Management Information Systems Report on Deutsche Bank Operations

This report analyzes the Management Information Systems (MIS) of Deutsche Bank, a multinational financial services company. It explores the interconnection of the bank's four main business divisions: Corporate Bank, Investment Bank, Private Bank, and DWS, highlighting their roles and dependencies. The report also examines different forms of data used by Deutsche Bank, including descriptive, diagnostic, inferential, predictive, and prescriptive analysis, and the crucial role of data warehousing in storing and managing this data. Furthermore, it touches upon the bank's Code of Conduct and the impact of GDPR data protection legislation. The report concludes with recommendations for Deutsche Bank to leverage big data strategies and technology to improve its operations and overcome challenges related to outdated technology and data management issues. The analysis is supported by a data flow diagram illustrating the interconnectedness of various departments and emphasizing the importance of effective data management for the bank's financial performance.

Management Information
Systems

Table of Contents
INTRODUCTION ..........................................................................................................................3
QUESTION 1...................................................................................................................................3
Different business divisions of Deutsche Bank are interconnected............................................3
QUESTION 2...................................................................................................................................6
Different forms of data and the role or responsibility of data warehousing in Deutsche Bank. .6
QUESTION 3...................................................................................................................................7
Information about the Code of Conduct and the new GDPR data protection legislation of
Deutsche Bank............................................................................................................................7
CONCLUSION ...............................................................................................................................7
REFERENCES ...............................................................................................................................8

INTRODUCTION
Management information system is a computer system that consists of software and
hardware, which serves as the backbone of an enterprise's operations. In simple word, this is the
study of technology, organisations, people and relationship among them (Bessler and et. al,
2018). Management information system (MIS) professional support organisations to realize
maximum benefit or advantages from investment in equipment, personal and business process.
For this report, Deutsche Bank AG is a given German multinational financial service company
and investment bank. Company was founded in 1870 by Paul Achleinter, Chrisstian Sewing and
headquartered in Frankfurt, Germany. Company specialise in providing of different services,
which are related with the investment banking, asset management, corporate banking,
commercial banking and private banking. Main purpose of this report is to identify the different
divisions of Deutsche Bank, different forms of data and the role of data warehousing in Deutsche
Bank and the information of code of conduct and many legislations.
QUESTION 1
Different business divisions of Deutsche Bank are interconnected
Deutsche Bank is a German multinational investment bank that specialise in providence
of different types of services to the customers. Deutsche Bank mainly has 4 business divisions
which are Corporate Bank, Private Bank, Investment Bank and Asset managers DWS. All these
decisions are work with each other that facilitate business organisation by improving its financial
performance in successful manner. In this organisation, there are also a different number of
infrastructure functions that performing or doing key management tasks (Epstein and et. al.,
2018). Technology team of Deutsche Bank are responsible and liable for the entire information
technology infrastructure of bank. All these divisions will be described as below:
Corporate Bank: At the core of Deutsche Bank's customer establishment is characterize
the corporate bank and market pioneer in real money the executives, trust and office, exchange
account and loaning and protections administrations. Emphasising on the finance departments
and treasures of financial institutions and corporate and commercial client across the world.
Corporate bank is the division of Deutsche Bank that is interconnected with investment bank.
Both are work with the purpose of improving financial and business performance of Deutsche
Bank.

Investment bank: This bank combines corporate finance and Deutsche bank's fixed
income & currencies as well as Deutsche bank research. This division of Deutsche bank mainly
emphasis on its traditional strengths in advisory, currencies, fixed income and financing. It gives
strategic advice or consul to corporate clients as well as includes a focused equity capital markets
business. This division is interconnected with the private and corporate bank. As this will support
Deutsche Bank in enhancing of its brand image successfully.
Private bank: This is another business division of Deutsche bank that combines the
expertise of banks in private banking with Postbank within Germany. Wealth management and
commercial bank international is one corporate division. This bank offers high quality advice as
well as wide range of financial services from a particular source. This division is interconnected
or interrelated with the technology and infrastructure (Ferreira and et. al., 2018). As private bank
requires technology to provide banking services to the customers. They also need effective
infrastructure that assist bank in improvement of its growth and development.
Infrastructure: This division or sectors service and control functions and, in specific
activity relating to group-wide, liquidity and capital management, supra-divisional resource
planning, steering and control as well as risk. This division is connected with the private and
investment bank because both banks are requiring effective infrastructure for improving the
business performance and productivity easily.
Technology: It is an important division of any type of organisation because they support
by providing technological services to the clients. From modernizing business systems
simplifying technology environment for developing industry-shaping tools. Deutsche bank has
become the first financial organisations to create professional research and development, such as
global technology centres, digital factory and global network of five innovation labs. Bank has
developed a dedicated technology, innovation and data functions that brings together their
experts under particular IT strategy (Huang and et. al., 2018). Technology division of Deutsche
Bank is interconnected with the all divisions such as private, investment, DWS etc. This
interconnection facilitates Deutsche Bank in enhancement of its business growth and success.
DWS: It is one of the main resource supervisors in the globe, with EUR 704bn of
resources under administration. Organization offer people just as foundations admittance to their
solid speculation abilities in all significant resource classes and furthermore arrangements
adjusted to advancement patterns. This division is online related with the technology because

DWS Group GmbH & Co. KGaA (DWS) require technology for providing banking services in
limited time and effective manner.
Therefore, all these are main divisions of the Deutsche bank that are interconnected with
the purpose of accomplishing business goals and objectives in successful and systemic manner.
This turn to impact on Deutsche bank by increasing its financial performance.
Illustration 1: Data flow diagrams
§
(Source: Data flow direction that helps in identifying of dependence of each department, 2020)
From the above mentioned diagram, it has been interpreted the dependence of each
department. In this computer net pay flows the information about valid payroll data, withholding
data, employee’s data and net pay & deductions. Therefore, information flows top the lower
level to middle and middle to upper level. This will be essential and significant for bank in
improving of its financial performance in systematic manner (Joosten and et. al., 2020).
Data flow direction is important part of the business success and growth. This will
facilitate banking industry in reducing the misunderstanding and also enhancing the business
performance. This will assist Deutsche bank in better understanding of their system or process,
uncover their risks, help in improving it, and assist in implementing of new system or process.

QUESTION 2
Different forms of data and the role or responsibility of data warehousing in Deutsche Bank
Big data originally emerged as the form to define larger datasets. It could not be stored,
captured, managed nor evaluated using traditional databases. Along with this, a data warehouse
is a kind of data management system, which designed to support and enable business intelligence
activities, mainly analytics. This are entirely intended to do queries and evaluate and often
include large amount or sum of historical data. Financial institutions or organisations are putting
big data or information in big manner, from encouraging cyber security in order to cultivating
loyalty of customers via personalised and innovative offerings. There are basically five types of
data analysis such as descriptive analysis, diagnostic analysis, inferential analysis, predictive
analysis and prescriptive analysis (Kasemsap, 2018). All these are main types of data analysis
that will be defined as below:
Descriptive analysis: It refers to the initial point for modelling of data. Main motive of
this analysis is to describe and define the primary attributes of the main area in a dataset. There
are certain types of data that will be analysis in this including account distributions, household
characteristics, average balance, cross-sell ratio etc.
Diagnostic analysis: By using known quantities and variables, bank can manipulate the
data or information to investigate the things they don't know. This includes the data about
product correlations, demographic clusters, customer profitability etc. This will help in
increasing of profitability and sales.
Inferential analysis: This will help banking industry or financial institutions to draw
conclusion through comparing sample set of data in against to broader data sets. This will
facilitate Deutsche bank in examining the account attrition data as well as quantitative customer
data. This will help in identifying of customer’s data in systemic manner (Levykin and Chala,
2018).
Predictive analysis: This draws on historical data to facilitate us forecast probable or
likely future results. In this, financial institutions look the previous behaviour of customers to
predict how they are probable to behave or act in future.
Prescriptive Analysis: This is another method of analysing the data in effective and
systematic manner. This will help in analysing of customer's prescriptive regarding the banking
system. As it supports in analysing of big data effectively and systematically.

Role of data warehousing in Deutsche Bank
One of the main roles of data warehousing is to maintain and record data for longer time
period that will be essential for bank in enhancement of its business performance in effective and
efficient manner. Data warehouse refers to the architecture that is used by banking industry for
maintaining critical historical information, which has been infusion from operational data or
information storage as well as transformed into formats, which is accessible to the Deutsche
bank. The development, maintenance and implementation of data, warehouse needs the active or
effective participation of a bigger cast of characters, all set of skills or knowledge. Therefore,
data warehousing plays an important and essential role in Deutsche bank by storing information
data for longer time period and also in improving financial performance of the company in
successful manner (Macchi and et. al., 2018).
Current information system
Deutsche Bank, one of the world's top money related organizations was established in
1870. This bank offers a scope of monetary items and administrations, including retail and
business banking, unfamiliar exchange, and administrations for consolidations and acquisitions.
After the 2008 bank emergency, Deutsche bank is currently battling with huge changes in the
financial business. One of their issues is that Deutsche bank submitted inadequate and
unfavourable credit default trade information and neglected to appropriately direct
representatives liable for trade information announcing. At last Deutsche bank's endeavours to
end the framework blackout caused new announcing issues adding on to the issues that as of now
exist. The trade information of Deutsche bank uncovered determined issues including various
invalid legitimate substance identifiers making this one of the banks issue (Merenkov, 2018).
These issues happened on the grounds that Deutsche bank neglected to have a satisfactory
business coherence and debacle recuperation plan for when time grants. The primary explanation
with regards to why they couldn't give the correct data is a result of their old-fashioned
innovation. Their old-fashioned innovation was a helpless data framework which caused
numerous issues since 2008. The exertion and cost to diminish this issue were too extraordinary
bringing about numerous banks utilizing the old frameworks set up to deal with the workload.
Deutsche bank attempted numerous arrangements, one of them was reporting a multi-billion-
dollar manage Hewlett-Packard to normalize and streamline its IT foundation to make a cutting

edge light-footed innovation stage. This brought about misfortunes as Cyan couldn't re-establish
benefit.
Recommendations
Construct large information methodology to remove business esteem: The colossal
standard of associations concurs that huge information is a remarkable occasion to get client bits
of knowledge, watch business activities inside continuous, conjecture organization results, etc.
Then again, it is so just if bank take advantage of this lucky break.
Technology strategy to enable big data strategy: On the back of the gigantic data
procedure/control, enlighten an endeavour wide advancement strategy for huge data the chiefs. A
couple of segments that will go into such a system would be guides, tendencies, organization,
etc. In the advancement procedure document you could detail out a guide for creating from
coordinated to semi-coordinated to unstructured data plans – the three-adventure enormous data
framework. Since the idea should be to dodge soloed data vaults, the development technique
should configuration supported stages and interfaces for getting, taking care of, and taking care
of each data plan. These stages must match as a lone development stack rather than as individual
advances, and as needs be, the development approach ought to in like manner address the
consolidation control for these stages. Finally, the strategy ought to similarly anticipate and
address the cut-off, storing and versatility troubles of what might be on the horizon (Pešalj,
Pavlov and Micheli, 2018).
Advantages and disadvantages of the information system
Advantages
Correspondence – with help of data advances the prompt informing, voice, messages and video
calls turns out to be quicker, less expensive just as much productive.
Openness – information structures has made it serviceable for associations to be open 24×7
wherever on the globe. This suggests that a business can be open at whatever point wherever,
making purchases from different countries easier and more worthwhile. It furthermore suggests
that you can have your items passed on right to your doorstep with moving a singular muscle.
Disadvantages
Implementation expenses –In order to incorporate the information system, it need attractive good
amount of price in a case of hardware, software and people. Programming, equipment and some

different administrations ought to be leased, purchased and upheld. Representatives should be
prepared with new data innovation and programming (Tian and et. al., 2019).
Security issues: Hackers and thieves obtain access to determine as well as corporate saboteurs
target responsive corporation data. This type of data can cover intellectual property, bank
records, vendor information and personal data on business management (Rantos, and et. al.,
2018).
QUESTION 3
Information about the Code of Conduct and the new GDPR data protection legislation of
Deutsche Bank
GDPR: The General Data Protection Regulation (EU) 2016/679 (GDPR) is a guideline in
EU law on information insurance and security in the European Union (EU) and the European
Economic Area (EEA). At the point when the handling depends on assent the information
subject has the option to repudiate it whenever. The Deutsche Bank AG as regulator inside the
importance of the General Data Protection Regulation ("GDPR") gathers and cycles individual
information and other data of you when utilizing Online Banking. The accompanying data gives
an outline of how we measure your own information as to our Online Banking. (Sarkar and et.
al., 2019)
Preparing will be legal just if and to the degree that at any rate one of the accompanying applies:
• the data subject has offered consent to the planning of their own data for at any rate one
express purposes;
• preparing is significant for the introduction of a consent to which the data subject is party or
to make progress in accordance with the data subject prior to going into an understanding;
• handling is fundamental for consistence with a legitimate promise to which the controller is
subject;
• handling is basic to make sure about the crucial interests of the data subject or of another
normal person;
• handling is key for the display of a task finished in the public interest or in the movement of
real force vested in the controller;

• preparing is basic for the inspirations driving the veritable interests searched after by the
controller or by an outcast, beside where such interests are supplanted by the interests or
chief rights and chances of the data subject which require security of individual data,
explicitly where the data subject is a youth.
• 2Point of the first subparagraph won't have any critical bearing to dealing with finished by
open specialists in the introduction of their tasks.
Part States may keep up or acquaint more explicit arrangements with adjust the use of the
standards of this Regulation as to handling for consistence with focuses (c) and (e) of passage 1
by deciding all the more absolutely explicit prerequisites for the preparing and different
measures to guarantee legitimate and reasonable handling including for other explicit preparing
circumstances as accommodated in Chapter IX (Soto-Acosta, Del Giudice and Scuotto, 2018).
Conditions for consent
Where handling depends on assent, the regulator will have the option to exhibit that the
information subject has agreed to preparing of their own information (Suša Vugec, Tomičić-
Pupek and Vukšić, 2018).
 If the information subject's assent is given with regards to a composed statement which
additionally concerns different issues, the solicitation for assent will be introduced in a
way which is unmistakably discernable from different issues, in a comprehensible and
effectively open structure, utilizing clear and plain language. 2Any piece of such a
statement which establishes an encroachment of this Regulation will not be authoritative.
 The information subject will reserve the option to pull out their assent whenever.
 The withdrawal of assent will not influence the legitimateness of handling dependent on
assent before its withdrawal.
 Prior to giving assent, the information subject will be educated thereof.
 It will be as simple to pull out as to give assent.
 While evaluating whether assent is unreservedly given, most extreme record will be taken
of whether, bury alia, the presentation of an agreement, including the arrangement of a
help, is restrictive on agree to the preparing of individual information that isn't essential
for the exhibition of that agreement

CONCLUSION
From the above mentioned information, it has been concluded that there are different
division of business that are interconnected with each other. This supported financial institutions
in improving of its financial performance successfully. Data flow diagram is facilitated business
organisation in flowing of information to the lower to middle and to upper level. Data
warehousing played important role in maintaining the data record for longer time period. Code of
conduct and different legislations helped banking industry in attaining of competitive advantages
successfully.

REFERENCES
Books and journals
Bessler, S. and et. al, 2018. Systems and methods for automatic generation of a relationship
management system. U.S. Patent 9,898,743.
Epstein, R.H. and et. al., 2018. Perioperative temperature measurement considerations relevant to
reporting requirements for national quality programs using data from anesthesia
information management systems. Anesthesia & Analgesia, 126(2), pp.478-486.
Ferreira, J.C. and et. al., 2018. An energy management platform for public
buildings. Electronics, 7(11), p.294.
Huang, Y. and et. al., 2018. Agricultural remote sensing big data: Management and
applications. Journal of Integrative Agriculture, 17(9), pp.1915-1931.
Joosten, A. and et. al., 2020. Anesthetic management using multiple closed-loop systems and
delayed neurocognitive recovery: a randomized controlled trial. Anesthesiology, 132(2),
pp.253-266.
Kasemsap, K., 2018. Mastering business process management and business intelligence in global
business. In Global Business Expansion: Concepts, Methodologies, Tools, and
Applications (pp. 76-96). IGI Global.
Levykin, V. and Chala, O., 2018. Method of automated construction and expansion of the
knowledge base of the business process management system. EUREKA: Physics and
Engineering, (4), pp.29-35.
Macchi, M. and et. al., 2018. Exploring the role of digital twin for asset lifecycle
management. IFAC-PapersOnLine, 51(11), pp.790-795.
Merenkov, A., 2018. Digital economy: transport management and intelligent transportation
systems. E-management, 1(1), pp.12-18.
Pešalj, B., Pavlov, A. and Micheli, P., 2018. The use of management control and performance
measurement systems in SMEs: A levers of control perspective.
Rantos, K. and et. al., 2018, July. Blockchain-based Consents Management for Personal Data
Processing in the IoT Ecosystem. In ICETE (2) (pp. 738-743).
Sarkar, B. and et. al., 2019. Product Channeling in an O2O supply chain management as power
transmission in electric power distribution systems. Mathematics, 7(1), p.4.
Soto-Acosta, P., Del Giudice, M. and Scuotto, V., 2018. Emerging issues on business innovation
ecosystems: the role of information and communication technologies (ICTs) for
knowledge management (KM) and innovation within and among enterprises. Baltic
Journal of Management.
Suša Vugec, D., Tomičić-Pupek, K. and Vukšić, V.B., 2018. Social business process
management in practice: Overcoming the limitations of the traditional business process
management. International Journal of Engineering Business Management, 10,
p.1847979017750927.