Here's how you can utilize algorithms to assess project success in the tech industry.

From my point of view project success in the tech industry using algorithms can be simplified by focusing on KPIs and metrics that are relevant to our project goals.

Actually I disagree with defining metrics as the starting point. As projects are a means to an end, those specifying tasks to be completed by the project, those creating the means, and those at the receiving end of the project need to be identified and involved in the process of metrics definition. Failure to start with these stakeholders leads to a high chance of creating vanity metrics or self-fulfilling KPIs.

In technology transfer, it is crucial for scientists and technology translators not just to use standard algorithms but to understand the company's core business and strategic goals. This understanding allows them to effectively tailor technology to meet specific business needs. After this, employing Modeling, Simulation, and Optimization (MSO) becomes beneficial. MSO helps develop customized models that fit the company's unique requirements, ensuring that the technology integrates seamlessly with operations, enhances efficiency, and aligns with the company's long-term goals. This approach guarantees optimal utilization of technology and fosters sustained growth and success.

Ensure that if an algorithm is needed to optimize your solution, you must verify some key points: 1. The algorithm should be easy to understand and modifiable. 2. Your code should maintain a standard coding approach and undergo quality checks (which can be done using various open-source tools such as SonarQube, CodeRabbit, etc.). 3. Before implementing the solution, you must have metrics around your solution that will ensure future performance of your app.

The key metric is “Compensation”. I’m not sure which is more shocking, that LinkedIn poses these questions or that people provide their expertise to LinkedIn in response uncompensated. Your professional experience and time is valuable. Why are you giving it away to LinkedIn for free?

In technology transfer, more than to use a predefined/generic algorithm, the scientist/technology translator must understand the underlying business and the main goals that the company would like to achieve. After that, Modelling, Simulation and Optimisation (MSO) is usually a nice way to develop a Tailor-made model fitted to the company real needs.

In my opinion, selecting the appropriate algorithms is essential for ensuring accurate evaluation. Static code analysis tools effectively detect errors, code smells, and vulnerabilities, saving time and ensuring consistent evaluations without manual review. In order to measure user satisfaction, sentiment analysis algorithms are highly effective. By processing user feedback, these algorithms provide a comprehensive view of user satisfaction. Matching the complexity of the algorithm to the metric is essential to avoid unnecessary complications or misleading conclusions, ensuring relevant and actionable insights for better decision-making.

Selecting the right algorithms is essential for accurate evaluation. For code quality, static code analysis tools are indispensable, automatically scrutinizing code for errors. To gauge user satisfaction, sentiment analysis algorithms excel in processing user feedback. Matching the algorithm's complexity to the metric being measured ensures relevance and actionability of insights gained. This alignment maximizes the effectiveness of evaluations, empowering organizations to make informed decisions and drive continuous improvement.

Algorithms are the core intelligent parts of the processing software which connect the upstream with the downstream. They are the brain behind crunching and processing data, values, tables, networks, process and manipulating product subsystem components according to the end user experiences. They stitch the overall problem objectives into meaningful solutions in the form of dashboards or actions which form the crux of the product, process or project. They are equivalent to design patterns which can be human or AI derived based on the design thinking or first principal objectives in the system, software or hardware ecosystem. The overall algorithmic performance determines the outcome of the project, product or process.

La selección de los algoritmos adecuados es esencial para una evaluación precisa. Para mejorar la calidad del código, puede usar herramientas de análisis de código estático que revisen automáticamente el código en busca de errores. Para medir la satisfacción del usuario, los algoritmos de análisis de sentimiento pueden procesar los comentarios de los usuarios para medir la satisfacción general. Es importante hacer coincidir la complejidad del algoritmo con la complejidad de la métrica que se mide para garantizar que la información que obtenga sea relevante y procesable

Data is king. All algorithms depend on it. If yours isn’t good, you have GIGO. GIGO = Garbage In Garbage Out. Focus on data. Not the algos.

Data's transformation to digital data is the key in my experience. A CSV format, for example, can reflect greatly in the algorithms' execution success if done right. Applications are numerous though and encompassing all key metrics in a format like CSV becomes an easy way (or the easiest way) out.

Data + Meta data plays a vital role in extracting the biological insights. Quality, Completeness (especially in meta data), and Diversity are crucial. These helps in methods selection and down stream interpretation

Data collection is foundational for algorithmic assessment, requiring establishing tracking systems to gather metrics such as feature usage, system uptime, and team velocity. Clean, organized data is essential for accurate algorithmic evaluations, emphasizing the need for robust collection methods. Data integrity is critical to assessment reliability, underscoring the importance of reliable procedures and accessible storage formats. Prioritizing meticulous data collection enables organizations to enhance evaluation accuracy, facilitating informed decision-making and proactive management. Comprehensive datasets aid in identifying trends, patterns, and areas for improvement, driving continuous project enhancement.

Data collection is considered one of the cornerstones of algorithmic assessment. An algorithm can only function with data, and this data must represent either the initial states of the problem or the optimal states of the system (after applying the solution). Therefore, data collection can encompass both or just the initial states.

One of my favourite ways to gather usage data is A/B testing. It is fairly simple, low effort and can gather valuable insights with proper tracking. One way to use A/B testing is to serve customers with different states (Variant - Control / A - B states), but, to minimize result, we can pass small sample first. For example, every 1% of traffic are getting new homepage / new UI then, we can measure user behaviour on that 1%, e.g. measure click rate, scroll behaviour, pixel track, before making full feature release. This is a great way to validate ideas before making full commitment to release and removes old feature.

Una vez que haya recopilado los datos, es hora de ejecutarlos a través de los algoritmos elegidos. El análisis puede revelar patrones y tendencias que no son inmediatamente obvios, como una correlación entre el número de confirmaciones de código y la aparición de errores. Esta información puede ayudarte a comprender qué salió bien y qué no, lo que te permitirá tomar decisiones basadas en datos para mejorar proyectos futuros.

To analyze the results of a chosen algorithm, start by reviewing key performance metrics like accuracy, precision, recall, F1 score, and ROC-AUC for classification, or MAE, MSE, and R-squared for regression. Examine the confusion matrix for classification problems to understand prediction errors. Use cross-validation to ensure robustness and compare results against baseline models. For regression, analyze residuals to spot patterns in prediction errors. Employ visual tools like ROC curves and scatter plots for deeper insights. Gather stakeholder feedback and iterate on the model, refining it based on the analysis to enhance performance.

After collecting data, running it through selected algorithms unveils hidden patterns and trends, like correlations between code commits and bug occurrences. These insights elucidate project strengths and weaknesses, empowering data-driven decision-making for future improvements.

To analyze the results, we must understand the problem. Only then can we comprehend what the algorithm did and how it attempted to solve the problem. It is in the analysis of results that we find the limitations of the solution or simply a solution to part of the problem. In my experience, algorithmic assessment and the results it yields never fully capture the complexity of the problem.

Before we jump with interpreting the algorithm findings, there is need for positive and negative control data. Once you benchmark your algorithm, then start interpreting findings will be more beneficial.

Interpreting algorithmic findings transcends mere numerical analysis; it demands contextual understanding. For example, if an algorithm suggests high user satisfaction but the project exceeded its budget, one must assess the benefits versus the costs. Contextual comprehension is paramount for making nuanced judgments about project success.

To interpret findings with a chosen algorithm, start by analyzing key metrics like accuracy, precision, recall, F1 score, and ROC-AUC for classification or MAE, MSE, and R-squared for regression. Examine the confusion matrix to understand prediction errors and perform error analysis to identify where the model is making mistakes. Review cross-validation results for robustness and perform residual analysis for regression models to spot error patterns. Assess feature importance to determine impactful variables. Use visual tools like ROC curves and scatter plots for insights. Compare results with baseline models, gather stakeholder feedback, and iterate to improve the model.

I would say in many cases, some simple and straight analysis algorithms like looking at month-over-month/yoy change will do good enough. Try not to complicate too much. All algorithms used should be centered around the needs/problems of the bussiness.

Lo más difícil es interpretar los datos ya que los datos sin una lectura correcta no sirve para nada, muchas veces creemos que muchos datos es bueno y no, debe haber una correcta lectura para obtener os resultados deseados.

No algorithm is perfect. There will always be a need for human intervention at various points in the process. Let's say you define your success metrics clearly at the start. Note: these can qualitative or quantitative. Evaluate the end-result of your process based on those metrics. Once evaluated, adjust the parameters and hyperparameters. In some cases, this means starting over, and possibly even adjusting your success metrics. It's extremely important to incorporate that flexibility into your process. Do that enough times, and success is guaranteed. In sum: define your metrics; evaluate your results; rinse and repeat!

When people are applying metrics like KPI's to algorithm creation, there seems to be an assumption that algorithm creation is linear with time. Quite often, there is no productivity and then all of a sudden the algorithm is created after the right amount of time with the right approach. The benefit of an algorithm definitely increase efficiencies in the process. But the development of an algorithm can be non-linear. I have seen cases where a manager would ask a person to stop working on an algorithm using the KPI approach, and the benefit from that algorithm would be lost if it was not completed.

The concept of measuring success has long been established, featuring numerous effective metrics, necessary data types, algorithms, and other resources that companies have widely shared over the years. Depending on your specific needs, there might even be established standards for these metrics. It's often beneficial to begin by exploring existing solutions and recommendations. Review available case studies to learn from their successes and failures, then build upon them. In doing so, you could: 1. Save considerable time in research and development. 2. Avoid common pitfalls. 3. Discover insights that you might not have considered.

Although most of the above content rightly focus on a quantitative approach, usually KPI driven, one must not forget about qualitative approach. Algorithms seldom use these approaches but since results are always analyzed by a human at the end of the day, one must not forget to use context, common sense and experience in interpretation of quantitative results. If an approach has 50% higher ROI than another, great, but if the base ROI is 5€, modifying the processes make no sense, or if it completely wreaks havoc on the personal schedule of the teams, for instance. I'm all in on a data driven approach, bu keep in mind that as analysts and even more so as managers, we have to think about the bigger picture and further than the raw data

We need documentation or flow charts of what you implemented. It is crucial to maintaining your work. Who knows what your code is following the best practices.