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Are you tired of searching through endless resources and struggling to find the right questions to ask in order to improve your cognitive computing and IT operations? Look no further, because we have the perfect solution for you - our Cognitive Computing and IT Operations Knowledge Base.
Forget about wasting time and effort on sifting through irrelevant information.
Our Knowledge Base provides you with 1601 handpicked and prioritized requirements, solutions, benefits, results, and real-world case studies for cognitive computing and IT operations.
These carefully curated datasets are guaranteed to save you time and boost your efficiency like never before.
But what sets our Knowledge Base apart from other alternatives on the market? We understand the urgency and scope of your needs, which is why our dataset is specifically tailored to help you get results quickly and effectively.
No more trial and error, our Knowledge Base provides you with the most important questions to ask in order to stay ahead of the game.
Furthermore, our dataset is designed for professionals just like you, making it easy to use and incorporate into your daily operations.
And for those looking for a more affordable option, our DIY Knowledge Base is the perfect alternative to expensive consulting services.
But don′t just take our word for it, the benefits of our product speak for themselves.
With our comprehensive research on cognitive computing and IT operations, you can trust that our Knowledge Base is based on the latest trends and best practices in the industry.
This means that you can expect to see improved performance, cost savings, and increased customer satisfaction.
Our Cognitive Computing and IT Operations Knowledge Base is not only ideal for individual professionals, but also for businesses looking to stay ahead of their competition.
And with a detailed product overview and specifications, you can easily see how our product compares to semi-related options in the market.
We understand that as a business, cost is an important factor in decision-making.
That′s why our Knowledge Base offers an affordable solution for your cognitive computing and IT operations needs.
And with our product, there are no hidden costs or surprises - just straightforward and effective results.
In a world where technology is advancing at an unprecedented pace, it′s essential for businesses to stay on top of the game.
And that′s exactly what our Cognitive Computing and IT Operations Knowledge Base helps you do.
So don′t wait any longer, try it out for yourself and see the pros and cons of our product compared to others in the market.
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Have you considered partnering with other organizations to apply machine learning and cognitive computing algorithms to your data?
Cognitive Computing
Cognitive computing involves applying machine learning and advanced algorithms to large amounts of data in order to gain insights and make decisions.
1. Improved efficiency and accuracy in data analysis for faster decision making.
2. Better prediction capabilities leading to proactive problem-solving.
3. Automation of routine tasks freeing up time for higher value activities.
4. Integration with existing systems for a seamless and holistic approach.
5. Cost savings through reduction of manual labor and human error.
6. Insights and recommendations for optimization of processes and resources.
7. Enhanced data security through intelligent threat detection.
8. Real-time monitoring and alerts for rapid issue resolution.
9. Customizable dashboards for personalized insights and visualization.
10. Potential for continuous improvement and innovation through constant learning and adaptation.
CONTROL QUESTION: Have you considered partnering with other organizations to apply machine learning and cognitive computing algorithms to the data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
In 10 years, our goal for Cognitive Computing is to have formed strategic partnerships with major organizations in various industries, such as healthcare, finance, transportation, and retail. Together, we will leverage our advanced cognitive computing technologies to analyze and make sense of massive amounts of data collected by these organizations.
Our partnerships will revolve around developing highly sophisticated and scalable machine learning and cognitive computing algorithms that can seamlessly process and analyze data from different sources. Our ultimate aim is to use these algorithms to unlock insights and make predictions that can improve decision-making, drive innovation, and solve complex problems for our partner organizations.
With our combined resources and expertise, we envision a future where cognitive computing is widely used across industries, leading to greater efficiency, cost savings, and overall improvement in operations. We also hope to contribute to the advancement of cognitive computing technology, pushing its boundaries and capabilities to new heights.
Ultimately, our goal is to make a significant impact on society by harnessing the power of cognitive computing and machine learning, improving the quality of life for individuals and driving progress for businesses. With our bold and audacious goal in mind, we are committed to continuously pushing the boundaries of what is possible with cognitive computing and shaping a brighter future for all.
"I can`t believe I didn`t discover this dataset sooner. The prioritized recommendations are a game-changer for project planning. The level of detail and accuracy is unmatched. Highly recommended!"
Introduction:
Cognitive computing has emerged as a powerful technology in recent years, with the ability to analyze vast amounts of data and provide valuable insights. However, it is often a challenging task for organizations to effectively leverage cognitive computing algorithms due to various reasons such as lack of expertise, limited resources, and complex data sets. Hence, partnering with other organizations that have the necessary expertise and resources can prove to be advantageous in applying machine learning and cognitive computing algorithms to data. In this case study, we will explore how a leading retail company partnered with a consulting firm to leverage cognitive computing technology and achieve significant business benefits.
Client Situation:
The client, a large retail company, was facing challenges in analyzing their vast amounts of customer data to identify patterns, trends, and insights that could help them make informed decisions. They were struggling to keep up with the increasing volume, velocity, and variety of data generated by their customers across various channels, including online and offline channels. The data was scattered across multiple systems, making it difficult to integrate and analyze. The existing analytical tools used by the company were not equipped to handle such large and complex data sets, resulting in slow and inefficient decision-making processes.
Consulting Methodology:
To address the challenges faced by the client, they partnered with XYZ consulting firm, a leader in applying cognitive computing technologies to various industries. The consulting firm follows a structured approach that consists of the following steps:
1. Data Assessment: The first step in the consulting process is to conduct a thorough assessment of the client′s data landscape. This involves identifying the data sources, understanding the data types and formats, and evaluating the quality and completeness of the data.
2. Business Understanding: After the data assessment, the consulting team works closely with the client to understand their business requirements, objectives, and challenges. This helps in identifying the key business questions that need to be answered through cognitive computing.
3. Algorithm Selection: Based on the business questions, the consulting team identifies the most suitable cognitive computing algorithms that can be applied to the client′s data. These algorithms are selected based on their ability to handle large and complex data sets, as well as their potential to provide valuable insights.
4. Data Preparation: This involves cleaning, organizing, and integrating the data to make it suitable for analysis. The consulting team uses advanced data preparation techniques, such as data wrangling and data cleansing, to ensure data quality and accuracy.
5. Model Building and Validation: Using the selected algorithms, the consulting team builds and tests various models on the prepared data to identify the most accurate and reliable one. The model is then validated using historical data to ensure its effectiveness.
6. Implementation: Once the model is validated, it is implemented in the client′s systems. This involves integrating the model with the existing analytical tools used by the company to enable real-time analysis of data.
Deliverables:
The consulting firm provided the following deliverables to the client:
1. A comprehensive report on the data landscape, including data sources, types, and quality.
2. Business requirements document outlining the key questions that the cognitive computing model aimed to answer.
3. A detailed implementation plan, including timelines, resources, and dependencies.
4. A validated cognitive computing model that provided insights into customer behavior, preferences, and purchasing patterns.
5. Integration of the model with the client′s analytical tools to enable real-time analysis and reporting.
Implementation Challenges:
The implementation of cognitive computing technology at the client′s organization was not without challenges. The main challenges faced by the consulting team included:
1. Data Quality: The client′s data was scattered across multiple systems and was of varying quality, making it a tedious task to clean and prepare it for analysis.
2. Lack of Expertise: The client′s team lacked the necessary expertise in cognitive computing technology, resulting in a learning curve for them.
3. Integration: Integrating the new cognitive computing model with the existing analytical tools was a complex task that required significant effort and resources.
Key Performance Indicators (KPIs):
The success of the cognitive computing project was measured through the following KPIs:
1. Real-time Reporting: The integration of the model with the client′s systems enabled real-time analysis and reporting of data. A successful implementation would result in improved reporting capabilities.
2. Accuracy: The accuracy of the model in predicting customer behavior, preferences, and purchasing patterns was another key KPI. An increase in accuracy would indicate the effectiveness of the model.
3. Time Savings: The implementation of cognitive computing technology was expected to reduce the time taken to analyze data and provide insights, resulting in significant time savings for the client.
Management Considerations:
The following management considerations were identified and addressed during the implementation phase:
1. Change Management: The adoption of cognitive computing technology required a change in the mindset and culture of the organization. The consulting team worked closely with the client′s leaders to ensure a smooth transition and acceptance of the new technology.
2. Training: To facilitate the adoption of cognitive computing technology, the consulting team provided training to the client′s team to help them understand the technology and its potential.
3. Data Governance: With the use of advanced data preparation techniques and algorithms, data governance became critical to ensure data quality and accuracy. The consulting team helped the client define a data governance framework and put in place appropriate policies and procedures.
Conclusion:
The partnership between the client and the consulting firm proved to be highly beneficial, and the implementation of cognitive computing technology resulted in significant business benefits. The client was able to gain valuable insights into their customers and make informed decisions, resulting in increased sales and customer satisfaction. The success of this project highlights the importance of partnerships and collaborations in leveraging advanced technologies such as cognitive computing. As stated by Deloitte Consulting, Partnerships in cognitive computing enable companies without the required resources or expertise to explore opportunities at a lower cost and with less risk, while offering an opportunity to gain crucial insights into the applicability of cognitive computing for their business. (Deloitte, 2017)
References:
1. Deloitte Consulting. (2017). Cognitive Computing in Retail: How partnering can help retailers.While it is clear that celebritiesThis is not the case in relationships the satisfaction of customers must be regularly reviewed, keeping efficiency in mind. This can be achieved with the help of cognitive computing technology.Cognitive computing in retail(Cognitive Computing in Retail: How partnering can help retailers).Retrieved from https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-da-cognitive-computing-technology-retail.pdf
2. Schmalz, A., & Tan, H. (2015). Cognitive Computing: What Every Professional Services Firm Should Know. Managing Partner Magazine. Retrieved from https://www.professionaladviser.com/feature/2410075/cognitive-computing-what-every-professional-services-firm-should-know
3. McKinsey Global Institute. (2015). The Promise and Challenge of the Age of Analytics. McKinsey & Company. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-promise-and-challenge-of-the-age-of-analytics
Are you tired of searching through endless resources and struggling to find the right questions to ask in order to improve your cognitive computing and IT operations? Look no further, because we have the perfect solution for you - our Cognitive Computing and IT Operations Knowledge Base.
Forget about wasting time and effort on sifting through irrelevant information.
Our Knowledge Base provides you with 1601 handpicked and prioritized requirements, solutions, benefits, results, and real-world case studies for cognitive computing and IT operations.
These carefully curated datasets are guaranteed to save you time and boost your efficiency like never before.
But what sets our Knowledge Base apart from other alternatives on the market? We understand the urgency and scope of your needs, which is why our dataset is specifically tailored to help you get results quickly and effectively.
No more trial and error, our Knowledge Base provides you with the most important questions to ask in order to stay ahead of the game.
Furthermore, our dataset is designed for professionals just like you, making it easy to use and incorporate into your daily operations.
And for those looking for a more affordable option, our DIY Knowledge Base is the perfect alternative to expensive consulting services.
But don′t just take our word for it, the benefits of our product speak for themselves.
With our comprehensive research on cognitive computing and IT operations, you can trust that our Knowledge Base is based on the latest trends and best practices in the industry.
This means that you can expect to see improved performance, cost savings, and increased customer satisfaction.
Our Cognitive Computing and IT Operations Knowledge Base is not only ideal for individual professionals, but also for businesses looking to stay ahead of their competition.
And with a detailed product overview and specifications, you can easily see how our product compares to semi-related options in the market.
We understand that as a business, cost is an important factor in decision-making.
That′s why our Knowledge Base offers an affordable solution for your cognitive computing and IT operations needs.
And with our product, there are no hidden costs or surprises - just straightforward and effective results.
In a world where technology is advancing at an unprecedented pace, it′s essential for businesses to stay on top of the game.
And that′s exactly what our Cognitive Computing and IT Operations Knowledge Base helps you do.
So don′t wait any longer, try it out for yourself and see the pros and cons of our product compared to others in the market.
In conclusion, our Knowledge Base is the ultimate resource for all your cognitive computing and IT operations needs.
With its handpicked datasets, targeted focus on urgency and scope, benefits for professionals and businesses, and affordability, it is the perfect solution for anyone looking to improve their IT operations.
Say goodbye to endless searching and hello to efficient and effective results with our Cognitive Computing and IT Operations Knowledge Base.
Don′t miss out, get yours today!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1601 prioritized Cognitive Computing requirements. - Extensive coverage of 220 Cognitive Computing topic scopes.
- In-depth analysis of 220 Cognitive Computing step-by-step solutions, benefits, BHAGs.
- Detailed examination of 220 Cognitive Computing 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: Autonomous Systems, Continuous Measurement, Web Design, IT Operations Management, Network Segmentation, Anti Virus Protection, Data Governance Framework, IT Strategy, Barcode Scanning, End User Training, Quality Function Deployment, Data Sharing, Software Updates, Backup Automation, Flexible Work Environment, Key Risk Indicator, Control Charts, Firewall Configuration, COSO, Data Encryption, Asset Tracking, Horizontal Management, Employee Ability, Scalable Processes, Capacity Planning, Design Complexity, Outsourcing Arrangements, Web Hosting, Allocation Methodology, Virtual Machine Management, Technical Documentation, Expanding Reach, Backup Verification, Website Security, Social Media Management, Managing Workloads, Policies Automation, Service Customization, Server Maintenance, Remote Operations, Innovation Culture, Technology Strategies, Disaster Planning, Performance Operations, Productivity Measurement, Password Management, Network Performance, Robust Communication, Virtual Security Solutions, Bandwidth Management, Artificial Intelligence Integration, System Backups, Corporate Security, Lean Management, Six Sigma, Continuous improvement Introduction, Wireless Networking, Risk Controls Effectiveness, Third Party Service Providers, Data Continuity, Mobile Applications, Social Impact Networking, It Needs, Application Development, Personalized Interactions, Data Archiving, Information Technology, Infrastructure Optimization, Cloud Infrastructure Management, Regulatory Impact, Website Management, User Activity, Functions Creation, Cloud Center of Excellence, Network Monitoring, Disaster Recovery, Chief Technology Officer, Datacenter Operations, SAFe Overview, Background Check Procedures, Relevant Performance Indicators, ISO 22313, Facilities Maintenance, IT Systems, Capacity Management, Sustainability Impact, Intrusion Detection, IT Policies, Software Architect, Motivational Factors, Data Storage, Knowledge Management, Outsourced Solutions, Access Control, Network Load Balancing, Network Outages, Logical Access Controls, Content Management, Coordinate Resources, AI Systems, Network Security, Security Controls Testing, Service Improvement Strategies, Monitoring Tools, Database Administration, Service Level Agreements, Security incident management software, Database Replication, Managing Time Zones, Remote Access, Can Afford, Efficient Operations, Maintenance Dashboard, Operational Efficiency, Daily Effort, Warranty Management, Data Recovery, Aligned Expectations, System Integration, Cloud Security, Cognitive Computing, Email Management, Project Progress, Performance Tuning, Virtual Operations Support, Web Analytics, Print Management, IT Budgeting, Contract Adherence, AI Technology, Operations Analysis, IT Compliance, Resource Optimization, Performance Based Incentives, IT Operations, Financial Reporting, License Management, Entity Level Controls, Mobile Device Management, Incident Response, System Testing, Service Delivery, Productivity Measurements, Operating System Patching, Contract Management, Urban Planning, Software Licenses, IT Staffing, Capacity Forecasting, Data Migration, Artificial Intelligence, Virtual Desktops, Enter Situations, Data Breaches, Email Encryption, Help Desk Support, Data Quality Management, Patch Support, Orchestration Tools, User Authentication, Production Output, Trained Models, Security Measures, Professional Services Automation, Business Operations, IT Automation, ITSM, Efficiency Tracking, Vendor Management, Online Collaboration, Support Case Management, Organizational Development, Supporting Others, ITIL Framework, Regulatory Compliance, Employee Roles, Software Architecture, File Sharing, Redesign Management, Flexible Operations, Patch Management, Modern Strategy, Software Deployment, Scheduling Efficiency, Inventory Turnover, Infrastructure Management, User Provisioning, Job Descriptions, Backup Solutions, Risk Assessment, Hardware Procurement, IT Environment, Business Operations Recovery, Software Audits, Compliance Cost, Average Transaction, Professional Image, Change Management, Accountability Plans, Resource Utilization, Server Clustering, Application Packaging, Cloud Computing, Supply Chain Resilience, Inventory Management, Data Leakage Prevention, Video Conferencing, Core Platform, IT Service Capacity, Models Called, Systems Review, System Upgrades, Timely Execution, Storage Virtualization, Cost Reductions, Management Systems, Development Team, Distribution Centers, Automated Decision Management, IT Governance, Incident Management, Web Content Filtering
Cognitive Computing Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Cognitive Computing
Cognitive computing involves applying machine learning and advanced algorithms to large amounts of data in order to gain insights and make decisions.
1. Improved efficiency and accuracy in data analysis for faster decision making.
2. Better prediction capabilities leading to proactive problem-solving.
3. Automation of routine tasks freeing up time for higher value activities.
4. Integration with existing systems for a seamless and holistic approach.
5. Cost savings through reduction of manual labor and human error.
6. Insights and recommendations for optimization of processes and resources.
7. Enhanced data security through intelligent threat detection.
8. Real-time monitoring and alerts for rapid issue resolution.
9. Customizable dashboards for personalized insights and visualization.
10. Potential for continuous improvement and innovation through constant learning and adaptation.
CONTROL QUESTION: Have you considered partnering with other organizations to apply machine learning and cognitive computing algorithms to the data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
In 10 years, our goal for Cognitive Computing is to have formed strategic partnerships with major organizations in various industries, such as healthcare, finance, transportation, and retail. Together, we will leverage our advanced cognitive computing technologies to analyze and make sense of massive amounts of data collected by these organizations.
Our partnerships will revolve around developing highly sophisticated and scalable machine learning and cognitive computing algorithms that can seamlessly process and analyze data from different sources. Our ultimate aim is to use these algorithms to unlock insights and make predictions that can improve decision-making, drive innovation, and solve complex problems for our partner organizations.
With our combined resources and expertise, we envision a future where cognitive computing is widely used across industries, leading to greater efficiency, cost savings, and overall improvement in operations. We also hope to contribute to the advancement of cognitive computing technology, pushing its boundaries and capabilities to new heights.
Ultimately, our goal is to make a significant impact on society by harnessing the power of cognitive computing and machine learning, improving the quality of life for individuals and driving progress for businesses. With our bold and audacious goal in mind, we are committed to continuously pushing the boundaries of what is possible with cognitive computing and shaping a brighter future for all.
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Cognitive Computing Case Study/Use Case example - How to use:
Introduction:
Cognitive computing has emerged as a powerful technology in recent years, with the ability to analyze vast amounts of data and provide valuable insights. However, it is often a challenging task for organizations to effectively leverage cognitive computing algorithms due to various reasons such as lack of expertise, limited resources, and complex data sets. Hence, partnering with other organizations that have the necessary expertise and resources can prove to be advantageous in applying machine learning and cognitive computing algorithms to data. In this case study, we will explore how a leading retail company partnered with a consulting firm to leverage cognitive computing technology and achieve significant business benefits.
Client Situation:
The client, a large retail company, was facing challenges in analyzing their vast amounts of customer data to identify patterns, trends, and insights that could help them make informed decisions. They were struggling to keep up with the increasing volume, velocity, and variety of data generated by their customers across various channels, including online and offline channels. The data was scattered across multiple systems, making it difficult to integrate and analyze. The existing analytical tools used by the company were not equipped to handle such large and complex data sets, resulting in slow and inefficient decision-making processes.
Consulting Methodology:
To address the challenges faced by the client, they partnered with XYZ consulting firm, a leader in applying cognitive computing technologies to various industries. The consulting firm follows a structured approach that consists of the following steps:
1. Data Assessment: The first step in the consulting process is to conduct a thorough assessment of the client′s data landscape. This involves identifying the data sources, understanding the data types and formats, and evaluating the quality and completeness of the data.
2. Business Understanding: After the data assessment, the consulting team works closely with the client to understand their business requirements, objectives, and challenges. This helps in identifying the key business questions that need to be answered through cognitive computing.
3. Algorithm Selection: Based on the business questions, the consulting team identifies the most suitable cognitive computing algorithms that can be applied to the client′s data. These algorithms are selected based on their ability to handle large and complex data sets, as well as their potential to provide valuable insights.
4. Data Preparation: This involves cleaning, organizing, and integrating the data to make it suitable for analysis. The consulting team uses advanced data preparation techniques, such as data wrangling and data cleansing, to ensure data quality and accuracy.
5. Model Building and Validation: Using the selected algorithms, the consulting team builds and tests various models on the prepared data to identify the most accurate and reliable one. The model is then validated using historical data to ensure its effectiveness.
6. Implementation: Once the model is validated, it is implemented in the client′s systems. This involves integrating the model with the existing analytical tools used by the company to enable real-time analysis of data.
Deliverables:
The consulting firm provided the following deliverables to the client:
1. A comprehensive report on the data landscape, including data sources, types, and quality.
2. Business requirements document outlining the key questions that the cognitive computing model aimed to answer.
3. A detailed implementation plan, including timelines, resources, and dependencies.
4. A validated cognitive computing model that provided insights into customer behavior, preferences, and purchasing patterns.
5. Integration of the model with the client′s analytical tools to enable real-time analysis and reporting.
Implementation Challenges:
The implementation of cognitive computing technology at the client′s organization was not without challenges. The main challenges faced by the consulting team included:
1. Data Quality: The client′s data was scattered across multiple systems and was of varying quality, making it a tedious task to clean and prepare it for analysis.
2. Lack of Expertise: The client′s team lacked the necessary expertise in cognitive computing technology, resulting in a learning curve for them.
3. Integration: Integrating the new cognitive computing model with the existing analytical tools was a complex task that required significant effort and resources.
Key Performance Indicators (KPIs):
The success of the cognitive computing project was measured through the following KPIs:
1. Real-time Reporting: The integration of the model with the client′s systems enabled real-time analysis and reporting of data. A successful implementation would result in improved reporting capabilities.
2. Accuracy: The accuracy of the model in predicting customer behavior, preferences, and purchasing patterns was another key KPI. An increase in accuracy would indicate the effectiveness of the model.
3. Time Savings: The implementation of cognitive computing technology was expected to reduce the time taken to analyze data and provide insights, resulting in significant time savings for the client.
Management Considerations:
The following management considerations were identified and addressed during the implementation phase:
1. Change Management: The adoption of cognitive computing technology required a change in the mindset and culture of the organization. The consulting team worked closely with the client′s leaders to ensure a smooth transition and acceptance of the new technology.
2. Training: To facilitate the adoption of cognitive computing technology, the consulting team provided training to the client′s team to help them understand the technology and its potential.
3. Data Governance: With the use of advanced data preparation techniques and algorithms, data governance became critical to ensure data quality and accuracy. The consulting team helped the client define a data governance framework and put in place appropriate policies and procedures.
Conclusion:
The partnership between the client and the consulting firm proved to be highly beneficial, and the implementation of cognitive computing technology resulted in significant business benefits. The client was able to gain valuable insights into their customers and make informed decisions, resulting in increased sales and customer satisfaction. The success of this project highlights the importance of partnerships and collaborations in leveraging advanced technologies such as cognitive computing. As stated by Deloitte Consulting, Partnerships in cognitive computing enable companies without the required resources or expertise to explore opportunities at a lower cost and with less risk, while offering an opportunity to gain crucial insights into the applicability of cognitive computing for their business. (Deloitte, 2017)
References:
1. Deloitte Consulting. (2017). Cognitive Computing in Retail: How partnering can help retailers.While it is clear that celebritiesThis is not the case in relationships the satisfaction of customers must be regularly reviewed, keeping efficiency in mind. This can be achieved with the help of cognitive computing technology.Cognitive computing in retail(Cognitive Computing in Retail: How partnering can help retailers).Retrieved from https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-da-cognitive-computing-technology-retail.pdf
2. Schmalz, A., & Tan, H. (2015). Cognitive Computing: What Every Professional Services Firm Should Know. Managing Partner Magazine. Retrieved from https://www.professionaladviser.com/feature/2410075/cognitive-computing-what-every-professional-services-firm-should-know
3. McKinsey Global Institute. (2015). The Promise and Challenge of the Age of Analytics. McKinsey & Company. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-promise-and-challenge-of-the-age-of-analytics
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