Tesla's "Data Engine" Strategy From Andre Karpathy

If leveraged quickly, using data can lead to life changing opportunities, fix problems, and create new connections you didn't know existed.

It did it for Tesla. They used their data engine to help them teach cars how to drive themselves autonomously.

But 80% of people have no idea that how data can be useful for them.

Here's my 6 Step System that I used to grow based off of Tesla's data engine:

1) Find Your Data Source

The first step is to understand where your data is coming from and how you are going to track it. What is it that you are trying to improve?

For example, when I was trying to stay off of my phone more so I could get more work done, I used the screen time insights my phone provides as my data source.

If you're trying to read more, you might track how many pages you read that day or how many minutes you read for. Whichever source and tracking method you choose, it should be related to your end goal/behavior.

The data you track should also be consistent and clean day to day, as well as, specific and quantitative.

2) Find A Problem

Once you have gathered enough data from your source, you will start to notice trends that can be used to help find strengths and weaknesses in your current system.

I remember when I finally realized my biggest weakness looking at my screen time trends. There were these huge spikes in the data at the same exact times everyday that would end up completely plummeting my productivity.

Using data can be a source for finding hard truths and helps you remove cognitive dissonance (or bring it to the surface of your awareness).

3) Boost The Problem To Find All Edge Cases

Now that I knew there is a problem at these specific times of day I could further digest and look into it to keep myself off my phone.

This is known as finding all the edge cases. The way to do this is by trying to collect more data on the specific problem, or further looking into the existing data you have where the problem exists.

So now at those times of day I might write a list of the environment I'm in, who's around, how I'm feeling, etc. to get more data on why I might be using my phone at those times.

4) Label All Cases

After you have gathered more data on the specific problem you boosted, you can start to identify and label the root causes to find solutions.

Now, I notice that every time I walk into my bedroom I pull out my phone and get distracted for 30 minutes. Or that every time I'm feeling bored after I get home from work I pull out my phone. Through labeling the data we can give more context as to why the problem is happening.

5) Train Data Set

Next, is training yourself based off of the data set now that you understand the underlying reasons for the problems.

With my newfound insight of why I was using my phone, I could place new habits in there way so I wouldn't distract myself. For example, when I get home from work now, I use that as a trigger to practice guitar instead of picking up my phone. I trained myself to avoid the distraction by placing something new in its place

With this new perspective you can iterate to find solutions and continually practice them until they are ready to be used.

6) Deploy back into your model that

The last step is deployment which helps you mine what is incomplete from your data set. This starts the cycle over again since you now have new data and new problems to work with. Deploying the new system you practiced helps you find more incomplete pockets so that you can them fill them too.

The Data Engine Flywheel

After a long enough time horizon, and enough iterations you will have a fully functioning data engine that will drive you towards growth.

Still remember, tracking the data is the easy part. It's finding key insights, then executing on them that is hard. Go out and give it a try. Start your flywheel.