An algorithm is a procedure or formula for simulating or solving a problem.
Many believe we are in the digital era of the algorithm for analytics based problem-solving. Because nowadays running a successful digital business is based on information, the business ability to explore intangible assets such as information and knowledge has become far more decisive for running a real-time intelligent business with unique business advantage.
Information algorithm: Information is growing exponentially, a good algorithm needs to be developed through integrating knowledge-based data into analytic models simulation testing, implemented for improving problem-solving effectiveness. These algorithms require data and understanding the idiosyncrasies of these data is critical to model performance. Information quality directly impacts decision making quality. Understanding how to synthesize new predictors in a way that increases the predictive power of these data is critical to increasing model performance.
Even though people like to think they use objective information and explicit criteria for decisions, they often find out later there were unrecognized issues, hidden criteria and just estimates of the future, not objective facts. They made rational decisions based on what they knew and the beliefs they held at the time. Information-savvy business leaders and professionals should show humility to admit unknown factors, recognize the limitations of their expertise, so they can leverage the right tools and algorithms to do deep analysis, and partner with diverse experts to apply the analytical algorithms for improving problem-solving effectiveness.
Contextual algorithm: Understanding nonlinearity as the very characteristic of the digital organization and business ecosystem is crucial for decision coherence. Contextual algorithm is to leverage data analysis and contextual intelligence to generate formulas for solving complex problems effectively. Because you need to have a big picture thinking to understand the interconnectivity of the parts and the whole, also do analysis to understand past, present, and future contextually. It’s important to do data investigation or any attempt at understanding the business context. Diagnosis analysis helps you to learn from the past, discover patterns; capture insight; predictive analysis allows you to forecast future events with a certain degree of accuracy, prescriptive analysis helps to figure out some methodologies and practices for solving certain problems smoothly.
The context in and with the human experience provides data with meaning, which leads to enhanced insight. The application of nonlinear thinking has to do with complexity which comes in due to the very characteristics such as complexity, ambiguity, diversification, unpredictability, and increased flux working and impacting together. It’s a construct that involves the ability to recognize and diagnose the plethora of contextual variables inherent in the present circumstances, try to identify the parts of a system and how those parts are connected, look at things from different angles, evaluate the dynamic circumstances, decide and act, and identify potential unintended consequences of decisions and actions, then intentionally and intuitively adjust behaviors in order to exert influence in that context.
Learning algorithm: There are many different "deep learning" algorithms which would give different performance under many scenarios. Which algorithm is the one that you should trust? It has to be the one that has the most reliable prediction or performance. Unless you are generating a reasonable estimate of the confidence that one has in your predictions and it is fairly tight, you are probably playing the game with today's machine learning/predictive modeling. This is not science.
Data algorithms are indeed nice tools for data savvy business professionals. It aims to enhance human abilities to use more information without the limitations that the human mind might have, and it uses explicit criteria with an ability to calculate patterns from infrequent data that our brains may have missed. Either for learning, deciding, and problem-solving, a good algorithm needs to be developed through integrating knowledge-based data into analytic models simulation testing, implemented for problem-solving.
An algorithm is a model of the real world. It is a procedure or formula for simulating or solving a problem. The model depends on the nature/structure of the data of the questions that need to be answered. Also, you need to keep in mind that underlying these algorithms are models, models have their own assumptions, strengths, and weaknesses.
Information algorithm: Information is growing exponentially, a good algorithm needs to be developed through integrating knowledge-based data into analytic models simulation testing, implemented for improving problem-solving effectiveness. These algorithms require data and understanding the idiosyncrasies of these data is critical to model performance. Information quality directly impacts decision making quality. Understanding how to synthesize new predictors in a way that increases the predictive power of these data is critical to increasing model performance.
Even though people like to think they use objective information and explicit criteria for decisions, they often find out later there were unrecognized issues, hidden criteria and just estimates of the future, not objective facts. They made rational decisions based on what they knew and the beliefs they held at the time. Information-savvy business leaders and professionals should show humility to admit unknown factors, recognize the limitations of their expertise, so they can leverage the right tools and algorithms to do deep analysis, and partner with diverse experts to apply the analytical algorithms for improving problem-solving effectiveness.
Contextual algorithm: Understanding nonlinearity as the very characteristic of the digital organization and business ecosystem is crucial for decision coherence. Contextual algorithm is to leverage data analysis and contextual intelligence to generate formulas for solving complex problems effectively. Because you need to have a big picture thinking to understand the interconnectivity of the parts and the whole, also do analysis to understand past, present, and future contextually. It’s important to do data investigation or any attempt at understanding the business context. Diagnosis analysis helps you to learn from the past, discover patterns; capture insight; predictive analysis allows you to forecast future events with a certain degree of accuracy, prescriptive analysis helps to figure out some methodologies and practices for solving certain problems smoothly.
The context in and with the human experience provides data with meaning, which leads to enhanced insight. The application of nonlinear thinking has to do with complexity which comes in due to the very characteristics such as complexity, ambiguity, diversification, unpredictability, and increased flux working and impacting together. It’s a construct that involves the ability to recognize and diagnose the plethora of contextual variables inherent in the present circumstances, try to identify the parts of a system and how those parts are connected, look at things from different angles, evaluate the dynamic circumstances, decide and act, and identify potential unintended consequences of decisions and actions, then intentionally and intuitively adjust behaviors in order to exert influence in that context.
Data algorithms are indeed nice tools for data savvy business professionals. It aims to enhance human abilities to use more information without the limitations that the human mind might have, and it uses explicit criteria with an ability to calculate patterns from infrequent data that our brains may have missed. Either for learning, deciding, and problem-solving, a good algorithm needs to be developed through integrating knowledge-based data into analytic models simulation testing, implemented for problem-solving.
An algorithm is a model of the real world. It is a procedure or formula for simulating or solving a problem. The model depends on the nature/structure of the data of the questions that need to be answered. Also, you need to keep in mind that underlying these algorithms are models, models have their own assumptions, strengths, and weaknesses.
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