How to avoid trouble: principles of good data analysis

Do some background research before running any numbers. Be sure you understand the objective, the theory, the jargon, and the conventional wisdom. Ask others what they know about the problem. Do more of the same web-searching that brought you to this site, and be critical of everything you find.
Be thorough in your search for data. In the end, your forecasts will contain no information that is not hidden in it somewhere. In some cases (say, designed experiments) the data may be limited in scope and already in hand, but in many cases it will be up to you to identify and collect it, and this may the most important and time-consuming part of the project. We now live in the era of "megadata", but often the data that you need is not so easy to find.
Check the data carefully once you have it: make sure you know where the numbers came from, what they mean, how they were measured, how they are aligned in time, and whether they are accurate. A lot of cleaning and merging of data may be needed before any analysis can begin, and you should know how to do it. You may find that you need yet-more-data or better-quality data to answer the key questions, and sometimes the most useful lesson is that the organization will need to do a better job of managing its data in the future.
Use descriptive, self-explanatory names for your variables (not Y’s and X’s or cryptological character strings) so that their meaning and units are clear and so that your computer output is self-documenting to the greatest extent possible.
Once you begin your analysis, follow good modeling practices: perform exploratory data analysis, use appropriate model types, interpret their estimated parameters, check their residual diagnostics, question their assumptions, and validate them with out-of-sample testing where possible.
Make effective use of statistical graphics in your own analysis and in your presentation of your results to others, and follow rules of good graphics. Among other things: graphs should be self-explanatory and not contain visual puzzles, they should have titles that are specific to the variables and models, axes should be well-scaled and well-labeled, the data area should have a white background, grid lines (if any are needed) should not be too dark, point and line widths should be sized appropriately for the density of the data, self-promoting artwork (3-D perspective, etc.) should be avoided, and above all, the data should tell its own story.
Keep in mind that not all relationships are linear and additive, not all randomness is normally distributed, and regression models are not magic boxes that can predict anything from anything. Be aware that it may be necessary to transform some of your variables (through deflating, logging, differencing, etc.) in order to match their patterns up with each other in the way that linear models require.
When comparing models, focus on the right objectives, which are usually making the smallest possible errors in the future and deriving inferences that are genuinely useful for decision making. A good fit to past data does not always guarantee an equally good prediction of what will happen next, and statistical significance is not always the same as practical significance.
Other things being equal, KEEP IT SIMPLE and intuitively reasonable. If others don’t understand the model, they may not use it, and perhaps they shouldn’t: simple models often outperform complicated models in practice.
If you use automatic forecasting software, you are still responsible for the model that is chosen, and you should be able to explain its logic to others. The availability of such software does not make it unnecessary to know how the models work. Rather, it makes that knowledge even more important. (By reading what is on this site, you have become partly qualified.) Be aware that in automatic rankings of models, there may be only tiny differences in error stats between the winner and its near competitors, and you may need to take other factors into account in your final selection, such as simplicity, clarity, and intuition. And again, your software cannot make something out of nothing. If your data is not informative or not properly organized to begin with, automatic methods will not turn it into gold.

Leave a paper trail, i.e., keep well-annotated records of your model-fitting efforts. Don’t just save your computer files: write up notes as you go along. Someone else (who may be a sharp-penciled auditor or perhaps only yourself 12 months hence) may need to reconstruct what you did and why you did it. Intelligent naming of variables and labeling of tables and charts will make this easier.

Neither overstate nor understate the accuracy of your forecast, and do not merely give a point value. Always report standard errors and/or confidence intervals.

If different forecasting approaches lead to different results, focus on differences in their underlying assumptions, their sources of data, their intrinsic biases, and their respective margins for error. Don’t just argue over the outputs. Look for opportunities to combine independent viewpoints and information.

After all has been said, if you believe your model is more useful and better supported than the alternatives, stand up for it. The future may still be very uncertain, but decisions ought to be based on the best understanding of that uncertainty. If you don't have confidence in your model (or anyone else’s), admit it, and keep digging.
Thank you!