Hey Welcome to the Site!
What you are saying is right, Data Science din't reach to the stage where it has some standard methods for achieving this(standard procedures, don't know we would be able to reach that stage in near future). But we have some general standards like:
- Forecasting: ETS,ARIMA,SARIMA etc
- Prediction: Linear Regression,Random Forest, GLM, Neural Network etc
- Classification: Logistic Regression, Random Forest etc
When you go to granular level it is hard to generalize, as every business problem is different and one single method cannot be used for solving all the business problems.
So, to answer the next question how do you get confidence that the outcome is good enough, I assume that you have heard about RMSE,MAPE and many more for predictions and confusion matrix for classification problem. We use these metrics to see access the models performance, for example if you are trying to classify whether the given cell is cancer cell or not, there are 100 records in which 90 are non-cancer cells and 10 are cancer cell, your model gives 99% accuracy but could classify 5 out of 9 literally 55% of the total in such scenarios you need to look cannot use accuracy, you need to use F1 score etc. As you were asking about a model right, all models are not useful. True not all models built are going to go for production level, you would choose the best one and productionize it. You can re-train your model on a basis(Daily, Weekly,Monthly based on business requirement). Would you call it a day off post completion of validation? I wouldn't, I would go to the Subject Matter Expert present him the results ask him/her for their insights, if they both are inline then I would do a Beta testing on some actual data and then productionize it.
Now to address your last question, There is no standard saying that this is good or bad, if it works for you, your Business then that is a Good Model. To convenience your mangers and subject matter(Data) experts, you need to dig deep into the data try all different scenarios ask as many questions as possible. Try understanding the data very well. So, you can answer Business Questions with data supportive answers(This is possible only when you are well-worse with data). As they are very good with business they would be asking questions with respect to business, you need to be ready with all such scenarios by understanding business and data well.
Finally, I do have a feeling like you do. I did alot of things but nothing worked but you shouldn't be unhappy as you understood that these are the ways which would lead you to Unsuccessful results(best example is Thomas Alva Edison has used 1000 diff metals before using Tungsten to make a bulb). Similarly all the methods which we have tried are different steps you have tried to get the solution. My funda is, did I try something different/new everyday or not. Crucial part of this process is, maintenance of clear documentation at each and every step. Which would come in handy in the near future.
Anything in R&D is never a waste it is just an other try or experiment, so your work is never waste. Your are trying to build a solid base for the bright future of your company.