7 habits of highly effective data analysis

7 habits of highly effective data analysis

7 habits of highly effective data analysis

Highly effective data analysis isn’t learned overnight, but it can be learned faster. Here are 7 habits of data analysis I wish someone told me for effectively incorporating, communicating and investing in data analysis geared towards an engineering team.

If you can’t explain your analysis to a 5 year old, then you’ll have a tough time selling it to others. The focus for product data analysis is not the analysis — don’t get me wrong, you need the analysis, but it’s the story you tell and your recommendations based on that data that really matter.

Using complex analysis that confuses will result in the exact opposite of what you want. You want to be able drive engineering behaviors and investments with your analysis. If your analysis is too opaque and engineers aren’t quickly wrapping their heads around the story you’re telling, your analysis has lost its value.

The ultimate measure of the impact of your data analysis is how engineering behaviors and investments change. Make it easy for others to change.

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Looking at more data across a broader time frame can give you more confidence in your analysis. However, a single pipeline of telemetry or logs is limited by the features being captured. Generally, a single pipeline only tells a part of the product story.

Same Analysis + Same Pipeline = Same Story

What you need is another source of data. Maybe all SQL operations are logged somewhere or maybe you have the facility to pull a sample of logs from your users. More data sources also allows you to confirm whether your story is consistent. More data will not give you more insight. More data sources will.

Shiny, new tools are fun to play with and sometimes useful, but remember the ultimate measure of the impact of your data analysis?

You want to make it easy for others to change, and change is not easy. Here are 3 things fromYour Brain at Work to keep in mind to give yourself the best shot at facilitating change:

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Unless you’re recommending the adoption of a new tool, the focus should not be on the tools, it’s on the core message of your story.

Indicators are your key performance indicators (KPI). They’ll likely come in the form of graphs, plots or tables. Your analysis doesn’t stop there. Indicators are only the first “I” of the3 I’s of data-driven engineering. Tell an insightful story around your data, and then recommend investments. You’re the agent of change, and your analysis must be infused with your insights and your recommendations for investments.

Data never comes clean. That’s why I frequently feel like a janitor. As a data janitor, I rarely trust all the data is there and in the right format. I always apply Kern’s CUSS acronym from Introduction to Probability and Statistics Using R to understand the data’s Center, Unusual features, Spread and Shape.

Knowing how the data is generated and the CUSS of the data allows you to draw better reasoned insights and investments.

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