# On the Road to Effective Data Analyses

DevFeed: [On the Road to Effective Data Analyses](<https://devfeed.tech/articles/on-the-road-to-effective-data-analyses-20039.md>)

Original publisher: [Read original article](<https://technology.doximity.com/articles/on-the-road-to-effective-data-analyses>)

Author: Doximity

Published: 2023-09-07T16:45:00Z

Content type: tutorial

Language: en

Sources: [Doximity](<https://devfeed.tech/sources/doximity.md>)

Topics: [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [coding](<https://devfeed.tech/topics/coding.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [Python](<https://devfeed.tech/topics/python.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [data](<https://devfeed.tech/tags/data.md>), [data-analyst](<https://devfeed.tech/tags/data-analyst.md>), [python](<https://devfeed.tech/tags/python.md>), [readability](<https://devfeed.tech/tags/readability.md>), [review](<https://devfeed.tech/tags/review.md>), [sql](<https://devfeed.tech/tags/sql.md>)

## AI overview

This article presents practical guidance for making data analyses more compelling and useful. It recommends breaking broad product questions into specific data questions, collaborating early to identify potential data issues, and writing readable, reviewable code using SQL common table expressions and Python functions.

## Source excerpt

In the modern business landscape, the significance of data analysis cannot be overstated. It empowers organizations to uncover insights, make informed decisions, and gain a competitive edge by deciphering the hidden patterns within vast datasets. Data analysis is the cornerstone of strategic innovation and effective decision-making in today's dynamic markets. At Doximity, data is crucial to the success of business operations, playing a key role in every step of the product development cycle. From determining the potential of new features to testing and tracking their performance, data analysts and business intelligence analysts are critical to Doximity's success. Data analysts are equipped with powerful data analysis tools. While the variety of tools makes data analysis enjoyable, it still requires practice and self-reflection to create engaging analyses that cater to their audience. Here are a few tips for making analyses more compelling and useful. Ask the right questions from your data Every data analysis begins with an open-ended product question, which must be broken down into more specific questions related to the data. Taking the time to structure the analysis with well-defined questions can prevent analysts from getting lost in unnecessary details. Brainstorming early with colleagues and product managers can provide valuable insights and help identify and avoid potential data issues that could lead to flawed findings. As an example, consider the open-ended production question - "Why is the engagement with component x of our app increasing/decreasing in the last 2 months?" Upon brainstorming, this leads to questions "closer" to the data, such as: Is this a local high/low or an all-time high/low? In particular, have we seen seasonality with this metric in recent years? Is there a competing component of the app whose engagement has gone down/up in recent times that is correlated with this component's engagement going up/down? Write readable code Although coding