Tuesday, May 24, 2022

Initiatecauseeffectanalysis

Analytics has no value until it informs decisions, optimizes processes, improves problem-solve effectiveness and overall organizational maturity.

The characteristics of digitalization are complexity, uncertainty, unpredictability and ambiguity, data analytics is the significant tool to help digital professionals (from leaders/managers to front desk/customer reps) make sound judgment and improve problem-solving effectiveness and efficiency. 

Data analysis is an interdisciplinary approach, involving engineering practice, science and art, etc, to discover patterns, generate great insight, and create multifaceted business value.



Do nonlinear cause-effect analysis
: It is fundamental but critical to figure out what exactly is the problem. For complex problems, trying to fix the wrong cause of a problem will waste time and resources, increase anxiety. The assumption that there is a single cause to a "problem" in a complex adaptive system is unhelpful. There are multiple, inter-related dynamics and you need to be looking for patterns rather than isolating causes. It’s not so effective to use linear logic to understand highly complex, nonlinear cause-effect relationship scenarios. The predictive “cause and effect” in system dynamics can include nonlinear cause and effect models, predict outcomes, find the factors involved in the “incorrect" outcome, in order to understand the interconnectivity between issues, digging through the root cause. Until the underlying problem is addressed, the symptom or result will continue to return.

Apply system analysis to come up with multiple solutions: Digital era is about people-centricity, options, innovation. For effective problem-solving, you have to ask yourself whether you are offering something in your dialogue that really helps customers see how they can differentiate themselves or their business practices. Understand the situation, understand customers, understand expectations from each group involved, understand motivation drivers; evaluate which alternatives might work better than others - that is the realm of logical analysis within systems understanding. Systems analysis needs to be dynamic, look more to the patterns, enables the management to keep an open mind, explore new possibilities, address risks, because the best business solution based on the systems analysis is the one that responds perfectly to its dynamic business environment smoothly.

Leverage risk analysis to improve problem-solving effectiveness:
Risks and opportunities co-exist. To deal with uncertainty and ambiguity, predictive analytics refers to future-oriented analyses that can be used to forecast business trends, help to drive improvements in business practices for improving problem-solving effectiveness. It involves techniques such as regression, forecasting, simulation, and risk analysis, hunting for value position. More often, business is looking at risk in a negative context but to grasp opportunities for growth, businesses should adapt that context at point of predictive analysis to identify the upside from a specific uncertainty one is threatened with for opportunity management, establish insight into approach and style in different situations and have the ability to learn to reflect, have the inter-disciplinary knowledge to smooth problem-solving.

Data based intelligence touches almost every critical aspect of business. In practice, businesses are using a variety of analysis tools to do problem diagnosis (current state), gap/impact analysis, risk analysis for improving manageability. Analytics has no value until it informs decisions, optimizes processes, improves problem-solve effectiveness and maturity.

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