Data analysis methodology to make time work for you
We've defined before, "what the law of timekeeping is." It is a "method of actively controlling the flow of time and achieving goals by recording time." So all the data exporting, organizing, templating, etc. that we have done before are actually prepared for "proactive control and goal achievement".
When we have the data, we also need to learn how to make guesses about the data we already have and validate them with actions, only a combination of data thinking and action can help you reach your goals.
So, data methodology, in a nutshell, is five steps: get data → process data → visualize → make guesses → verify. Repeat the cycle.
- 1) Access to data
When we export data from TimeTrack, we make sure that the data does not overlap, i.e. "up to 24 hours a day", there is no overlap time.
Then, even if there are groups, we don't check the "Show separate groups in CSV report" in the settings, because the data will be interrupted when exporting the data, and you need to manually modify the proportional data and increase the workload.
No clusters in the figure above, "Cluster A" in the figure below
2) Data processing
We need to make additions (e.g., additional notes), deletions (e.g., deletion of the first data entry at the beginning of the month), and corrections (e.g., correction of the "other" classification) to the exported data before it can be officially deposited in the source data table.
3) Data visualization
We created pivot tables and pivot views, coupled with conditional formatting, to allow us to see changes in the data at a glance.
We make "self-righteous" guesses by looking at data comparisons, trends, peaks, and outliers.
5) Verified Conjecture
With real data, we can use data pivot tables to discover the difference between imagined and actual data.
For example, "I thought I got a higher salary when I changed jobs", but an actual analysis of the data found that "the hourly wage decreased after changing jobs"; "I thought I was studying after work", but an actual analysis of the data found that "all activities after work are 'entertainment'", etc.
This data analysis methodology has slowly helped me to develop data thinking, what is data thinking?
Let me give you a common example from life, if someone asks.
"What's the point of reading a book and forgetting it?"
The same goes for the recorded data, if you simply save the data and keep repeating the "get - process - visualize", it will not help you in any substantial way except to look at the data and get high.
You have to get out of this cycle, and don't forget that you now have an advantage that most people don't have, and whatever it is, you can at least measure it in terms of "time", and time can work for you.
For example, we're looking at a goal and A says, "I'm going to read 100 books this year".
What does this phrase mean in terms of time?
We opened the time data, sifted through the "reading" data for the past year in the monthly pivot table, selected all the reading time, to see how long it took to read a book myself, and my answer was 2.5 hours/book (including books that were flipped and not read).
That means "I'll read 100 books this year" means 100*2.5 = 250 hours this year, an average of 0.7 hours and 42 minutes per day. It can be done if nothing goes wrong.
Suppose we see another sentence where B says, "I can only see 50 movies this year at most".
We sifted through the "entertainment" data and located (movies with "@" tags) all the movies and found that I watched 235 dramas last year (movies + variety + documentaries, with movies accounting for about 80% of the total), of which there were about 188 movies, which took an average of 1.7 hours each.
In quantitative terms, "only 50 movies" B is likely to fail, since this goal is already 30% too high (180-180*0.3=126), so it is an unreasonable goal for B.
Did you find it?
The ability to "figure out if a certain descriptive phrase makes sense" based on your historical time data is so important!
Most people don't have this ability because they don't have a reference to historical data that they have time to record, they don't know how many books they read in a year and how much time they spend at work or sleeping.
This kind of "make time work for me" thinking is to quantify the problems in our lives with time and then replace them with familiar indicators that are more conducive to achieving our goals. Of course, you can estimate the data, but you know how unreliable the "feeling" is when you've logged your time, and even less reliable the predictions you get.
Once you have mastered the methodology of data analysis and the data mindset that "you can measure everything with time data", you need to look at the data from more of a perspective. So what are some of the perspectives we are looking at when analyzing the data?