State of Data Software Survey 2024

Despite the vast advancements in data science, the accessibility of data software is still opaque. While Excel continues to be the most popular data analysis software of the last twenty years, a growing landscape of ETL, data manipulation, and data visualization tools has created diverse workflows across a range of software. Despite this, many data pipelines and workflows are siloed, and more information is required to understand what other business intelligence software and scripting languages are used. 

This lack of visibility results in a massive underestimation of lost or wasted time, underused computing resources, and unnecessarily complex onboarding processes. In effect, businesses choose from options they are aware of, accepting the pitfalls as a cost of working with data. 

To illuminate this issue further, we surveyed 350 data analysts. Some key findings are listed below:

  • Prevalence of Excel and Power BI: Microsoft Excel remains highly popular among data analysts, used by 65% of survey respondents. Power BI is also significant, used by 46%.

  • Issues with Data Dashboards: The primary concerns with data dashboards are lack of customization (54% of respondents), missing real-time capabilities (44%), and difficulties in onboarding new employees (41%).

  • Data Software Lag: Significant delays are reported due to large data sets, with 83% of respondents experiencing delays with 1 million or more data records. This lag can extend project times significantly, often requiring 1-3 hours to convert raw data into usable insights, and even longer for some companies.

  • Necessity of Python Skills: Python is considered essential by 78% of analysts for modern data tasks, but 80% report a lack of Python skills as a significant barrier.

  • Data Visualization Challenges: Although crucial, 88% of analysts find creating compelling data visualizations challenging, impacting the overall effectiveness of data insights.

  • Importance of Large Language Models (LLMs): LLMs are viewed as important by 90% of respondents, indicating a trend toward integrating AI and machine learning in data analysis tools.

Complete the form on this page if you’d like to download a full PDF version of our data analyst survey, including all results broken out by respondent count and access to all 17 questions. 

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