Optimize the Application Maintenance Estimation (AME) Process

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Augment your traditional expert estimation with parametric estimation to reduce surprises. Your Challenge Application Development Managers have to defend maintenance budgets to executives. Often the estimates are based on a single method – expert estimation – instead of also including parametric estimation in the process. The result is a variance in estimated and actual budgets caused by varying degrees of scope interpretation by experts who are called upon to estimate. Our Advice Critical Insight Having a single expert estimation technique is not enough. You need to include an analytical parametric technique to quickly surface assumptions and have that discussion early rather than having to defend a budget shortfall. Impact and Result Produce a charter that will help you sell an optimized Application Maintenance Estimation process internally for the benefit of the business.

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Optimize Your Application Maintenance Estimation (AME) Process
Augment your traditional estimation with parametric estimation to reduce surprises.
This research is designed for an Application Development Manager who:
• Has to defend maintenance budgets to business executives.
• Needs to understand the gaps that currently exist in his or her unbalanced estimation process.
• Wants to know what metrics to measure prior to a wider rollout.
• Requires an understanding of the various expert and parametric estimation techniques to make an informed decision.
Schools of thought:
Parametric estimation: Use of analytic formulas to derive estimation.
Expert estimation: Use of analogy and experience to derive estimation.
Hybrid (our recommendation): Combination of at least one parametric and expert to balance the approaches.
Drivers/Trends/Change:
Applications are getting more complex for future estimation.
Velocity of development teams is increasing while the number of resources is not rising as quickly.
Users are demanding quality apps.
Value Creation:
For the development team: Having the right funding will enable enough time and budget to do it right.
IT: Ability to clearly set expectations and justify costs to the business.
Business: Improving overall quality of produced deliverable.
Understand the Project Rationale A historic portfolio is essential in establishing your estimation model. It will allow you to assess your first
pass draft estimation against known values to establish early confidence.
Structure Your Application Maintenance Estimation Optimization Team Development team estimators and process analysts should exhibit some
manner of subject matter expertise and authority. Otherwise, the estimates will not be justifiable to the business. The right people must be
involved in the optimization effort for the result to have credibility.
Analyze Your Current Process Gaps Inevitably a single mode estimation approach will lead to inaccuracies that are difficult to catch early.
Optimize Your Estimation Process This exercise is a model training initiative. You are trying to create a model using historic information to
help make a future informed decision. If your historic project data is not clean, this step will not automatically rectify the meta issue around
quality.
Implement Your New Estimation Process A gradual rollout for estimation will help ensure that any conflicts will not arise at one time and
overwhelm the operational team. By focusing on a smaller set of projects, any estimation discrepancies can be managed in a controlled manner.
Measure Ongoing ValueAny maintenance project that was a significant outlier in your trends can be the symptom of a bigger problem. Take the
time to address these outliers in terms of risk for future maintenance projects. Inject a modified risk assessment into your new maintenance
estimation process.

Optimize the Application Maintenance Estimation (AME) Process

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

Artificial Intelligence: Facts and Myths

Joaquim Jorge

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Tech Trends Report 2024 Future Today Institute.pdf

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Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

Presentation on how to chat with PDF using ChatGPT code interpreter

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GenCyber Cyber Security Day Presentation

Michael W. Hawkins

This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

Powerful Google developer tools for immediate impact! (2023-24 C)

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Optimize the Application Maintenance Estimation (AME) Process

1. Optimize Your Application Maintenance Estimation (AME) Process Augment your traditional estimation with parametric estimation to reduce surprises. This research is designed for an Application Development Manager who: • Has to defend maintenance budgets to business executives. • Needs to understand the gaps that currently exist in his or her unbalanced estimation process. • Wants to know what metrics to measure prior to a wider rollout. • Requires an understanding of the various expert and parametric estimation techniques to make an informed decision. Schools of thought: Parametric estimation: Use of analytic formulas to derive estimation. Expert estimation: Use of analogy and experience to derive estimation. Hybrid (our recommendation): Combination of at least one parametric and expert to balance the approaches. Drivers/Trends/Change: Applications are getting more complex for future estimation. Velocity of development teams is increasing while the number of resources is not rising as quickly. Users are demanding quality apps. Value Creation: For the development team: Having the right funding will enable enough time and budget to do it right. IT: Ability to clearly set expectations and justify costs to the business. Business: Improving overall quality of produced deliverable. Understand the Project Rationale A historic portfolio is essential in establishing your estimation model. It will allow you to assess your first pass draft estimation against known values to establish early confidence. Structure Your Application Maintenance Estimation Optimization Team Development team estimators and process analysts should exhibit some manner of subject matter expertise and authority. Otherwise, the estimates will not be justifiable to the business. The right people must be involved in the optimization effort for the result to have credibility. Analyze Your Current Process Gaps Inevitably a single mode estimation approach will lead to inaccuracies that are difficult to catch early. Optimize Your Estimation Process This exercise is a model training initiative. You are trying to create a model using historic information to help make a future informed decision. If your historic project data is not clean, this step will not automatically rectify the meta issue around quality. Implement Your New Estimation Process A gradual rollout for estimation will help ensure that any conflicts will not arise at one time and overwhelm the operational team. By focusing on a smaller set of projects, any estimation discrepancies can be managed in a controlled manner. Measure Ongoing ValueAny maintenance project that was a significant outlier in your trends can be the symptom of a bigger problem. Take the time to address these outliers in terms of risk for future maintenance projects. Inject a modified risk assessment into your new maintenance estimation process.

Optimize the Application Maintenance Estimation (AME) Process

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