CMPT 641 – Individual Assignment #2: Digital transformation strategy for UCW Registrar's Office

Question

Task: How can the UCW Registrar's Office leverage digital transformation strategy to improve operational efficiency and enhance customer experience in admissions processes?

Answer

Problem Statement

The Registrar's Office at UCW offers a wide range of services and employs various operational procedures. In order to leverage modern digital technologies to raise the calibre of their procedures, they are searching for a digital transformation strategy. You were tasked with creating their digital transformation strategy and following the project's digital transition.

You need to follow these steps to develop the plan.

1- Writing the introduction

Please provide your answer for the following questions for Managers of this department

a. What is digital transformation strategy?

In order to radically alter how an organisation runs and provides value, digital transformation strategy refers to the integration of digital technology and solutions into its processes, operations, and culture. It entails utilising digital technology to spur innovation, boost efficiency, improve consumer experiences, and develop new business models, such as automation, data analytics, cloud computing, artificial intelligence, and online platforms (Lanzolla, Lorenz, Miron-Spektor, Schilling, Solinas, & Tucci, 2020).

b. What are the benefits of a digital transformation strategy.

Improved Efficiency

Processes are streamlined, manual operations are automated, and operational inefficiencies are reduced as a result of digital transformation strategy, which increases productivity and lowers costs.

Enhanced Customer Experience

With the use of digital technology, interactions with consumers may be personalised and seamless, giving them access to simple self-service alternatives, quicker response times, and customised experiences (Ulas, 2019).

Data-driven Insights

Organisations may gather, analyse, and use a lot of data thanks to digital transformation strategy to get useful insights for planning, forecasting, and enhancing operational efficiency.

Competitive Advantage

By fostering innovation, agility, and the capacity to adjust to changing market trends and consumer expectations, adopting digital transformation strategy enables organisations to stay one step ahead of the competition.

Scalability and Growth

Digital technologies give businesses the scale and flexibility they need to expand into new areas and swiftly adapt to shifting customer expectations.

Collaboration and Communication

Better information sharing, teamwork, and creativity are made possible through enhanced collaboration and communication both inside and beyond departments.

c. What are the risks of a digital transformation strategy.

Resistance to Change

New technology, procedures, and job positions may be met with resistance or difficulty by employees, which lowers productivity and morale.

Security and Privacy Concerns

A lack of adequate security measures increases the danger of data breaches, cyberattacks, and privacy violations since digital transformation strategy includes managing and storing vast volumes of data.

Integration Challenges

It may be difficult and time-consuming to integrate new digital systems with legacy ones, which can cause operational disruptions and compatibility problems (Andriushchenko, et al., 2020).

Cost and ROI

Initiatives for digital transformation strategy sometimes include substantial expenditures in technological infrastructure, training, and implementation, and if projects are not well planned and carried out, there is a danger of not getting the required return on investment.

Data Quality and Governance

To prevent making judgements that are biassed or wrong because of faulty data, it is crucial to place a strong emphasis on data quality, integrity, and governance.

Vendor Dependence

Adopting digital solutions might raise a company's reliance on outside suppliers, which could increase the risk of vendor lock-in, service interruptions, and support issues.

d. What are the key steps of a digital transformation strategy project.

1. Evaluate present procedures, pinpointing trouble spots and potential areas for development.

3. Examine available digital technologies and solutions to determine which ones best satisfy the demands and specifications.

4. Redesign workflows and processes to conform to the selected digital technology.

5. Create an implementation strategy that includes deadlines, resource allocation, and significant checkpoints.

6. Carry out change management and training initiatives to make sure the new technology and procedures are adopted smoothly.

7. Use key performance indicators (KPIs) to track and assess the accomplishment of initiatives that have been put into action.

8. On the basis of feedback and changing demands, continuously assess and enhance the digital solutions that have been put in place.

9. Ensure that privacy and security protocols are in place to safeguard data and adhere to applicable laws.

10. Encourage cross-departmental cooperation and stakeholder integration to maximise the effects of digital transformation strategy initiatives (Warner & Wäger, 2019).

Understanding the current state of the scope

In this step, you need to draw the Enterprise Architecture (As Is) for your scope.

Note #1: You need to draw EA for the scope that assigned to your team, but you need to complete this assignment individually. To find your assigned scope, check your Teams in the following address

Note #2: To draw your Enterprise Architecture (As Is), Complete Table 2 and explain it.

Table 2: Enterprise Architecture (As Is)

Business Architecture

Visio Screenshot

Visio Link  https://guscanada-my.sharepoint.com/:u:/g/personal/sagardeep_singh8319_myucw_ca/EePDfs_PCkJAvxjC2pAdRloB18jDvItNj_OY1VSY0SGFCA?e=KJfx3E

Process Advisor Screenshot

Process Advisor Link https://guscanada-my.sharepoint.com/:u:/g/personal/sagardeep_singh8319_myucw_ca/EePDfs_PCkJAvxjC2pAdRloB18jDvItNj_OY1VSY0SGFCA?e=KJfx3E

Data Architecture

Applicant Data: Personal Data: Consists of the applicant's name, contact information, birthdate, and other identifying data. Academic History: Details on the applicant's prior educational settings, degrees obtained, and academic standing. Supporting Documents: These comprise transcripts, letters of recommendation, essays, and any other paperwork submitted as part of the application (Anthony Jnr, 2021). Admissions Data: Application Information: This section contains information on submitted applications, including the application form, the submission date, and the program(s) that interest the applicant. Application Status: Monitors how an application is progressing (submitted, being reviewed, accepted, denied, or waiting list). Admissions Decision: Indicates whether the applicant was admitted, denied, or placed on the waitlist. Communication History: Keeps track of all correspondence, including emails, letters, and other exchanges, between the admissions team and the applicant. Document Management: Document Repository: Keeps digital copies of papers submitted by applicants, including transcripts, letters of recommendation, and essays. Document Metadata: Records information about each document, such as the kind of document, the date of submission, and the file format. Student Information System (SIS): Student Profile: Information about each approved and enrolled applicant is sent to the SIS, where it is used to create a student profile. Course Registration: Records the course codes, names, and schedules that the student has registered for. Enrolment Status: Indicates whether the student is currently enrolled (active, on leave, graduated, or withdrawn). Reporting and Analytics: Admissions Metrics: Gathers and examines information to produce reports on statistics pertaining to admissions, including applicant numbers, acceptance rates, and demographics. Performance tracking: keeps track of the admissions process' key performance indicators (KPIs), which provide information for decision-making and process improvement. Integration and Interfaces: Data Integration: Creates interfaces or points of integration with external systems, such as document management programmes, external databases for validation, or notification channels. User Interfaces: Offers user interfaces so that applicants, admissions staff, and pertinent data, such as application forms, applicant profiles, and communication tools, may be accessed and used. Data Governance: Data Standards: To guarantee accuracy and consistency, standards for data formats, naming conventions, and quality are established. Data Privacy and Security: Puts in place procedures to safeguard applicant data, abide by data protection laws, and regulate access.

Application Architecture

The software programmes and systems used to manage and support the admissions process are included in the application architecture for the admissions process at the Registrar's Office (Pattij, van de Wetering, & Kusters, 2022). The essential elements are explained as follows:   Admissions System: It has tools for accepting and processing applications, tracking applicant data, monitoring admissions decisions, and creating reports. This is the core application that simplifies the complete admissions process. Depending on the stage of the admissions process, the system may comprise modules or features for application submission, document verification, applicant review, and decision-making. Application Form: This is the application form that prospective students utilise to submit their admissions requests. The application form may be made accessible in a variety of formats, such as downloadable/printable forms or online web forms. The form could have sections for personal data, educational background, programme preferences, and other necessary information. Document Management System: The administration, storage, and retrieval of application elements are all supported by this system. It offers the ability to upload, classify, index, and retrieve documents. To speed up the document review process, the system could include tools for document versioning, access controls, and workflow management. Communication Channels: Throughout the admissions process, these are the platforms utilised to connect and communicate with candidates. Email, internet portals, and sometimes postal mail are typical communication methods. To enable automatic alerts, updates, and requests for further documents from applicants, the system should interface with these channels. Integration Interfaces: These connections allow information to be sent from the admissions system to other systems or outside sources. Utilising integration interfaces, candidate data may be verified against external databases like those of academic institutions or verification services. Once a candidate has been approved and enrolled, they can additionally activate data synchronisation with the Student Information System (SIS). Reporting and Analytics: This part of the system is concerned with producing reports and carrying out data analysis in relation to the admissions procedure. It could consist of reporting features or modules in the admissions system that offer information on the quantity and quality of applications received, the acceptance rate, demographics, and other important data. The admissions process may be made better by identifying trends, patterns, and problem areas with the use of data visualisation and analytics. Security and Access Controls: The applications and data used in the admissions process are secure and accurate thanks to this component. To secure applicant information, it entails steps like user identification, role-based access controls, and data encryption. The design of the application architecture should take into account compliance with any data privacy laws.

Business Architecture explanation

The general structure, functions, and interactions within the organisation to accomplish the aims and objectives connected to admissions are all included in the business architecture for the admissions process in the Registrar's Office (Gong, Yang, & Shi, 2020). The essential elements are explained as follows:

Admissions Policies and Procedures:

• The policies, regulations, and practises that are in writing and regulate the admissions process are included in this component.

• It describes the standards used to evaluate applicants, admission quotas, unique circumstances, and exceptions.

• The policies and procedures give direction to the admissions personnel so that decisions are made consistently and fairly.

Admissions Staff:

• The people participating in the admissions process, including as admissions officers, administrators, and support staff, are represented by this component.

• The admissions department is in charge of managing application submissions, document validation, application evaluation, and applicant communication.

• They are essential to the evaluation of applications, the decision-making process, and the proper operation of the admissions process.

Collaboration and Communication:

• Within the Registrar's Office and with outside stakeholders, this component emphasises developing teamwork and good communication.

• For effective information sharing and decision-making, cooperation between admissions personnel, academic units, and other administrative units is crucial.

• To offer updates, seek further papers, and inform applicants of admissions decisions, communication with applicants is essential.

Applicant Experience:

• This element focuses on the admissions process experience of potential students.

• It entails delivering understandable and accessible application information, simple application forms, and prompt applicant communication.

• A great applicant experience demonstrates the university's dedication to student happiness and aids in attracting and keeping talented students.

Performance Monitoring and Continuous Improvement:

• Key performance indicators (KPIs) are tracked and measured as part of this component's evaluation of the admissions process' efficacy and efficiency.

• Application volume, approval rates, decision-making speed, and applicant satisfaction indicators are a few examples of KPIs.

• The implementation of process improvements and the identification of opportunities for improvement are made possible by the regular examination of these indicators.

Stakeholder Engagement:

• This part of the process focuses on interacting with various parties engaged in admissions.

• Academic units, professors, university administration, outside organisations, and applicants themselves can all be considered stakeholders.

• Engaging stakeholders through advisory committees, feedback systems, and cooperative efforts ensures that the admissions process is in line with the expectations of all stakeholders and with the organization's strategic goals.

In order to accomplish efficient and successful admissions operations, the business architecture for the admissions process makes sure that the rules, processes, and resources are coordinated. To optimise the admissions process and support the overall goal of the institution, it places a strong emphasis on teamwork, applicant experience, performance monitoring, and stakeholder involvement.

Data Architecture explanation

The Registrar's Office's data architecture for the admissions process focuses on how to organise, structure, and manage data to support admissions operations and facilitate efficient data usage (Kattel & Mergel, 2019). The essential elements are explained as follows:

Data Sources:

• The origin of the data utilised in the admissions process is referred to as the data source. Among these sources are possible application forms, supplementary materials, outside databases, and other pertinent sources.

• To provide a centralised and trustworthy data repository, the data architecture ensures the identification, integration, and consolidation of data from different sources.

Data Models:

• The structure, connections, and characteristics of the data items utilised in the admissions process are defined by data models.

• To represent applicant information, application specifics, admissions decisions, correspondence records, and other pertinent entities, the architecture incorporates logical and physical data models.

• A standardised structure for storing, organising, and retrieving data is provided by data models.

Data Storage and Management:

• This part of the system is concerned with the administration and storage of admissions-related data.

• Documents, communication logs, application data, applicant information, and other pertinent data are stored in databases, data warehouses, or data lakes.

• Data storage solutions provide data security, scalability, and integrity in order to handle the increasing amount of admissions-related data.

Data Integration:

• The process of merging data from diverse systems and sources used in the admissions process is referred to as data integration.

• To facilitate seamless data flow across applications, systems, and databases, the design integrates data integration techniques such as Extract, Transform, Load (ETL) procedures, data connectors, and APIs.

• Through integration, the admissions process is made to have accurate, consistent, and accessible application data.

Data Quality and Governance:

• Data quality and governance procedures guarantee the consistency, accuracy, and completeness of the data utilised in the admissions process.

• To ensure data integrity, the architecture has mechanisms for data validation rules, data cleaning processes, and data quality monitoring.

• In order to specify data ownership, access restrictions, data protection safeguards, and compliance with pertinent rules, data governance frameworks are put into place.

Analytics and Reporting:

• This part of the system focuses on using data to produce reports, analytics, and insights into the admissions procedure.

• The data architecture makes it possible to extract, convert, and analyse admissions data to produce reports on demographics, application trends, acceptance rates, and other important variables.

• Analytics tools and reporting frameworks support data-driven decision-making and on-going admissions process improvement.

The admissions process's data architecture guarantees the data's accessibility, availability, and integrity at all times. It offers efficient data integration, storage, governance, and analytics, assisting in the optimisation of the Registrar's Office's admissions procedures and aiding well-informed decision-making (Mendhurwar & Mishra, 2021).

Application Architecture explanation

The Registrar's Office's application architecture for the admissions process is centred on the computer programmes, networks, and interactions that support and streamline the many steps in the admissions process (Sanchis, García-Perales, Fraile, & Poler, 2019). The essential elements are explained as follows:

Admissions System:

• The primary application that controls the whole admissions process is the admissions system.

• Applications can be submitted, documents can be managed, candidates can be tracked, applications can be reviewed, and admissions decisions can be made.

• Modules for maintaining applicant profiles, producing notifications, and producing reports might all be included in the system.

Application Portal:

• The channel via which potential students access and submit their applications is the application portal.

• It offers candidates a user-friendly and secure online platform where they can fill out application forms, add supporting materials, and check the progress of their applications.

• The portal could provide options for document uploads, form validation, and progress monitoring.

Document Management System:

• The safe storing, retrieval, and processing of application papers is done via the document management system.

• The management and examination of papers including transcripts, letters of reference, essays, and other supporting materials may be done quickly and effectively by the admissions team thanks to this system.

• The system may provide functionality for workflow management, document versioning, access restrictions, and indexing.

Communication and Notification System:

• Throughout the admissions process, this element makes communication between the admissions office and candidates easier.

• In order to keep candidates updated on their application progress, document requests, and admissions decisions, it provides functions like automatic email notifications, online messaging, and alarms.

• The method could also facilitate internal coordination and collaboration among admissions staff members.

Integration Interfaces:

• Data interchange between the admissions system and other systems or outside organisations is made possible through integration interfaces.

• They make it easier to integrate data from outside sources, including academic databases or verification services, for the validation and verification of application data.

• Once an applicant has been approved and enrolled, integration interfaces can link the admissions system with the Student Information System (SIS).

Reporting and Analytics:

• This part of the system is concerned with producing reports and offering analytical information on the admissions procedure.

• It has dashboards and reporting tools that let management and the admissions team keep track of performance, track applicant analytics, and find areas for improvement.

• Analytics skills may enhance data-driven decision-making by identifying trends, patterns, and correlations in admissions data

Security and Access Controls:

• To safeguard applicant information and guarantee the fairness and discretion of the admissions process, security measures are essential.

• To protect sensitive data, the architecture has data privacy protections, role-based access restrictions, encryption, and authentication procedures.

• Consideration and implementation of compliance with pertinent data protection legislation, such as GDPR or HIPAA, should be made.

These elements are brought together in the application architecture for the admissions process to produce a seamless, streamlined system that facilitates the effective handling of applications, documentation, communication, and decision-making (Roth, 2019). While enabling admissions personnel to efficiently process and analyse applications, it guarantees a user-friendly experience for applicants.

3- Analyzing the current state of the scope

Table 3: SWOT Analysis

Strength

Automated Decision-making (Using Big Data Analytic) steps: Improved Decision Accuracy: Automated decision-making utilising big data analytics may take use of massive data sets to create judgements that are more precise and well-informed. Better results and increased admissions process effectiveness may result from this. Speed and Efficiency: By processing and analysing data considerably more quickly than human techniques, automation shortens the time needed for decision-making. This facilitates prompt admissions choices and speeds up the application process. Scalability: Big data analytics systems are made to manage a lot of data, enabling scalability as the number of applications rises. This guarantees that the system can support growth without compromising efficiency. Big data analytics-driven automated decision-making offers insightful data about applicant patterns, preferences, and performance. Strategic choices about programme offers, marketing initiatives, and budget allocation may be made using these findings (Preindl, Nikolopoulos, & Litsiou, 2020). Automated Resource Management (Using Cloud Computing) Steps: Flexibility and Scalability: By enabling the Registrar's Office to scale computing resources up or down in response to demand, cloud computing offers flexibility in resource management. This guarantees efficient resource allocation during busy times and cost savings during slow times. Improved dependability: Cloud computing provides high availability and dependability, reducing system downtime and guaranteeing on-going access to crucial applications and data for admissions staff and candidates. Cost effectiveness: Because the Registrar's Office may use the infrastructure of cloud service providers, cloud computing reduces the requirement for substantial upfront infrastructure expenditures. This lowers construction expenses and makes operational costs depending on consumption predictable. cooperation and Accessibility: Since cloud-based resource management allows admissions personnel to access and work on apps and data from any location with an internet connection, cooperation is made easier. This promotes collaboration and raises output (Fenech, Baguant, & Ivanov, 2019). Automated Data Collection (Using IoT) Steps: Real-time Data Collection: IoT devices have the ability to gather and send data in real-time, giving accurate information on a variety of admissions-related topics, including application submission, document validation, and applicant tracking. Increased Efficiency and Accuracy: Automated data collecting with IoT devices lowers the possibility of mistakes and delays that might happen with human data entering. This increases the admissions process's overall correctness and effectiveness. IoT devices may be used to track and monitor the movement of tangible documents during the admissions procedure, such as transcripts or recommendation letters. This makes document management more transparent and accountable. Simplified processes: The Registrar's Office can simplify processes and get rid of time-consuming manual chores by automating data collecting using IoT devices. This frees up staff members' time so they may concentrate on more worthwhile tasks (Savastano, Amendola, Bellini, & D’Ascenzo, 2019).

Weaknesses

Manual Steps

Decision-making steps: Human error is a risk in manual decision-making, which can result in biases, mistakes, and inconsistent results in the admissions process. Manual decision-making procedures can take a lot of time, especially when processing a lot of applications. Delays in admissions decisions and extended application processing timeframes may result from this. Limited Data Analysis: When making decisions manually, the full potential of the data that is available for analysis may not be realised. It becomes difficult to analyse and extract valuable insights from complicated and massive datasets without automated tools and algorithms. Lack of Consistency: Because various people may perceive and assess application data differently, manual decision-making procedures may not be consistent or standardised. This may lead to candidates being treated differently and raise questions about fairness (Stich, Zeller, Hicking, & Kraut, 2020).   Resource management steps: Manual resource management can lead to inefficient resource allocation, which can affect facilities, equipment, and staff. This may result in the under- or overuse of resources, which would reduce overall operational effectiveness and efficiency. Scaling Challenges: Manual resource management techniques may find it challenging to grow as the number of applications and administrative activities rise. Without an equal increase in employees or resources, managing increased workloads becomes difficult. Real-time visibility into resource availability and utilisation is frequently lacking in manual resource management. This may lead to ineffective scheduling, disputes, and challenges with resource coordination. Limited Accessibility: Physical papers, spread sheets, or manual tracking systems may be used in manual resource management procedures, which can limit accessibility and make it difficult for staff members to collaborate. Delays in the decision-making process for resource allocation may result from this.   Data collection steps: Error Prone: Manual data gathering increases the likelihood of errors, such as incorrect data input or omitted information. This might affect the validity and reliability of the data, which would be problematic for decision-making and analysis. Time-consuming: Manual data gathering procedures can take a long time and need a lot of work to obtain and input data. This may cause delays in processing applications and producing results on time. Real-time insights into application trends and progress are scarce with manual data collecting. Monitoring shifting trends and acting quickly to address new problems becomes difficult. Data integration is a challenging and error-prone operation since manual data collecting frequently entails many sources and formats. This may make it more difficult to gather all candidate data into one perspective for analysis and decision-making.

Maturity Assessment

Big Data Analytic

Radar Chart Screenshot:

Radar Chart link: https://guscanada-my.sharepoint.com/:x:/g/personal/sagardeep_singh8319_myucw_ca/EXp2eOSUlcZEoOUQFArOna8BV_cysAIEuncta7zeX6-k1Q?e=e79PQa

Cloud Computing

Radar Chart Screenshot:

Radar Chart link: https://guscanada-my.sharepoint.com/:x:/g/personal/sagardeep_singh8319_myucw_ca/EXp2eOSUlcZEoOUQFArOna8BV_cysAIEuncta7zeX6-k1Q?e=t07fr8

IoT

Radar Chart Screenshot:

Radar Chart link: https://guscanada-my.sharepoint.com/:x:/g/personal/sagardeep_singh8319_myucw_ca/EXp2eOSUlcZEoOUQFArOna8BV_cysAIEuncta7zeX6-k1Q?e=Xpg9i0

Note #1: You can use this video to learn how to draw a radar chart in excel.

Note #2: To draw a radar chart for each digital Technology you can use the maturity Assessments in course shell.

Strength Explanation

The Registrar's Office's digital transformation strategy strategy includes a number of essential technologies with varying capabilities that enable appreciable improvements in their operations (Chawla & Goyal, 2022). Let's look at the advantages depending on the aforementioned variables and technologies:

1. Automated Decision-making (Using Big Data Analytics):

Strengths: Strong big data analytics capabilities are made possible by the use of Apache Hadoop, Apache Spark, Apache Flink, Tableau, and SAS Analytics. Advanced data processing, data visualisation, and analytical tools are provided by these technologies. They enable the analysis of enormous volumes of data, the creation of insightful understandings, and highly accurate and effective data-driven judgements.

2. Automated Resource Management (Using Cloud Computing):

Strengths: The Registrar's Office now has a flexible and scalable infrastructure because to the introduction of cloud computing technologies including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM Cloud, Salesforce Cloud, and Oracle Cloud. On-demand resource provisioning, excellent dependability, and cost effectiveness are all features of these cloud systems. They make it simple to scale computer resources, guaranteeing top performance and economical resource management.

3. Automated Data Collection (Using IoT):

Strengths: The Registrar's Office now has real-time data collecting capabilities thanks to the integration of the Raspberry Pi, Arduino, AWS IoT Core, Google Cloud IoT Core, Microsoft Azure IoT Hub, and IBM Watson IoT Platform. These Internet of Things (IoT) technologies allow for precise and effective data collecting, tracking, and asset monitoring. They provide simplified processes, lowering manual labour requirements and boosting overall effectiveness.

The overall goal of the digital transformation strategy strategy is to improve decision-making, resource management, and data gathering procedures by using cutting-edge technology like big data analytics, cloud computing, and IoT. The advantages include the capacity to efficiently manage resources on demand with dependability, gather and track data in real-time for faster processes, and extract insights from enormous amounts of data. These advantages will boost the Registrar's Office's operational effectiveness, decision-making abilities, and level of collaboration.

Weaknesses Explanation

Although the Registrar's Office's digital transformation strategy strategy includes a number of reliable technologies, it is crucial to take into account any potential flaws that might result from their application (Safiullin & Akhmetshin, 2019). On the basis of the aforementioned factors and technology, let's look at the weaknesses:

1. Automated Decision-making (Using Big Data Analytics):

Weaknesses: The complexity of deploying and administering big data analytics solutions may be one. To properly manage the infrastructure, data processing, and analytics tools, specialised knowledge and skills may be needed. Additionally, assuring data quality and accuracy may provide difficulties, particularly if the data sources are many and poorly connected.

2. Automated Resource Management (Using Cloud Computing):

Weaknesses: The reliance on cloud computing technology may result in security and privacy issues with data. Strong security processes and safeguards must be in place to safeguard sensitive data. Dependence on outside service providers and the danger of service interruptions or outages might be another potential vulnerability. If not properly handled, these factors could have an adverse effect on the operations of the Registrar's Office.

3. Automated Data Collection (Using IoT):

Weaknesses: Managing and integrating a wide variety of devices and sensors may be difficult when using IoT technology. To guarantee seamless connectivity, data transfer, and compatibility across various IoT components, rigorous planning and coordination are necessary. Additionally, the use of IoT devices need on-going maintenance and monitoring to handle problems with device failure, data synchronisation, and data accuracy.

It is crucial to take the necessary steps to rectify these deficiencies. This entails making investments in personnel training and skill development to manage and utilise the technologies efficiently, putting in place strong security measures to secure data, and creating backup and redundancy plans to reduce risks related to service outages. The Registrar's Office may achieve a successful digital transformation strategy with reduced risks and optimised technology use for their procedures by addressing these shortcomings.

References:

Andriushchenko, K., Buriachenko, A., Rozhko, O., Lavruk, O., Skok, P., Hlushchenko, Y., et al. (2020). Peculiarities of sustainable development of enterprises in the context of digital transformation strategy. Entrepreneurship and sustainability issues, 7(3), 2255.

Anthony Jnr, B. (2021). Managing digital transformation strategy of smart cities through enterprise architecture–a review and research agenda. Enterprise Information Systems, 15(3), 299-331.

Chawla, R. N., & Goyal, P. (2022). Emerging trends in digital transformation strategy: a bibliometric analysis. Benchmarking: An International Journal, 29(4), 1069-1112.

Fenech, R., Baguant, P., & Ivanov, D. (2019). The changing role of human resource management in an era of digital transformation strategy. Journal of Management Information & Decision Sciences, 22(2).

Gong, Y., Yang, J., & Shi, X. (2020). Towards a comprehensive understanding of digital transformation strategy in government: Analysis of flexibility and enterprise architecture. Government Information Quarterly, 37(3).

Kattel, R., & Mergel. (2019). Estonia's digital transformation strategy: Mission mystique and the hiding hand.

Lanzolla, G., Lorenz, A., Miron-Spektor, E., Schilling, M., Solinas, G., & Tucci, C. L. (2020). Digital transformation strategy: What is new if anything? Emerging patterns and management research. Academy of Management Discoveries, 6(3), 341-350.

Mendhurwar, S., & Mishra, R. (2021). Integration of social and IoT technologies: architectural framework for digital transformation strategy and cyber security challenges. Enterprise Information Systems, 15(4), 565-584.

Pattij, M., van de Wetering, R., & Kusters, R. (2022). Enhanced digital transformation strategy supporting capabilities through enterprise architecture management: a fsQCA perspective. Digital Business, 2(2).

Preindl, R., Nikolopoulos, K., & Litsiou, K. (2020). Transformation strategies for the supply chain: The impact of industry 4.0 and digital transformation strategy. Supply Chain Forum: An International Journal, 21(1), 26-34.

Roth, S. (2019). Digital transformation strategy of social theory. A research update. Technological Forecasting and Social Change, 146, 88-93.

Safiullin, M. R., & Akhmetshin, E. M. (2019). Digital transformation strategy of a university as a factor of ensuring its competitiveness. International Journal of Engineering and Advanced Technology, 9(1), 7387-7390.

Sanchis, R., García-Perales, Ó., Fraile, F., & Poler, R. (2019). Low-code as enabler of digital transformation strategy in manufacturing industry. Applied Sciences, 10(1), 12.

Savastano, M., Amendola, C., Bellini, F., & D’Ascenzo, F. (2019). Contextual impacts on industrial processes brought by the digital transformation strategy of manufacturing: A systematic review. Sustainability, 11(3), 891.

Stich, V., Zeller, V., Hicking, J., & Kraut, A. (2020). Measures for a successful digital transformation strategy of SMEs. Procedia Cirp, 93, 286-291.

Ulas, D. (2019). Digital transformation strategy process and SMEs. Procedia Computer Science, 158, 662-671.

Warner, K. S., & Wäger, M. (2019). Building dynamic capabilities for digital transformation strategy: An ongoing process of strategic renewal. Long range planning, 52(3), 326-349.