The results are in: Integration is an integral element of data analysis tools
To highlight some of the current issues surrounding data analysis and management, I turned to various data professionals on Quora to answer this very important question: What Do Data Analysts Need Most from Their Data Analysis Tools?
Here’s what a few of them had to say about key data analysis tool features:
“Integration. A tool will only get into our workflow if it doesn’t disrupt it. Secondly, iteration and review is a big part of our workflow. One of the things that is rarely addressed in discussions about data analysis is the amount of follow up questions that pop up. If I can’t iterate on any given step of the process, I’m wasting valuable time redoing work. This involves not only the ability to code but also to keep the code in repositories.”
“Information about data quality. The ability to identify the source of pattern fluctuations and the source (I.e. Market vs system impacts). This saves gobs of time researching the source of an unexpected dip, spike or shift in data populations.”
1) Integration with existing popular software – If you’re developing a new data analysis tool, find out what the existing popular data analysis tools are (e.g., popular data analysis packages in R or Python) and closely integrate your tool with those packages. I don’t want to have to completely rework my normal workflow to integrate a new data analysis tool.
2) Useable on the command line and/or in scripts – Any good data analyst does their analysis on the command line with programming languages and scripts. If a new data analysis tool is only useable through a GUI, then I probably won’t use it.
3) Open source – I need to be able to double-check the code underlying the tool, especially if I think there’s a bug. If I can’t check the underlying code, then I’m probably not going to use the new data analysis tool.
- Nasim Farsi, Data Scientist in Social Network Platforms:
1) Performance: As an analyst I would definitely need a reasonable performance to be able to remain focused on my experiments and analysis.
2) Built-in anomaly detection: An analytics tool that comes with intelligent anomaly detection would really save most data users’ time significantly.
3) Out-of-the-box integration: A tool that could connect to typical datastores and be ready to use within seconds is the winner.
You can check out these and more responses on Quora. I also invite you to share your thoughts and your own ideas around this topic, in the comments section below.
We want to hear from you: What data analytics tools features are missing/crucial for getting the job done? Share your comments with our readers below.
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