An Exploration of the Current Approaches to Data Management inBanking Institutions: Towards a Recommended Approach for EnterpriseData Management in Banking Institutions
Faulty of Science and Technology
Following the trends in the banking industry and the challenges inthe management of data, one of the significant considerations forbanks is currently the management of data through the identificationof the deficiencies, and formulation of the roadmap to addresscurrent shortcomings. According to Baskerville and Myers1,data management involves the process of development, the execution aswell as the supervision of procedures, programmes, policies orpractice, which protect, control, enhance, and deliver the real valueof data as well as the assets of information.
Components and Capabilities of Data Management System
Sasan2proposed that for any data management system (DMS) to be effective,various elements need to be present. They include the Data ManagementVision (DMV) that describes the vision as well as the principles andthe fundamental values that define the nature of the managementsystem. In addition, he stressed that there should be Data ManagementGoals (DMG) that must relate to the goals of the respective bankinginstitution, its priorities, and objectives. These require adoptionand communication to the major stakeholders including customers andshareholders. He added by proposing a Governance Model (GM) thatneeds an adoption of the institution’s general mechanism for themanagement of the particular system as well as the funding andimplementation. Furthermore, he included Issues Management andResolution (IMR) where he expressed that the bank must have thecapability of identifying, tracking and updating status forintegration issues and all data during operations or the present datahandling initiatives. Finally, he mentioned Monitoring and Control(MC) that comprise of the combined capabilities for reporting andmeasuring the effectiveness and quality of the data management methodas it operates. Therefore, the effectiveness of the data managementsystem (DMS) of any banking institution depends on its ability toconsider these elements in the most effective manner.
While addressing the challenge that banks face in the management ofdata, Shu and Strassmann suggested that in the current environment,the data management system must possess the various importantcapabilities that will ensure efficiency and effectiveness at alllevels of data handling and usage3. These capabilities include Critical Data Inventory (CDI) that musthave all the data components that the bank deems significant forregulatory compliance and decision-making. The creation of thisinventory must incorporate the views of the business clients to helpin prioritising or containing the scope of the data managementsystem. They also mentioned Data Integration (DI) that must cover thedata acquisition tools, composition as well as enrichment of datacoming from various sources into one view or store. DI assists thebank to address the process of controlling and monitoring theintegrity of data as it flows from producers to consumers. Inaddition, they included Data Profiling (DP) that involves dataexamination to acquire statistics and features concerning thestructure of the data available. Their argument is that thiscapability is significant for the assessment of data, classification,integration and the analysis of the impact. The next capability isData Quality (DQ) that measures the degree that the data isappropriate for the intended purpose. They pointed that keydimensions for this capability are completeness, accuracy,duplication, integrity, and consistency. They also hinted thatend-to-end data quality paves the way for comparison across trends.
While stressing their view, Shu and Strassmann also mentioned thesignificance of Metadata Management (MM) that captures all theimportant characteristics of the respective data such as the type,source, timestamp, owner, length, semantics, lineage, traceability,etc4.This directs to the use of uniform tools and methods for collecting,defining, and management of the information to ensure consistencythroughout the banking corporation. In addition, they proposed theMaster Data Management (MDM) that is a single file, which isauthoritative as well as agreed upon data source significant for theoperations of the corporation. This must hold obstinatenontransactional data such as employee, product, customer, etc. Theyargue that MDM ensures the presence of one perfect version for allthe data used in the organization. Another important capability thatthey mentioned is Reference Data Management (RDM) that assists thebank to categorise and classify data appropriately. They pointed anexample of the product master that carries all the products and theircharacteristics. Just like MM and MDM, RDM plays a vital role inensuring consistency and integrity of data. Finally, they proposedData Privacy (DP) that ensures all the processes, algorithms, andtechnologies that require all the contents align completely with theprotection and privacy laws or regulations. Therefore, an efficientdata management system must possess all these capabilities.
In his study, Skyrius underlined the attitudes of the decision makerconcerning the various factors that influence the quality of thedecisions made by banking institutions5.The factors include the sources of data, the tools used in dataanalysis and the effectiveness of the use of information technology.He argues that for the decision to be appropriate and sound, all thefactors must meet the required minimum standards and conditions. Thismeans that the data source must be credible, the tools for dataanalysis must be accurate to provide the right answer and thetechnology should be good to ensure efficiency. While supporting hisview, Handzic, paid attention to the effect of availability of dataon the capability of the management to process and utilise data forlong-term and short-term decision making and planning of businesstasks6.His study established that availability of enough data leads tobetter management of data and high efficiency as well as accuracy inmaking decisions. His study has established the significance of usingthe most appropriate tools to ensure consistency in the acquisitionof data that will enable efficiency in operations.
Data Management and Technology
Liu and Young concentrated on the major data management models andtheir effect on business decisions by considering three dissimilarconditions7.Their study indicated that business enterprises such as banks areadvancing because of enterprise applications systems thatcontemporary IT tools provide, with a good example being KnowledgeManagement Systems (KMS), Enterprise Resource Planning (ERP), andCustomer Relations Management (CRM). These tools assist bankinginstitutions to make sound business decisions that produce positiveoutcomes to the economy. Therefore, the authors insist that forbanking institutions to enhance their financial, organisationalcapabilities and the level of competitiveness of the market, theyshould have a good knowledge of all the dimensions of data managementand develop or define human resources, internal operations,technologies, and manage them appropriately at all organisationallevels. Nevertheless, the authors agree the fact that it is quitechallenging to effectively, establish the connection between DMS,planning, and decision-making.
Surprisingly, the study conducted by Shu and Strassmann that involvedtwelve banks operating in the United States between 1989 and 1997revealed that despite the significance of IT in the operations anddata management, IT does not contribute significantly to theperformance of banks8.However, many other studies have proved the significance of IT in themanagement of data, which enhance efficiency in business operationsbecause it promotes good decision-making. For example, Kozak exploredthe effect of advancements in IT on the cost effectiveness and profitof banking corporations from 1992 to 2003, where he proved theexistence of a direct relationship between IT advancement and goodmanagement of data that in turn leads to high productivity andreduced overheads9.
While supporting the need for an effective system of data management,O`Neill and Adya argued that the corporations that have no efficientsystem of data processing and sharing will not succeed in leveragingthe intellectual capital of its managers to ensure growth andinnovation10.Efficient management of data will facilitate sharing of experiencesthat enables the transfer of the required data to all the levels ofmanagement to sustain the competitiveness of the corporation andimprovement of the quality of products and services. In addition,Barachini braced by expressing that banking institutions shouldmotivate their workers continuously to share valuable data to enablebetter control of their intellectual assets11.Efficient management of data will provide banking institutions anopportunity for managing its knowledge effectively12.It will also enable the corporation to conduct and maximise theinitiatives of the management in harmonising the suitable approachesin both short-term and long-term scheduling.
Liu and Young proved that efficient data management is essential inthe field of decision-making because it helps in monitoringinstability in the financial system it verifies the sequence ofactivities and takes steps to stabilise the system13.However, they also argue that nonprogrammed decisions provide a goodsupport because they supply data, enable analysis, evaluation, choiceas well as the implementation of the process of decision-making. Inanother study, Minelli, Chambers and Dhiraj proposed that anefficient system of data management is necessary for improvement andenhancement of the process of decision-making on the issues thatsignificantly, affect the performance of the financial sector14.
Data Management System Strategy
Otto, Lee and Caballero hold that all banking institutions require aclear image of their objectives and goals for the adoption of anyspecific DMS that suits their individual business interests and therequirements of the market15. Therefore, they require the creation of selection criteria on theirobjectives to enable easier identification of the most appropriatesystem. He adds that the banking institution must have clearobjectives in their data management strategies and vision beforeconsidering the use of technology. Minelli, Chambers and Dhirajestablished that most banks are keen on customer service systemrather than considering the entire business strategy16.They proposed that a bank must not only concentrate data managementon customer service but also use the system widely to benefit allsections of the organisation.
In addition, Otto, Lee, and Caballero proposed that the DMS mustalign the commercial priorities to the specific elements as well asthe capabilities17.This approach has to consider the contemporary status of everycapability from the anticipated needs of the state in the future. Hestressed that it is suitable to begin with management maturity modelthat provides a good assessment of all the capabilities. The modelproposes few defined policies and rules concerning the quality ofdata and its integration. It also reveals that the same data canexist in various applications while redundant data can be indifferent records and sources. The ownership of data, as well as itsapplication, is also synonymous. This will occur about thepreparation of the corporation based on its policies, people,technology as well as the adoption viewpoint. He argues that thiswill enable the bank to see the gaps and plan appropriately to meetits goals in the future.
In addition, Hormozi and Giles stated that in the establishment ofthe path for creating or enhancing the data management, there isalways a tendency of creating an endwise functionality in thesystem18.He stressed that such preemptive approaches cause serious problemsregarding duplication, inconsistency, and unreliable data,processing, reporting, and decision-making. As a means of avoidingsuch challenges, he proposed a layered methodology in which everyhorizontal component or capability has a separate management unit.This will ensure efficiency and easier sharing and interpretation ofdata. Below is an illustration of the Layered Model.
1 2 3 4 5 6
Sales and marketing
Risk and Compliance
Finance and accounting
Audit and legal
Figure 1: Layered Model
Endwise Data Quality Assurance
In his study, Minelli, Chambers and Dhiraj established that duringthe designation of data quality programmes, the largest number ofbanking corporations concentrate only on the data that is in thewarehouse19.However, he argues that this approach is not effective because itdoes not allow banks to identify all sources of data and cause theacquisition of data with poor quality. In the end, it is difficult toestablish whether the data used for reporting and analysis is poor inquality. Therefore, he recommended that the best approach forensuring end-to-end quality of data is to create a programme thatincorporates all the data right from the source schemes to the finalreports. With this method, measurement of data quality occurs invarious touch positions as it flows in the business process. Theapproach leads to efficiency in the management and analysis of datathat in turn leads to quality decision-making leading to stability inthe financial sector.
Integration of results from various data quality processed (manual and automated) into data quality mart
Aggregation and alignment of rules to data quality dimensions, Designation of scorecards and weights
Integration of results from feeds (Excel, SAS, IDQ)
Data Quality Mart should accommodate manual and automated feeds
Traceability and aggregation of results
Data Quality Mart design should accommodate traceability of elements by sources
Executive level dashboard will drill down capabilities
The dashboard and reporting tool design should be in sync with the drill down levels and hierarchies
Figure 2: End-to-end Data Quality Solution
Liébana-Cabanilla et al. proposed for a centralised system of datamanagement by arguing that it offers many advantages includingconsistency, accuracy, and timely across all the systems of datawithin the corporation20.He adds that this will lead to a massive reduction in thereconciliation functions and will improve the efficiency as well asthe effectiveness of different working teams within the organisation.In addition, he expressed that for the management of risks, thesystem offers tools for easier identification and correction ofcounterparty risks. He stresses that precise measurement and controlof the corporate risks is impossible in the absence of a reliable,accurate and consistent provision of data by an effective datamanagement system. While supporting his view, Minelli, Chambers andDhiraj pointed that such system is suitable because it enablesaccurate certification of data with a higher level of confidence21.In addition, he argued that a centralised system of data managementpromotes consistency and integrity that paves the way for higherconfidence in the management of decisions and reports. It is alsosignificant from the legal, audit as well as the complianceviewpoint. He concluded by stating that the system facilitates upselling and cross selling by enabling a single management system forall customers.
Data Management and Customisation
Liu and Chang suggested that the major tool for acquiring theemotional promises from the various stakeholders including customersis the customisation that can enhance loyalty22.They argue that this is relevant because of the rapidly changingfinancial atmosphere, increasing competitiveness and high churningrates of customers. Therefore, banks have to mostly, focus onlong-term customer relationship and retention. This drives thecorporation to constant revision and modification of its datamanagement system to meet the needs of customers. They add that if abanking institution is incapable of customising its DMS, then itshould consider personalisation. They continued by arguing that abank with the capability of dealing with emergency and contingency,implementing professional commercial plans and providing innovativeservices, customisation helps in increasing the reorganisation ofclient values. Otto, Lee, and Caballero supported them by expressingthat in the adoption of DMS, banks should mostly concentrate oncustomer loyalty, customer differentiation, one-to-one marketing,customer lifetime values as well as customisation23.In addition, Chan and Chan holds that the higher the functionality ofdata management the low the necessity of customisation of the datamanagement system. Variations in the process of customisation arebetween banks based on the individual features of the market hencecare is necessary during the implementation of the strategy.
Liébana-Cabanillas et al. consider customer value management as animportant component in the DMS and argues that it is necessary forensuring that the data DMS assists in refining and leveraging thebenefits of managing customer relationships24.He adds that this is necessary for measuring and understanding boththe current as well as the future value of its customers. Inaddition, Allen (2002) supports him by arguing that linking datamanagement to customer value management enables an understanding ofthe customer needs that is the first step of making the operationsefficient and effective. Therefore, the DMS must help in discoveringthe needs of customers and their value expectations. For example, Liuand Chang revealed that the cognition and requirement of data qualityamongst banking institutions in Taiwan were high because theseinstitutions relied heavily on data to discover the demands of themarket25.They added that the adoption of effective DMS would enable banks tounderstand the behavior of customers before the designation ofproducts and services. They stress that banks need to recognise thesignificance of data quality to acquire good information regardingthe needs of customers as they serve them.
On the other hand, Dorfman argues on the banking sector capacity ofgleaning existing clients as well as potentially profitable customersin the future26.He adds that this is possible because banking institutions have gooddatabases containing balance positions, and transaction files. Whilesupporting idea, Streeter stated that banking institutions have thecapability of providing the appropriate value to clients27.However, Dorfman has proposed an efficient approach for banks to tapthe highest value from customers28.Firstly, he holds that banking institutions need to conduct regularaudits to establish their opportunities and threats. Secondly, theyshould survey and collect data from customers to obtain an effectiveunderstanding of the needs and desires of customers. The conclusionis that discovering the desires of consumers and value provision issignificant for the DMS adoption in an economy.
Data Management System and Risk Management
Hormazi and Giles (2004) looked at the relationship between DMS andeffectiveness in the management of risks among various bankinginstitutions. They argue that the fact that banks perform numerousfunctions in every day, proper data management is necessary forefficient service delivery. He, therefore, proposed the utilisationof artificial neutral network (ANN) as the best approach to dealingwith the challenges involved. Fadlalla and Lin described ANN as asuccessful pattern that has applied successfully in many countriesincluding many banks in the United States29.This pattern recognition method, based on Turban, Aronson and Liangargument, is capable of learning the various configurations in thepresented data during training sessions and applies spontaneouslywhat has learned or acquired from fresh cases30.They pointed that a significant application of ANN is the approval ofbank loans because it can easily reveal potential defaulters. Theyobserved that a successful application of ANN approach is thedetection of unfamiliar patterns of credit spending that leads toexposure of deceitful charges. Therefore, conduction of decisionsupporting systems like ANN in analysing data should be the cultureof all banks in the world to ensure effective management of risksthat will promote financial stability.
Governance and Control
The DMS of any banking institution must consider various aspects suchas alignment with the specific goals of the corporation ensureproper coordination of important stakeholders from both technologyand business sides, and view technology as an enabler of businesscapabilities and driver for the needs of the corporation. Therefore,the development of an effective DMS requires the inclusion ofimportant elements such as control and governance31.Banks need to create a balance between quick tactical requirementsand long-term objectives32.In addition, an effective DMS paves the way for sustainable evolutionas the bank continues to grow and transform.
Figure 3: Governance and Control
The literature points the significance of maintaining a consistentapproach to the development, use, and evaluation of data amongbanking institutions. Efficient data management is an important toolin decision-making at diverse management levels to meet the strategicgoals of any banking corporation. The literature has also revealedthat DMS have become more significant in the contemporary world thanbefore. The formulation of the DMS should consider six major factors:the primacy of customer service, customisation of the functions ofcustomer relation management, discovering the needs of customers andbuilding morale among the workers. It should also pave the way for anefficient decision-making system to ensure effective management ofrisks. An efficient DMS should align with the specific goals andobjectives of the corporation. It is also possible to design a DMSthat brings together all the stakeholders from both technology andbusiness sides. Banks should view technology as an enabler ofcorporate capabilities. A comprehensive framework that incorporatescontrol and governance is necessary to build and sustain a matureDMS. It is also significant for banks to sustain a balance betweentactical quick wins and long-term business objectives. Finally, asuccessful DMS is the one that constructs a strong foundation whileallowing for sustainable evolution as the corporation grows.
Recommendation for Future Work
From the numerous challenges and benefits of the various DMS adoptedby banking corporations, it is now necessary to formulate acomprehensive data management strategy that will improve themanagement of risks and ensure efficiency and effectiveness inoperations. The strategy should help banks to cope with the rapidlychanging modes of business operations to respond timely to thedemands of the market, which will promote economic growth. Inaddition, the new strategy for data management should guide banks tomake the most appropriate decisions and predict future market trendsprecisely.
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Turban, E., Aronson, J. E., and Liang, T. P.. Decisionsupport systems and intelligent systems (7th ed.). UpperSaddle River, NJ: Prentice Hall PTR, 2004.
1 Baskerville, R.L. and Myers, M.D., Information systems as a reference discipline. MIS Quarterly, 2002, 26(1), 1-14
2 Sasan R. Management Information Systems Education from a Systemic Viewpoint. Systemic Practice and Action Research, 1999, 12(4). 399-408.
3 Shu, W. and Strassmann, P.A. Does information technology provide banks with profit?. Information & Management, 2005, 42(5), 781-787.
4 Shu, W. and Strassmann, P.A. Does information technology provide banks with profit?. Information & Management, 2005, 42(5), 781-787.
5 Skyrius, R.. Business Decision Making, Managerial Learning and Information. Bank of Vilnius: Luthuania, 2001.
6 Handzic Meliha. Does More Information Lead to Better Informing. Sydney: The Bank of New South Wales, 2001.
7 Liu, S and Young, R.I.M. An exploration of key information models and their relationships in global manufacturing decision support, Proc. IMechE, Journal of Engineering Manufacture, 2007, 21(1), 711-724.
8 Shu, W. and Strassmann, P.A. Does information technology provide banks with profit?. Information & Management, 2005, 42(5), 781-787.
9 Kozak, S. The role of information technology in the profit and cost efficiency improvements of the banking sector. Journal of Academy of Business and Economics, 2005, 2(1), 34-38.
10 O`Neill, B.S., and Adya, M. Knowledge sharing and the psychological contract: Managing knowledge workers across different stages of employment. Journal of Managerial Psychology, 2007, 22(1), 411-436.
11 Barachini, F. Cultural and social issues for knowledge sharing. Journal of Knowledge Management. 2009, 13(1), 98 – 110
12 Edmondson, A. The Local and Variegated Nature of Learning in Organizations: A Group-Level Perspective. Organization Science, 2002, 13(2), 128-147.
13 Liu, S and Young, R.I.M. An exploration of key information models and their relationships in global manufacturing decision support, Proc. IMechE, Journal of Engineering Manufacture, 2007, 21(1), 711-724.
14 Minelli, M., Chambers, M., & Dhiraj, A. Big data, big analytics: emerging business intelligence and analytic trends for today`s businesses. Hoboken: John Wiley & Sons, 2012.
15 Otto, B., Lee, Y. W., & Caballero, I. Information and data quality in networked business. Electronic Markets, 2011, 21(2), 79-
16 Minelli, M., Chambers, M., & Dhiraj, A.. Big data, big analytics: emerging business intelligence and analytic trends for today`s businesses. Hoboken: John Wiley & Sons, 2012.
17 Otto, B., Lee, Y. W., & Caballero, I. Information and data quality in networked business. Electronic Markets, 2011, 21(2), 79-
18 Hormozi, A. M., & Giles, S. Data mining: A competitive weapon for banking and retail industries. Information systems management, 2004, 21(2), 62-71.
19 Minelli, M., Chambers, M., & Dhiraj, A. Big data, big analytics: emerging business intelligence and analytic trends for today`s businesses. Hoboken: John Wiley & Sons, 2012.
20 Liébana-Cabanillas, F., Nogueras, R., Herrera, L. J., & Guillén, A. Analysing user trust in electronic banking using data mining methods. Expert Systems with Applications, 2013, 40(14), 5439-5447.
21 Minelli, M., Chambers, M., & Dhiraj, A.. Big data, big analytics: emerging business intelligence and analytic trends for today`s businesses. Hoboken: John Wiley & Sons, 2012.
22 Liu, N. and Chang, K. (2005). The effects of customer relationship programs on customer loyalty—an empirical study of Taiwan financial institutions. Commerce & management quarterly. 6(2), 491-514.
23 Otto, B., Lee, Y. W., & Caballero, I. Information and data quality in networked business. Electronic Markets, 2011, 21(2), 79-
24 Liébana-Cabanillas, F., Nogueras, R., Herrera, L. J., & Guillén, A. Analysing user trust in electronic banking using data mining methods. Expert Systems with Applications, 2013, 40(14), 5439-5447.
25 Liu, N. and Chang, K. The effects of customer relationship programs on customer loyalty—an empirical study of Taiwan financial institutions. Commerce & management quarterly. 2005, 6(2), 491-514.
26 Dorfman, R.. Targeting high-value professionals. Bank Marketing, 2006, 38(8), 32-36.
27 Streeter, W. Value risk. American Banker Association journal, 1999, 91(8), 15-27.
28 Dorfman, R. Targeting high-value professionals. Bank Marketing, 2006, 38(8), 32-36.
29 Fadlalla, A. and Lin, C. An analysis of the applications of neural networks in finance. Interfaces, 2001,31(4), 112-122.
30 Turban, E., Aronson, J. E., and Liang, T. P.. Decision support systems and intelligent systems (7th ed.). Upper Saddle River, NJ: Prentice Hall PTR, 2004.
31 Capgemini Financial Services. A Case For Enterprise Data Management In Banking: Many of Today’s Challenges for Banking Institutions can be Addressed by a Structured Enterprise Data Management Initiative. Rosemont: Capgemini Financial Services, 2012.
32 Hwang, H. G., Ku, C. Y., Yen, D. C., & Cheng, C. C. Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan. Decision Support Systems, 2004, 37(1), 1-21