How is the scientific method not used for this always? The scientific method and way of thinking is probably the most useful thing you can ever master. Science allows you to set up a question and then set up an experiment in such a way that you cannot lie to yourself about the result.
For example, let's ask the question: Is my business doing well?

A layman's approach will almost certainly be evaluated on a subjective/emotional basis. i.e. "We are doing something great!" "Everyone is happy." "I have so many customers" "Funding is in!" "I got a great review on tech crunch three months ago"

A more scientific approach is to first define the question in such a way that it can be measured.
So let's say a business that does well does three things:

Does not lose money,
Has happy customers(low support calls/emails, high reviews,product is used),
Has happy employees (low turnover, gets quality work done (measurable by issue tracking)

Now you can say: Does my business meet the requirements for a successful business. If no, write down those numbers. That is now your control and baseline.

It is time to experiment. It's best not to change more than one thing at a time in science so you know exactly what the effect is vs the control.

Lets say you realize you're losing money and your customers aren't happy. You do some research and come to believe (not know) that it's because the login screen is incredibly awkward. Google analytics shows that the bounce rate for the front page is huge so there's some data to back this up. It's time to form your first hypothesis.

My business isn't doing well because the login page is so incredibly awkward. If I fix the login page the business will do better.

Remember that both the "awkward" and "do better" while subjective declarations have actual metrics to back them up: bounce rate on analytics and the previously defined metrics for a good business.

Now you do the experiment defined earlier: fix the login page.

Now you collect data. If your bounce rate goes down, your customers are happier and start paying the monthly fee. Then you have solved your problem and you know with a very high degree of certainty how you have solved your problem.

Now let's say you looked at the data at the end of this experiment and noticed a decrease from the control you set a while back. Well, obviously that was not the problem (or at least not 100% the problem, part of science is knowing how certain to be about things) Maybe next you can define the problem as: the site is buggy, issues aren't resolved quickly. If I observe my employees I can see that they spend a lot of time staring at the 98inch projector screen I set up in my 'cool office' instead of coding. Now you can do a trial run of turning off the projector on some days vs others and seeing if the work goes up, stays the same, or goes down. Or you can discuss the problem with your employees and see if they have input. Then define the experiment using that; just don't forget to actually measure something objective at the end of the day.

This way of thinking is invaluable. You can drill down exactly to the problem core. Waste less resources. Argue less. Most importantly you can actually sit down at the end of the day and prove that you have solved a problem with actual data that cannot be easily disputed, and if you've really done it right the experiment can be repeated and have the same result each time.