Attention all data management professionals!
Are you tired of sifting through endless resources and still not finding the answers you need? Look no further than our Data Architecture in Data Management Knowledge Base.
With over 1600 prioritized requirements, solutions, benefits, and results, this comprehensive dataset will revolutionize the way you approach data architecture.
One of the most important aspects of data management is understanding urgency and scope.
Our Knowledge Base provides you with the most crucial questions to ask in order to get accurate and timely results.
Plus, our vast selection of case studies and use cases offers practical examples of how our data architecture solutions have been successfully applied in real-world scenarios.
But what sets our Data Architecture in Data Management Knowledge Base apart from competitors and alternative resources? Not only is it specifically tailored for professionals like yourself, but it also offers a affordable DIY alternative to expensive data management tools.
In addition, our dataset offers a detailed overview of product specifications and types, allowing you to easily compare and contrast with semi-related products.
But the benefits don′t stop there.
Our data architecture knowledge base saves you time and effort by providing a one-stop-shop for all your data management needs.
No more jumping from resource to resource, trying to piece together the information you need.
With our extensive research on data architecture and its applications in businesses, you can trust that our dataset is reliable and relevant.
Worried about cost? Don′t be.
Our Data Architecture in Data Management Knowledge Base offers a cost-effective solution for professionals and businesses alike.
And with a detailed list of pros and cons, you′ll have all the information you need to make an informed decision.
So what does our product actually do? It simplifies and streamlines the often complex and overwhelming task of data architecture.
With our comprehensive dataset, you can easily identify and prioritize requirements, find effective solutions, and achieve measurable results - all while saving time and money.
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What are the common mistakes that data management or analytics professionals make when it comes to big data and gaining new insights from data? Do you need to build a data center just to keep up with the sheer volume of data hitting the mainframe?
Data Architecture
One common mistake is not properly understanding the structure and flow of data, leading to difficulty in organizing and interpreting it for insights.
1. Not having a clear data architecture plan can lead to inefficient data management and hinder the ability to uncover valuable insights.
2. Poor data quality can arise from neglecting to establish data governance policies and processes, resulting in unreliable insights.
3. Inadequate data security measures can compromise sensitive data, making it difficult or even impossible to gain insights from certain datasets.
4. Not implementing a scalable infrastructure can limit the volume of data that can be processed and analyzed, hindering the potential for new insights.
5. Focusing solely on existing data sources can lead to missed opportunities for gaining insights from alternative or unexplored datasets.
6. Relying solely on traditional analytics techniques may not be sufficient in capturing all potential insights from big data.
7. Insufficient collaboration and communication between data management and analytics teams can result in a disconnect between data availability and data insights.
8. Underutilizing the capabilities of advanced data analytics tools can prevent organizations from uncovering deeper insights and patterns within their data.
9. Neglecting to continuously review and update data management processes and strategies can lead to outdated and inadequate analysis methods.
10. Failing to consider the ethical implications of using big data can result in backlash and financial losses for organizations, damaging their reputation.
CONTROL QUESTION: What are the common mistakes that data management or analytics professionals make when it comes to big data and gaining new insights from data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
In 2030, I envision the world of data architecture to have evolved into a seamless, integrated and dynamic system that drives innovation and decision-making across all industries. My big hairy audacious goal for Data Architecture in 2030 is to create a truly autonomous and self-learning data ecosystem that can handle the immense volume, velocity, and variety of data generated by businesses, governments, and individuals.
This data ecosystem will be built upon advanced technologies such as artificial intelligence, machine learning, and blockchain to continuously improve and optimize itself while protecting the privacy and security of all data sources. It will break down data silos, seamlessly integrate data from multiple sources, and provide insights and recommendations in real-time.
However, to achieve this ambitious goal, we must address and avoid common mistakes that data management and analytics professionals make when dealing with big data:
1. Neglecting data governance: It is essential to establish clear policies, processes, and guidelines for data management and use. Without proper data governance, data can become unreliable, duplicative, or inaccessible, hindering the ability to gain meaningful insights from it.
2. Focusing only on technology: It is easy to get caught up in the shiny new tools and technologies when dealing with big data. However, the success of a data architecture ultimately lies in its ability to solve business problems and drive value, not just the sophistication of its technology.
3. Not understanding the data: Big data is only valuable if it is accompanied by insights and context. Data professionals must understand the data they are working with and its limitations, or else the insights gained will be flawed or inaccurate.
4. Ignoring privacy and security: As data becomes more readily available, the risk of breaches, hacking, and misuse also increases. Organizations must prioritize the protection and ethical use of data to maintain public trust and comply with regulations.
5. Not aligning with business objectives: A data architecture must be aligned with the overall goals and strategies of an organization to drive value and impact. Failure to do so can lead to inefficiencies and missed opportunities.
In conclusion, by addressing these common mistakes and continuously pushing the boundaries of what is possible in data architecture, we can create a truly transformative and autonomous data ecosystem that will revolutionize the way we understand and utilize data in the years to come.
"I am thoroughly impressed by the quality of the prioritized recommendations in this dataset. It has made a significant impact on the efficiency of my work. Highly recommended for professionals in any field."
Client Situation:
Company X is a large retail conglomerate with a global presence. Over the years, the company has amassed a significant amount of data across various business functions such as sales, marketing, supply chain, and customer service. With the rise of big data and analytics, Company X recognizes the potential of gaining valuable insights from this data to improve its operations and drive business growth. However, the company has been struggling to leverage big data effectively. The senior management team believes that there are several mistakes being made by their data management and analytics professionals that are hindering their ability to gain new insights and derive value from their data. They have engaged a consulting firm to conduct a thorough analysis of their data architecture, processes, and practices to identify these mistakes and provide recommendations for improvement.
Consulting Methodology:
The consulting firm used a holistic approach to assess Company X′s data architecture and practices. They began by conducting a thorough review of the company′s current data infrastructure, including databases, data warehouses, and data lakes. Next, they analyzed the company′s data governance and data quality practices to understand the processes in place for managing and maintaining data integrity. The consulting team also reviewed the company′s data analytics capabilities, including tools and techniques used for data exploration, modeling, and visualization. This analysis was complemented by interviews with key stakeholders across different departments to gather their perspectives on the use of big data within the organization.
Deliverables:
The consulting firm delivered a comprehensive report that highlighted the common mistakes being made by data management and analytics professionals at Company X. The report also included recommendations for improving the company′s data architecture and practices to better leverage big data. Some of the key findings and recommendations are discussed below:
1. Lack of Proper Data Governance:
The review of the company′s data governance practices revealed that there was no clear ownership or accountability for data management. This lack of governance resulted in inconsistencies in data definitions, poor data quality, and a lack of trust in the data. To address this issue, the consulting firm recommended creating a formal data governance framework with clearly defined roles and responsibilities for data ownership, data quality management, and data security.
2. Inadequate Data Integration:
Company X had multiple sources of data, but there was no centralized data integration strategy in place. As a result, data from different systems and departments were not being integrated effectively, leading to data silos and duplication. The consulting firm recommended implementing an enterprise data integration solution to consolidate and manage data from various sources efficiently.
3. Overreliance on Traditional Data Warehouses:
The analysis of the company′s data infrastructure revealed that they were primarily using traditional on-premises data warehouses to store and analyze data. This limited scalability and flexibility, making it challenging to handle the volume, variety, and velocity of big data. The consulting firm advised the company to explore modern cloud-based data warehousing solutions that could handle large volumes of data and provide real-time analytics capabilities.
Implementation Challenges:
The biggest challenge faced during the implementation of the recommendations was the cultural shift required within the organization. Company X had been operating for many years using traditional methods of data management and analytics. Therefore, it was challenging to change mindsets and get buy-in from all stakeholders for the proposed changes. Another significant challenge was the cost involved in implementing some of the recommendations, such as investing in new data integration and analytics tools.
KPIs and Management Considerations:
To measure the success of the implementation, the consulting firm suggested several key performance indicators (KPIs) for the company to track. These included increased data quality, reduced time to insights, improved data accessibility and usability, and cost savings from eliminating redundancies and improving scalability. The senior management team was advised to create a data-oriented culture in the organization by encouraging data-driven decision-making and investing in employee training on modern data management and analytics techniques.
Conclusion:
The consulting firm′s analysis and recommendations helped Company X identify the common mistakes being made by its data management and analytics professionals and provided a roadmap for improving their data architecture and practices. By implementing the proposed changes, the company was able to overcome these challenges and leverage big data effectively to gain new insights and drive business growth.
Citations:
- Gartner. (2019). Top 10 Data and Analytics Technology Trends That Will Change Your Business. Retrieved from: https://www.gartner.com/en/documents/3976817/top-10-data-and-analytics-technology-trends-that-will-chan
- Deloitte. (2019). The four mistakes that can doom analytics projects. Retrieved from: https://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/four-mistakes-doom-analytics-projects.html
- McKinsey & Company. (2016). How Big Data can improve manufacturing. Retrieved from: https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/how-big-data-can-improve-manufacturing
- Accenture. (2015). Big Data Management and Analytics for Business Value. Retrieved from: https://www.accenture.com/_acnmedia/PDF-32/Accenture-Big-Data-Management-Analytics-Business-Value.pdf
Are you tired of sifting through endless resources and still not finding the answers you need? Look no further than our Data Architecture in Data Management Knowledge Base.
With over 1600 prioritized requirements, solutions, benefits, and results, this comprehensive dataset will revolutionize the way you approach data architecture.
One of the most important aspects of data management is understanding urgency and scope.
Our Knowledge Base provides you with the most crucial questions to ask in order to get accurate and timely results.
Plus, our vast selection of case studies and use cases offers practical examples of how our data architecture solutions have been successfully applied in real-world scenarios.
But what sets our Data Architecture in Data Management Knowledge Base apart from competitors and alternative resources? Not only is it specifically tailored for professionals like yourself, but it also offers a affordable DIY alternative to expensive data management tools.
In addition, our dataset offers a detailed overview of product specifications and types, allowing you to easily compare and contrast with semi-related products.
But the benefits don′t stop there.
Our data architecture knowledge base saves you time and effort by providing a one-stop-shop for all your data management needs.
No more jumping from resource to resource, trying to piece together the information you need.
With our extensive research on data architecture and its applications in businesses, you can trust that our dataset is reliable and relevant.
Worried about cost? Don′t be.
Our Data Architecture in Data Management Knowledge Base offers a cost-effective solution for professionals and businesses alike.
And with a detailed list of pros and cons, you′ll have all the information you need to make an informed decision.
So what does our product actually do? It simplifies and streamlines the often complex and overwhelming task of data architecture.
With our comprehensive dataset, you can easily identify and prioritize requirements, find effective solutions, and achieve measurable results - all while saving time and money.
Don′t miss out on this game-changing resource for data management professionals.
Invest in our Data Architecture in Data Management Knowledge Base and take your data architecture to the next level.
Try it risk-free today!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1625 prioritized Data Architecture requirements. - Extensive coverage of 313 Data Architecture topic scopes.
- In-depth analysis of 313 Data Architecture step-by-step solutions, benefits, BHAGs.
- Detailed examination of 313 Data Architecture 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: Data Control Language, Smart Sensors, Physical Assets, Incident Volume, Inconsistent Data, Transition Management, Data Lifecycle, Actionable Insights, Wireless Solutions, Scope Definition, End Of Life Management, Data Privacy Audit, Search Engine Ranking, Data Ownership, GIS Data Analysis, Data Classification Policy, Test AI, Data Management Consulting, Data Archiving, Quality Objectives, Data Classification Policies, Systematic Methodology, Print Management, Data Governance Roadmap, Data Recovery Solutions, Golden Record, Data Privacy Policies, Data Management System Implementation, Document Processing Document Management, Master Data Management, Repository Management, Tag Management Platform, Financial Verification, Change Management, Data Retention, Data Backup Solutions, Data Innovation, MDM Data Quality, Data Migration Tools, Data Strategy, Data Standards, Device Alerting, Payroll Management, Data Management Platform, Regulatory Technology, Social Impact, Data Integrations, Response Coordinator, Chief Investment Officer, Data Ethics, Metadata Management, Reporting Procedures, Data Analytics Tools, Meta Data Management, Customer Service Automation, Big Data, Agile User Stories, Edge Analytics, Change management in digital transformation, Capacity Management Strategies, Custom Properties, Scheduling Options, Server Maintenance, Data Governance Challenges, Enterprise Architecture Risk Management, Continuous Improvement Strategy, Discount Management, Business Management, Data Governance Training, Data Management Performance, Change And Release Management, Metadata Repositories, Data Transparency, Data Modelling, Smart City Privacy, In-Memory Database, Data Protection, Data Privacy, Data Management Policies, Audience Targeting, Privacy Laws, Archival processes, Project management professional organizations, Why She, Operational Flexibility, Data Governance, AI Risk Management, Risk Practices, Data Breach Incident Incident Response Team, Continuous Improvement, Different Channels, Flexible Licensing, Data Sharing, Event Streaming, Data Management Framework Assessment, Trend Awareness, IT Environment, Knowledge Representation, Data Breaches, Data Access, Thin Provisioning, Hyperconverged Infrastructure, ERP System Management, Data Disaster Recovery Plan, Innovative Thinking, Data Protection Standards, Software Investment, Change Timeline, Data Disposition, Data Management Tools, Decision Support, Rapid Adaptation, Data Disaster Recovery, Data Protection Solutions, Project Cost Management, Metadata Maintenance, Data Scanner, Centralized Data Management, Privacy Compliance, User Access Management, Data Management Implementation Plan, Backup Management, Big Data Ethics, Non-Financial Data, Data Architecture, Secure Data Storage, Data Management Framework Development, Data Quality Monitoring, Data Management Governance Model, Custom Plugins, Data Accuracy, Data Management Governance Framework, Data Lineage Analysis, Test Automation Frameworks, Data Subject Restriction, Data Management Certification, Risk Assessment, Performance Test Data Management, MDM Data Integration, Data Management Optimization, Rule Granularity, Workforce Continuity, Supply Chain, Software maintenance, Data Governance Model, Cloud Center of Excellence, Data Governance Guidelines, Data Governance Alignment, Data Storage, Customer Experience Metrics, Data Management Strategy, Data Configuration Management, Future AI, Resource Conservation, Cluster Management, Data Warehousing, ERP Provide Data, Pain Management, Data Governance Maturity Model, Data Management Consultation, Data Management Plan, Content Prototyping, Build Profiles, Data Breach Incident Incident Risk Management, Proprietary Data, Big Data Integration, Data Management Process, Business Process Redesign, Change Management Workflow, Secure Communication Protocols, Project Management Software, Data Security, DER Aggregation, Authentication Process, Data Management Standards, Technology Strategies, Data consent forms, Supplier Data Management, Agile Processes, Process Deficiencies, Agile Approaches, Efficient Processes, Dynamic Content, Service Disruption, Data Management Database, Data ethics culture, ERP Project Management, Data Governance Audit, Data Protection Laws, Data Relationship Management, Process Inefficiencies, Secure Data Processing, Data Management Principles, Data Audit Policy, Network optimization, Data Management Systems, Enterprise Architecture Data Governance, Compliance Management, Functional Testing, Customer Contracts, Infrastructure Cost Management, Analytics And Reporting Tools, Risk Systems, Customer Assets, Data generation, Benchmark Comparison, Data Management Roles, Data Privacy Compliance, Data Governance Team, Change Tracking, Previous Release, Data Management Outsourcing, Data Inventory, Remote File Access, Data Management Framework, Data Governance Maturity, Continually Improving, Year Period, Lead Times, Control Management, Asset Management Strategy, File Naming Conventions, Data Center Revenue, Data Lifecycle Management, Customer Demographics, Data Subject Portability, MDM Security, Database Restore, Management Systems, Real Time Alerts, Data Regulation, AI Policy, Data Compliance Software, Data Management Techniques, ESG, Digital Change Management, Supplier Quality, Hybrid Cloud Disaster Recovery, Data Privacy Laws, Master Data, Supplier Governance, Smart Data Management, Data Warehouse Design, Infrastructure Insights, Data Management Training, Procurement Process, Performance Indices, Data Integration, Data Protection Policies, Quarterly Targets, Data Governance Policy, Data Analysis, Data Encryption, Data Security Regulations, Data management, Trend Analysis, Resource Management, Distribution Strategies, Data Privacy Assessments, MDM Reference Data, KPIs Development, Legal Research, Information Technology, Data Management Architecture, Processes Regulatory, Asset Approach, Data Governance Procedures, Meta Tags, Data Security Best Practices, AI Development, Leadership Strategies, Utilization Management, Data Federation, Data Warehouse Optimization, Data Backup Management, Data Warehouse, Data Protection Training, Security Enhancement, Data Governance Data Management, Research Activities, Code Set, Data Retrieval, Strategic Roadmap, Data Security Compliance, Data Processing Agreements, IT Investments Analysis, Lean Management, Six Sigma, Continuous improvement Introduction, Sustainable Land Use, MDM Processes, Customer Retention, Data Governance Framework, Master Plan, Efficient Resource Allocation, Data Management Assessment, Metadata Values, Data Stewardship Tools, Data Compliance, Data Management Governance, First Party Data, Integration with Legacy Systems, Positive Reinforcement, Data Management Risks, Grouping Data, Regulatory Compliance, Deployed Environment Management, Data Storage Solutions, Data Loss Prevention, Backup Media Management, Machine Learning Integration, Local Repository, Data Management Implementation, Data Management Metrics, Data Management Software
Data Architecture Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Data Architecture
One common mistake is not properly understanding the structure and flow of data, leading to difficulty in organizing and interpreting it for insights.
1. Not having a clear data architecture plan can lead to inefficient data management and hinder the ability to uncover valuable insights.
2. Poor data quality can arise from neglecting to establish data governance policies and processes, resulting in unreliable insights.
3. Inadequate data security measures can compromise sensitive data, making it difficult or even impossible to gain insights from certain datasets.
4. Not implementing a scalable infrastructure can limit the volume of data that can be processed and analyzed, hindering the potential for new insights.
5. Focusing solely on existing data sources can lead to missed opportunities for gaining insights from alternative or unexplored datasets.
6. Relying solely on traditional analytics techniques may not be sufficient in capturing all potential insights from big data.
7. Insufficient collaboration and communication between data management and analytics teams can result in a disconnect between data availability and data insights.
8. Underutilizing the capabilities of advanced data analytics tools can prevent organizations from uncovering deeper insights and patterns within their data.
9. Neglecting to continuously review and update data management processes and strategies can lead to outdated and inadequate analysis methods.
10. Failing to consider the ethical implications of using big data can result in backlash and financial losses for organizations, damaging their reputation.
CONTROL QUESTION: What are the common mistakes that data management or analytics professionals make when it comes to big data and gaining new insights from data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
In 2030, I envision the world of data architecture to have evolved into a seamless, integrated and dynamic system that drives innovation and decision-making across all industries. My big hairy audacious goal for Data Architecture in 2030 is to create a truly autonomous and self-learning data ecosystem that can handle the immense volume, velocity, and variety of data generated by businesses, governments, and individuals.
This data ecosystem will be built upon advanced technologies such as artificial intelligence, machine learning, and blockchain to continuously improve and optimize itself while protecting the privacy and security of all data sources. It will break down data silos, seamlessly integrate data from multiple sources, and provide insights and recommendations in real-time.
However, to achieve this ambitious goal, we must address and avoid common mistakes that data management and analytics professionals make when dealing with big data:
1. Neglecting data governance: It is essential to establish clear policies, processes, and guidelines for data management and use. Without proper data governance, data can become unreliable, duplicative, or inaccessible, hindering the ability to gain meaningful insights from it.
2. Focusing only on technology: It is easy to get caught up in the shiny new tools and technologies when dealing with big data. However, the success of a data architecture ultimately lies in its ability to solve business problems and drive value, not just the sophistication of its technology.
3. Not understanding the data: Big data is only valuable if it is accompanied by insights and context. Data professionals must understand the data they are working with and its limitations, or else the insights gained will be flawed or inaccurate.
4. Ignoring privacy and security: As data becomes more readily available, the risk of breaches, hacking, and misuse also increases. Organizations must prioritize the protection and ethical use of data to maintain public trust and comply with regulations.
5. Not aligning with business objectives: A data architecture must be aligned with the overall goals and strategies of an organization to drive value and impact. Failure to do so can lead to inefficiencies and missed opportunities.
In conclusion, by addressing these common mistakes and continuously pushing the boundaries of what is possible in data architecture, we can create a truly transformative and autonomous data ecosystem that will revolutionize the way we understand and utilize data in the years to come.
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Data Architecture Case Study/Use Case example - How to use:
Client Situation:
Company X is a large retail conglomerate with a global presence. Over the years, the company has amassed a significant amount of data across various business functions such as sales, marketing, supply chain, and customer service. With the rise of big data and analytics, Company X recognizes the potential of gaining valuable insights from this data to improve its operations and drive business growth. However, the company has been struggling to leverage big data effectively. The senior management team believes that there are several mistakes being made by their data management and analytics professionals that are hindering their ability to gain new insights and derive value from their data. They have engaged a consulting firm to conduct a thorough analysis of their data architecture, processes, and practices to identify these mistakes and provide recommendations for improvement.
Consulting Methodology:
The consulting firm used a holistic approach to assess Company X′s data architecture and practices. They began by conducting a thorough review of the company′s current data infrastructure, including databases, data warehouses, and data lakes. Next, they analyzed the company′s data governance and data quality practices to understand the processes in place for managing and maintaining data integrity. The consulting team also reviewed the company′s data analytics capabilities, including tools and techniques used for data exploration, modeling, and visualization. This analysis was complemented by interviews with key stakeholders across different departments to gather their perspectives on the use of big data within the organization.
Deliverables:
The consulting firm delivered a comprehensive report that highlighted the common mistakes being made by data management and analytics professionals at Company X. The report also included recommendations for improving the company′s data architecture and practices to better leverage big data. Some of the key findings and recommendations are discussed below:
1. Lack of Proper Data Governance:
The review of the company′s data governance practices revealed that there was no clear ownership or accountability for data management. This lack of governance resulted in inconsistencies in data definitions, poor data quality, and a lack of trust in the data. To address this issue, the consulting firm recommended creating a formal data governance framework with clearly defined roles and responsibilities for data ownership, data quality management, and data security.
2. Inadequate Data Integration:
Company X had multiple sources of data, but there was no centralized data integration strategy in place. As a result, data from different systems and departments were not being integrated effectively, leading to data silos and duplication. The consulting firm recommended implementing an enterprise data integration solution to consolidate and manage data from various sources efficiently.
3. Overreliance on Traditional Data Warehouses:
The analysis of the company′s data infrastructure revealed that they were primarily using traditional on-premises data warehouses to store and analyze data. This limited scalability and flexibility, making it challenging to handle the volume, variety, and velocity of big data. The consulting firm advised the company to explore modern cloud-based data warehousing solutions that could handle large volumes of data and provide real-time analytics capabilities.
Implementation Challenges:
The biggest challenge faced during the implementation of the recommendations was the cultural shift required within the organization. Company X had been operating for many years using traditional methods of data management and analytics. Therefore, it was challenging to change mindsets and get buy-in from all stakeholders for the proposed changes. Another significant challenge was the cost involved in implementing some of the recommendations, such as investing in new data integration and analytics tools.
KPIs and Management Considerations:
To measure the success of the implementation, the consulting firm suggested several key performance indicators (KPIs) for the company to track. These included increased data quality, reduced time to insights, improved data accessibility and usability, and cost savings from eliminating redundancies and improving scalability. The senior management team was advised to create a data-oriented culture in the organization by encouraging data-driven decision-making and investing in employee training on modern data management and analytics techniques.
Conclusion:
The consulting firm′s analysis and recommendations helped Company X identify the common mistakes being made by its data management and analytics professionals and provided a roadmap for improving their data architecture and practices. By implementing the proposed changes, the company was able to overcome these challenges and leverage big data effectively to gain new insights and drive business growth.
Citations:
- Gartner. (2019). Top 10 Data and Analytics Technology Trends That Will Change Your Business. Retrieved from: https://www.gartner.com/en/documents/3976817/top-10-data-and-analytics-technology-trends-that-will-chan
- Deloitte. (2019). The four mistakes that can doom analytics projects. Retrieved from: https://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/four-mistakes-doom-analytics-projects.html
- McKinsey & Company. (2016). How Big Data can improve manufacturing. Retrieved from: https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/how-big-data-can-improve-manufacturing
- Accenture. (2015). Big Data Management and Analytics for Business Value. Retrieved from: https://www.accenture.com/_acnmedia/PDF-32/Accenture-Big-Data-Management-Analytics-Business-Value.pdf
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