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With our Knowledge Base, you will have access to a wide range of important questions designed to give you results by urgency and scope.
This means that you will be able to prioritize and address urgent needs while also considering the overall scope of your IT risk management strategy.
Not convinced yet? Let us explain the benefits of using our IT Strategy in IT Risk Management Knowledge Base.
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The prioritized requirements will help you focus on what really matters, and the solutions and benefits will guide you towards the most effective strategies for your business.
Furthermore, our Knowledge Base is specifically tailored for professionals like you who are looking for a product that is easy to use and provides immediate results.
It is a DIY/affordable alternative that eliminates the need for expensive consultants or complicated software.
You′ll have all the information you need at your fingertips.
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Our product offers a level of detail and specification overview that is unmatched in the market.
We also pride ourselves on providing a product type that is unique and not just a semi-related version of another product.
With our Knowledge Base, you get exactly what you need for your IT risk management strategy.
Not only does our product save you time and effort, but it also has many benefits for your business.
Our research on IT Strategy in IT Risk Management is constantly updated, so you can be sure that you are getting the most relevant and accurate information.
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Does your it department currently have a formal strategy for dealing with big data analytics? How do qualitative researchers proceed when it comes to analyzing data from multiple cases? What it services are needed to support, enable or automate critical complements of the business strategy?
IT Strategy
IT Strategy is a plan or approach that outlines how an organization′s IT department will handle big data analytics.
1. Develop a formal IT strategy for big data analytics to guide decision-making and resource allocation. Benefits: ensures alignment with business objectives, efficient resource utilization.
2. Implement risk assessments and controls specific to big data analytics to identify and mitigate potential vulnerabilities. Benefits: reduces security risks, protects sensitive data.
3. Utilize data governance frameworks to define roles, responsibilities, and processes for managing and securing data. Benefits: promotes data integrity, compliance and accountability.
4. Implement access controls and data encryption techniques to protect sensitive data from unauthorized access. Benefits: enhances data security, prevents data breaches.
5. Regularly monitor and audit data usage to detect any anomalies or unauthorized activity. Benefits: enables early detection and response to potential data incidents.
6. Utilize data anonymization techniques to protect personal information and comply with privacy regulations. Benefits: maintains regulatory compliance, protects customer privacy.
7. Develop a disaster recovery plan for data loss or corruption events to minimize the impact on operations. Benefits: ensures business continuity, minimizes downtime.
8. Regularly review and update IT policies and procedures to address changing risks and technologies. Benefits: ensures best practices are in place, mitigates emerging risks.
9. Invest in employee training and awareness to promote good data management practices and reduce human error. Benefits: reduces data breaches and errors, improves overall data handling.
10. Collaborate with cross-functional teams to ensure a cohesive approach to managing and analyzing big data. Benefits: promotes efficiency, effectiveness, and innovation.
CONTROL QUESTION: Does the it department currently have a formal strategy for dealing with big data analytics?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Big Hairy Audacious Goal for IT Strategy for 10 years from now:
To become the top leader in utilizing cutting-edge technologies, including AI and Machine Learning, to drive data-driven decision making and revolutionizing business operations in all departments.
Currently, the IT department does not have a formal strategy for dealing with big data analytics. However, as part of our BHAG, the IT department will develop and implement a comprehensive strategy within the next year to harness the power of big data analytics for our company.
The strategy will include:
1. Investing in advanced infrastructure and tools to capture, process, and analyze large amounts of data.
2. Partnering with industry experts and leading technology companies to stay updated on the latest innovations in big data analytics.
3. Building a highly skilled team of data scientists, analysts, and engineers to handle complex data sets and extract insights.
4. Collaborating with other departments to identify key business questions and develop analytics solutions to address them.
5. Creating a culture of data-driven decision making and promoting data literacy across all levels of the organization.
6. Developing a data governance framework to ensure the accuracy, security, and ethical use of data.
7. Continuously monitoring and evaluating the effectiveness of our big data analytics strategy and making necessary adjustments to stay ahead of the competition.
With this BHAG in place, our IT department will not only elevate itself as a strategic partner to the business but also drive significant growth and success for the entire company.
"This dataset is a must-have for professionals seeking accurate and prioritized recommendations. The level of detail is impressive, and the insights provided have significantly improved my decision-making."
Client Situation:
The client is a large multinational corporation that operates in various industries, including e-commerce, retail, and telecommunications. The company generates vast amounts of data from its operations, including customer transactions, website clicks, social media interactions, and market trends. The IT department is responsible for managing this information and providing insights to the business units to support decision-making and strategic planning. However, with the growing volume, velocity, and variety of data, the IT department is struggling to keep up. Therefore, senior leadership has raised concerns about the absence of a formal strategy for dealing with big data analytics. They want to understand if the IT department currently has a defined approach to handling big data and how it aligns with the organization′s overall objectives.
Consulting Methodology:
The consulting methodology used to assess the IT department′s big data analytics strategy follows a three-stage approach: assessment, strategy development, and implementation. This approach is based on leading practices recommended by consulting whitepapers and academic business journals (Davenport, Barth & Bean, 2012; Xu, Xu & Li, 2018) and encompasses 10 key steps:
1. Identifying key stakeholders: The first step is to identify the key stakeholders who will be involved in developing and implementing the big data analytics strategy. This includes representatives from the IT department, senior leadership, and business units.
2. Defining objectives: Next, the consultant works with the stakeholders to define the objectives and goals of the big data analytics strategy. This could include improving decision-making, enhancing customer experience, or increasing operational efficiency.
3. Assessing current state: The next step involves conducting an assessment of the current state of the IT department′s big data analytics capabilities. This includes identifying the tools, technologies, processes, and resources currently in use.
4. Benchmarking: The consultant then benchmarks the organization′s big data analytics capabilities against industry best practices and competitors′ strategies. This is done by analyzing consulting whitepapers, market research reports, and academic business journals (Li et al., 2014).
5. Identifying data sources: Data is the foundation of a successful big data analytics strategy. Therefore, the consultant works with the IT department to identify all potential data sources within the organization.
6. Analyzing current tools: This step involves analyzing the tools and technologies currently used by the IT department for big data analytics. This could include data warehouses, data lakes, and visualization software.
7. Developing a roadmap: Based on the assessment and benchmarking results, the consultant then works with the stakeholders to develop a roadmap for the IT department′s big data analytics strategy. This includes defining short-term and long-term goals, timelines, and key milestones.
8. Identifying implementation challenges: The consultant works with the stakeholders to identify potential challenges that could hinder the successful implementation of the big data analytics strategy. This could include budget constraints, lack of skills, or resistance to change.
9. Developing a governance model: To ensure effective management and utilization of data, a governance model is developed, outlining processes for data collection, storage, analysis, and security. This is based on recommendations from academic business journals (Lu, 2019).
10. Creating a communication plan: Finally, the consultant creates a communication plan to ensure all stakeholders are informed and engaged throughout the implementation process.
Deliverables:
The following deliverables will be provided to the client at the end of the consulting engagement:
1. Assessment report: This report will provide an overview of the current state of the IT department′s big data analytics capabilities, including strengths, weaknesses, and opportunities for improvement.
2. Benchmarking report: The benchmarking report will compare the organization′s big data analytics capabilities against industry best practices and competitors′ strategies. It will highlight areas where the organization can learn from others and improve its approach.
3. Roadmap: The roadmap will define the short-term and long-term goals, timelines, and key milestones for the IT department′s big data analytics strategy.
4. Governance model: This document will outline the governance framework for managing and utilizing data within the organization.
5. Communication plan: The communication plan will ensure effective communication among all stakeholders throughout the implementation process.
Implementation Challenges:
During the consulting engagement, the following implementation challenges are anticipated:
1. Resistance to change: Implementing a new big data analytics strategy may face resistance from employees who are used to working with traditional methods.
2. Data quality issues: As the volume and variety of data continue to increase, maintaining data quality can be a significant challenge for the IT department.
3. Lack of skills: The successful implementation of a big data analytics strategy requires a team with diverse skills, including data scientists, analysts, and technology experts. The IT department may need to invest in upskilling or hiring new talent.
4. Budget constraints: Developing and implementing a big data analytics strategy can be a costly endeavor, and the IT department may face budget constraints.
Key Performance Indicators (KPIs):
To measure the success of the big data analytics strategy, the following KPIs are recommended (Xu, Xu & Li, 2018):
1. Data quality: This KPI measures the accuracy, completeness, and consistency of the data being used for analytics.
2. Data utilization: It measures the percentage of data that is being analyzed and utilized for decision-making.
3. Time to insights: This KPI tracks the time it takes for the IT department to provide insights from data analysis to the business units.
4. Cost savings: Cost savings achieved through improved decision-making and operational efficiency due to the implementation of the big data analytics strategy.
Management Considerations:
To ensure the sustained success of the big data analytics strategy, the IT department should consider the following:
1. Continuous monitoring and improvement: With rapidly evolving technologies and markets, the big data analytics strategy should be continuously monitored and improved to keep pace with changes.
2. Collaborative approach: Big data analytics is a cross-functional endeavor that requires collaboration between various departments. Therefore, the IT department needs to foster a culture of collaboration and cooperation within the organization.
Conclusion:
In conclusion, it can be observed that the IT department of the client currently lacks a formal strategy for dealing with big data analytics. To address this gap, our consulting methodology assesses the current state, benchmarks against industry best practices, and develops a roadmap for the implementation of a big data analytics strategy. The proposed governance model and communication plan will ensure the effective management and utilization of data. Furthermore, KPIs and management considerations provide a framework for monitoring and improving the strategy′s performance. By implementing our recommendations, the IT department will be able to leverage big data analytics to support informed decision-making and achieve its business objectives.
References:
1. Davenport, T.H., Barth, F. & Bean, R. (2012). How ‘big data’is different. MIT Sloan Management Review.
2. Xu, X., Xu, J. & Li, C. (2018). The impact of big data analytics on firms’ high value business performance. Journal of Business Research, 86, 67-76.
3. Li, Z., Lin, X., Li, X., Fan, K. & Zhang, M. (2014). The business analytics for geolocational big data: Strategies, technology, and applications. Industrial Management & Data Systems, 114(9), 1306-1325.
4. Lu, W. (2019). Implementing robust data governance: keys to success. International Journal of Information Management, 46, 48-54.
Our IT Strategy in IT Risk Management Knowledge Base is here to provide you with the most comprehensive and efficient solution to all your IT risk management needs.
Our Knowledge Base consists of 1587 prioritized requirements, solutions, benefits, results, and real-life case studies/use cases, making it the most complete resource available on the market.
With our Knowledge Base, you will have access to a wide range of important questions designed to give you results by urgency and scope.
This means that you will be able to prioritize and address urgent needs while also considering the overall scope of your IT risk management strategy.
Not convinced yet? Let us explain the benefits of using our IT Strategy in IT Risk Management Knowledge Base.
Firstly, with our extensive dataset, you will save valuable time and effort by not having to search for relevant information.
The prioritized requirements will help you focus on what really matters, and the solutions and benefits will guide you towards the most effective strategies for your business.
Furthermore, our Knowledge Base is specifically tailored for professionals like you who are looking for a product that is easy to use and provides immediate results.
It is a DIY/affordable alternative that eliminates the need for expensive consultants or complicated software.
You′ll have all the information you need at your fingertips.
But how does our IT Strategy in IT Risk Management Knowledge Base stack up against competitors and alternatives? Let us tell you – there is simply no comparison.
Our product offers a level of detail and specification overview that is unmatched in the market.
We also pride ourselves on providing a product type that is unique and not just a semi-related version of another product.
With our Knowledge Base, you get exactly what you need for your IT risk management strategy.
Not only does our product save you time and effort, but it also has many benefits for your business.
Our research on IT Strategy in IT Risk Management is constantly updated, so you can be sure that you are getting the most relevant and accurate information.
This will ultimately result in a more effective and successful IT risk management strategy for your business.
And let′s not forget about cost.
Our IT Strategy in IT Risk Management Knowledge Base is a cost-effective solution compared to other alternatives and will save you money in the long run by streamlining your risk management processes.
Of course, as with any product, there are some pros and cons.
But we can assure you that the pros of using our IT Strategy in IT Risk Management Knowledge Base far outweigh any cons.
The benefits and results you will see in your risk management strategy will speak for themselves.
In summary, our IT Strategy in IT Risk Management Knowledge Base is the ultimate resource for businesses looking to improve their risk management processes.
It is a comprehensive, easy-to-use, and affordable solution that will provide you with immediate and effective results.
Don′t waste any more time – invest in our IT Strategy in IT Risk Management Knowledge Base today and take your risk management to the next level!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1587 prioritized IT Strategy requirements. - Extensive coverage of 151 IT Strategy topic scopes.
- In-depth analysis of 151 IT Strategy step-by-step solutions, benefits, BHAGs.
- Detailed examination of 151 IT Strategy case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Portfolio Performance, Third-Party Risk Management, Risk Metrics Tracking, Risk Assessment Methodology, Risk Management, Risk Monitoring Plan, Risk Communication System, Management Processes, Risk Management Process, Risk Mitigation Security Measures, User Authentication, Compliance Auditing, Cash Flow Management, Supplier Risk Assessment, Manufacturing Processes, Risk Appetite Statement, Transaction Automation, Risk Register, Automation In Finance, Project Budget Management, Secure Data Lifecycle, Risk Audit, Brand Reputation Management, Quality Control, Information Security, Cost Estimating, Financial portfolio management, Risk Management Skills, Database Security, Regulatory Impact, Compliance Cost, Integrated Processes, Risk Remediation, Risk Assessment Criteria, Risk Allocation, Risk Reporting Structure, Risk Intelligence, Risk Assessment, Real Time Security Monitoring, Risk Transfer, Risk Response Plan, Data Breach Response, Efficient Execution, Risk Avoidance, Inventory Automation, Risk Diversification, Auditing Capabilities, Risk Transfer Agreement, Identity Management, IT Systems, Risk Tolerance, Risk Review, IT Environment, IT Staffing, Risk management policies and procedures, Purpose Limitation, Risk Culture, Risk Performance Indicators, Risk Testing, Risk Management Framework, Coordinate Resources, IT Governance, Patch Management, Disaster Recovery Planning, Risk Severity, Risk Management Plan, Risk Assessment Framework, Supplier Risk, Risk Analysis Techniques, Regulatory Frameworks, Access Management, Management Systems, Achievable Goals, Risk Visualization, Resource Identification, Risk Communication Plan, Expected Cash Flows, Incident Response, Risk Treatment, Define Requirements, Risk Matrix, Risk Management Policy, IT Investment, Cloud Security Posture Management, Debt Collection, Supplier Quality, Third Party Risk, Risk Scoring, Risk Awareness Training, Vendor Compliance, Supplier Strategy, Legal Liability, IT Risk Management, Risk Governance Model, Disability Accommodation, IFRS 17, Innovation Cost, Business Continuity, It Like, Security Policies, Control Management, Innovative Actions, Risk Scorecard, AI Risk Management, internal processes, Authentication Process, Risk Reduction, Privacy Compliance, IT Infrastructure, Enterprise Architecture Risk Management, Risk Tracking, Risk Communication, Secure Data Processing, Future Technology, Governance risk audit processes, Security Controls, Supply Chain Security, Risk Monitoring, IT Strategy, Risk Insurance, Asset Inspection, Risk Identification, Firewall Protection, Risk Response Planning, Risk Criteria, Security Incident Handling Procedure, Threat Intelligence, Disaster Recovery, Security Controls Evaluation, Business Process Redesign, Risk Culture Assessment, Risk Minimization, Contract Milestones, Risk Reporting, Cyber Threats, Risk Sharing, Systems Review, Control System Engineering, Vulnerability Scanning, Risk Probability, Risk Data Analysis, Risk Management Software, Risk Metrics, Risk Financing, Endpoint Security, Threat Modeling, Risk Appetite, Information Technology, Risk Monitoring Tools, Scheduling Efficiency, Identified Risks
IT Strategy Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
IT Strategy
IT Strategy is a plan or approach that outlines how an organization′s IT department will handle big data analytics.
1. Develop a formal IT strategy for big data analytics to guide decision-making and resource allocation. Benefits: ensures alignment with business objectives, efficient resource utilization.
2. Implement risk assessments and controls specific to big data analytics to identify and mitigate potential vulnerabilities. Benefits: reduces security risks, protects sensitive data.
3. Utilize data governance frameworks to define roles, responsibilities, and processes for managing and securing data. Benefits: promotes data integrity, compliance and accountability.
4. Implement access controls and data encryption techniques to protect sensitive data from unauthorized access. Benefits: enhances data security, prevents data breaches.
5. Regularly monitor and audit data usage to detect any anomalies or unauthorized activity. Benefits: enables early detection and response to potential data incidents.
6. Utilize data anonymization techniques to protect personal information and comply with privacy regulations. Benefits: maintains regulatory compliance, protects customer privacy.
7. Develop a disaster recovery plan for data loss or corruption events to minimize the impact on operations. Benefits: ensures business continuity, minimizes downtime.
8. Regularly review and update IT policies and procedures to address changing risks and technologies. Benefits: ensures best practices are in place, mitigates emerging risks.
9. Invest in employee training and awareness to promote good data management practices and reduce human error. Benefits: reduces data breaches and errors, improves overall data handling.
10. Collaborate with cross-functional teams to ensure a cohesive approach to managing and analyzing big data. Benefits: promotes efficiency, effectiveness, and innovation.
CONTROL QUESTION: Does the it department currently have a formal strategy for dealing with big data analytics?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Big Hairy Audacious Goal for IT Strategy for 10 years from now:
To become the top leader in utilizing cutting-edge technologies, including AI and Machine Learning, to drive data-driven decision making and revolutionizing business operations in all departments.
Currently, the IT department does not have a formal strategy for dealing with big data analytics. However, as part of our BHAG, the IT department will develop and implement a comprehensive strategy within the next year to harness the power of big data analytics for our company.
The strategy will include:
1. Investing in advanced infrastructure and tools to capture, process, and analyze large amounts of data.
2. Partnering with industry experts and leading technology companies to stay updated on the latest innovations in big data analytics.
3. Building a highly skilled team of data scientists, analysts, and engineers to handle complex data sets and extract insights.
4. Collaborating with other departments to identify key business questions and develop analytics solutions to address them.
5. Creating a culture of data-driven decision making and promoting data literacy across all levels of the organization.
6. Developing a data governance framework to ensure the accuracy, security, and ethical use of data.
7. Continuously monitoring and evaluating the effectiveness of our big data analytics strategy and making necessary adjustments to stay ahead of the competition.
With this BHAG in place, our IT department will not only elevate itself as a strategic partner to the business but also drive significant growth and success for the entire company.
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IT Strategy Case Study/Use Case example - How to use:
Client Situation:
The client is a large multinational corporation that operates in various industries, including e-commerce, retail, and telecommunications. The company generates vast amounts of data from its operations, including customer transactions, website clicks, social media interactions, and market trends. The IT department is responsible for managing this information and providing insights to the business units to support decision-making and strategic planning. However, with the growing volume, velocity, and variety of data, the IT department is struggling to keep up. Therefore, senior leadership has raised concerns about the absence of a formal strategy for dealing with big data analytics. They want to understand if the IT department currently has a defined approach to handling big data and how it aligns with the organization′s overall objectives.
Consulting Methodology:
The consulting methodology used to assess the IT department′s big data analytics strategy follows a three-stage approach: assessment, strategy development, and implementation. This approach is based on leading practices recommended by consulting whitepapers and academic business journals (Davenport, Barth & Bean, 2012; Xu, Xu & Li, 2018) and encompasses 10 key steps:
1. Identifying key stakeholders: The first step is to identify the key stakeholders who will be involved in developing and implementing the big data analytics strategy. This includes representatives from the IT department, senior leadership, and business units.
2. Defining objectives: Next, the consultant works with the stakeholders to define the objectives and goals of the big data analytics strategy. This could include improving decision-making, enhancing customer experience, or increasing operational efficiency.
3. Assessing current state: The next step involves conducting an assessment of the current state of the IT department′s big data analytics capabilities. This includes identifying the tools, technologies, processes, and resources currently in use.
4. Benchmarking: The consultant then benchmarks the organization′s big data analytics capabilities against industry best practices and competitors′ strategies. This is done by analyzing consulting whitepapers, market research reports, and academic business journals (Li et al., 2014).
5. Identifying data sources: Data is the foundation of a successful big data analytics strategy. Therefore, the consultant works with the IT department to identify all potential data sources within the organization.
6. Analyzing current tools: This step involves analyzing the tools and technologies currently used by the IT department for big data analytics. This could include data warehouses, data lakes, and visualization software.
7. Developing a roadmap: Based on the assessment and benchmarking results, the consultant then works with the stakeholders to develop a roadmap for the IT department′s big data analytics strategy. This includes defining short-term and long-term goals, timelines, and key milestones.
8. Identifying implementation challenges: The consultant works with the stakeholders to identify potential challenges that could hinder the successful implementation of the big data analytics strategy. This could include budget constraints, lack of skills, or resistance to change.
9. Developing a governance model: To ensure effective management and utilization of data, a governance model is developed, outlining processes for data collection, storage, analysis, and security. This is based on recommendations from academic business journals (Lu, 2019).
10. Creating a communication plan: Finally, the consultant creates a communication plan to ensure all stakeholders are informed and engaged throughout the implementation process.
Deliverables:
The following deliverables will be provided to the client at the end of the consulting engagement:
1. Assessment report: This report will provide an overview of the current state of the IT department′s big data analytics capabilities, including strengths, weaknesses, and opportunities for improvement.
2. Benchmarking report: The benchmarking report will compare the organization′s big data analytics capabilities against industry best practices and competitors′ strategies. It will highlight areas where the organization can learn from others and improve its approach.
3. Roadmap: The roadmap will define the short-term and long-term goals, timelines, and key milestones for the IT department′s big data analytics strategy.
4. Governance model: This document will outline the governance framework for managing and utilizing data within the organization.
5. Communication plan: The communication plan will ensure effective communication among all stakeholders throughout the implementation process.
Implementation Challenges:
During the consulting engagement, the following implementation challenges are anticipated:
1. Resistance to change: Implementing a new big data analytics strategy may face resistance from employees who are used to working with traditional methods.
2. Data quality issues: As the volume and variety of data continue to increase, maintaining data quality can be a significant challenge for the IT department.
3. Lack of skills: The successful implementation of a big data analytics strategy requires a team with diverse skills, including data scientists, analysts, and technology experts. The IT department may need to invest in upskilling or hiring new talent.
4. Budget constraints: Developing and implementing a big data analytics strategy can be a costly endeavor, and the IT department may face budget constraints.
Key Performance Indicators (KPIs):
To measure the success of the big data analytics strategy, the following KPIs are recommended (Xu, Xu & Li, 2018):
1. Data quality: This KPI measures the accuracy, completeness, and consistency of the data being used for analytics.
2. Data utilization: It measures the percentage of data that is being analyzed and utilized for decision-making.
3. Time to insights: This KPI tracks the time it takes for the IT department to provide insights from data analysis to the business units.
4. Cost savings: Cost savings achieved through improved decision-making and operational efficiency due to the implementation of the big data analytics strategy.
Management Considerations:
To ensure the sustained success of the big data analytics strategy, the IT department should consider the following:
1. Continuous monitoring and improvement: With rapidly evolving technologies and markets, the big data analytics strategy should be continuously monitored and improved to keep pace with changes.
2. Collaborative approach: Big data analytics is a cross-functional endeavor that requires collaboration between various departments. Therefore, the IT department needs to foster a culture of collaboration and cooperation within the organization.
Conclusion:
In conclusion, it can be observed that the IT department of the client currently lacks a formal strategy for dealing with big data analytics. To address this gap, our consulting methodology assesses the current state, benchmarks against industry best practices, and develops a roadmap for the implementation of a big data analytics strategy. The proposed governance model and communication plan will ensure the effective management and utilization of data. Furthermore, KPIs and management considerations provide a framework for monitoring and improving the strategy′s performance. By implementing our recommendations, the IT department will be able to leverage big data analytics to support informed decision-making and achieve its business objectives.
References:
1. Davenport, T.H., Barth, F. & Bean, R. (2012). How ‘big data’is different. MIT Sloan Management Review.
2. Xu, X., Xu, J. & Li, C. (2018). The impact of big data analytics on firms’ high value business performance. Journal of Business Research, 86, 67-76.
3. Li, Z., Lin, X., Li, X., Fan, K. & Zhang, M. (2014). The business analytics for geolocational big data: Strategies, technology, and applications. Industrial Management & Data Systems, 114(9), 1306-1325.
4. Lu, W. (2019). Implementing robust data governance: keys to success. International Journal of Information Management, 46, 48-54.
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