You can look at the data from these 6 angles
With the data in hand, what perspectives can we observe and analyze in practice? Six angles of reference are provided here.
- 1) Number of activities
Go to the monthly pivot table, put the activity item and time in the "row", then put the "duration" in the "value", the value field is set to the count item, and the number format is set to normal.
It is possible to filter the number of clicks on active items in different time periods, obtain a frequency value based on the number of clicks, and observe the trend in the number (frequency) of activities.
- 2) Activity summing
To enter the monthly perspective, put the active item and time in the "row", then put the "duration" in the "value", set the value field to the sum item, and set the numeric format to "[h]:mm:ss".
The sum of the duration of the activities in different time periods is screened, the length of time is obtained based on the number of times, and the trend of the sum of the activities is observed.
- 3) Activity rate
For example, when we look at the number of "work" activities, if the number keeps increasing, does it mean that we are more and more engaged in our work? Not necessarily. Because there may be a situation where the working hours remain the same and the number of simple tasks increases, it is possible that the number of interruptions at work has increased, and although the number of activities has increased, the level of work commitment has actually decreased.
To set the ratio of indicators we can open the monthly perspective, put the activity item and time in the "row", then put the "duration" in the "value", value field set to sum item, digital format set to "[h]:mm:ss", then drag the duration once more to "value", set to value field set to count, digital format set to general.
Then add the count/sum item for the formula to the new column:
Drag and drop if the data remains unchanged, click the Excel tab, remove the getpivotdata generation √, and rewrite the formula again:
Get the number of "views" per hour, as shown in the figure:
We'll be able to spot a spike in the April reading rate based on the summation/counting ratio, and then you can ask yourself, from a 5W2H perspective, what went wrong?
- 4) Volatility
Sometimes it may not feel obvious just to look at the numbers, but it's much easier to see the fluctuations if we use a line graph:
Immediately there will be a spike in August and December, and then the search for the cause can continue.
- 5) Seasonal changes
There is a baseline for activity time in life, but it fluctuates with the seasons, just as you spend more money on ice cream in the summer than in the winter, so do activities like "washing up".
The weather in Guangzhou starts hot in March and runs through September, with a rebound in October and November.
The time consuming curve of wash time completely overlaps with the weather, the hotter the weather, the shorter the wash time, which is a seasonal change.
Similarly, we can analyze the curves of weekdays and rest days, and such seasonal and regular changes can help us better predict time consumption.
- 6) Efficiency values (peak/valley)
We filter the activity terms of gain time and damage events, arranged by hour, to find our 24-hour efficiency distribution.
It can be seen that the damage is mostly distributed at 8:00, 18-19:00. These two time periods correspond to "just got up" and "just got off", respectively, which means that if I want to be more efficient, I must improve my actions during these two periods immediately.
By the way, you can give yourself a list of actions.
- 1.Get up in the morning and go downstairs for a run or listen to a book/watch a study video.
- 2.Listening to books on the go after work / talking on the phone with family, studying or exercising / reading immediately upon arrival home.
You can't go wrong with encountering time periods that have to be improved, filling them with friendships, sports, and learning.
- 7) Remarks - In-depth details
When we analyze the data, in addition to the duration of the first and second levels of classification, we also analyze in depth the changes in the "notes", because we control the number of active items from the beginning, then there must be some behavior that cannot be listed separately.
For example, in "3)", we found that the rate of "reading" in April was significantly abnormal, so we can further analyze the notes of "reading" to see what caused the fluctuations under "reading".
Add a note to "Row" in the monthly pivot table, select summary data, and descending order, as shown in the figure:
You can immediately see exactly what I did that took the most time to do under "read" time.
Number of activity items, sum, ratio, fluctuation, seasonal (cyclical) variation, filtering of notes, just 6 are common angles from which we look at the data, follow this process through all your activity directions and you will surely find more than one new land!
Pick any angle to get started, then keep asking yourself questions, digging deep, and using action to solve those problems you see!