights. With the use of this data, they were able to ascertain the relevance of contact duration on campaign outcomes, the impact of various vocations on deposit, and whether or not there was a significant age difference between depositors and non-depositors. In order to make sure that the right people were contacted at the right time through the appropriate channels, they also created campaign plans.

They took a broader look into soccer analytics in the second case study. They evaluated a sizable dataset that comprised player attributes, match events, and performance metrics in order to assist a soccer team's management in making data-driven decisions. To monitor our results, we built a Tableau dashboard that included a comparison of two soccer legends, a ranking of goalkeepers based on save attempts, and an analysis of the influence of physical attributes on game performance. Without these illustrations and evaluations, the team would not have been able to conduct its scouting missions or strategic discussions.

Reference

Du, K. (2018). The impact of multi-channel and multi-product strategies on firms' risk-return performance. Decision Support Systems, 109, 27-38.

Fialho, G., Manhães, A., & Teixeira, J. P. (2019). Predicting sports results with artificial intelligence–a proposal framework for soccer games. Procedia Computer Science, 164, 131-136.

Ghatasheh, N., Faris, H., AlTaharwa, I., Harb, Y., & Harb, A. (2020). Business analytics in telemarketing: Cost-sensitive analysis of bank campaigns using artificial neural networks. Applied Sciences, 10(7), 2581.

Hubáček, O., Šourek, G., & Železný, F. (2022). Forty years of score-based soccer match outcome prediction: an experimental review. IMA Journal of Management Mathematics, 33(1), 1-18.

Kartika, T., Firdaus, A., & Najib, M. (2020). Contrasting the drivers of customer loyalty; financing and depositor customer, single and dual customer, in Indonesian Islamic bank. Journal of Islamic Marketing, 11(4), 933-959.

Katz, H. (2022). The media handbook: A complete guide to advertising media selection, planning, research, and buying. Routledge.

Muazu Musa, R., PP Abdul Majeed, A., Kosni, N. A., Abdullah, M. R., Muazu Musa, R., PP Abdul Majeed, A., ... & Abdullah, M. R. (2020). An overview of beach soccer, sepak takraw and the application of machine learning in team sports. Machine Learning in Team Sports: Performance Analysis and Talent Identification in Beach Soccer & Sepak-takraw, 1-12.

Seweryn, K., Wróblewska, A., & Łukasik, S. (2023). Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer--Current Trends and Research Perspectives. arXiv preprint arXiv:2309.12067.

Smerichevskyi, S. F., Reshetnikova, I. L., & Polishchuk, Y. A. (2019). Multican marketing as an innovation technology of providing services in the conditions of globalization of the banking market.

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