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From Numbers to Insights: Three Steps to Make Learning Data Actionable

Published Thu Dec 19 2019

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In a world where Venmo processes $51,892 in transactions every sixty seconds and that same minute means almost 350,000 scrolls on Instagram, there’s no shortage of data. What is apparent is a shortage of the ability to turn that data into something meaningful, like an insight you can act upon or a decision you can make.

In a world where Venmo processes $51,892 in transactions every sixty seconds and that same minute means almost 350,000 scrolls on Instagram, there’s no shortage of data. What is apparent is a shortage of the ability to turn that data into something meaningful, like an insight you can act upon or a decision you can make.

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This is as true for learning and development data as it is anywhere else. Data is a tool, and like any tool, it’s only as good as its user. So our team has put together three steps to get hands-on with your learning data and start using it to show business impact and drive decision making.

This is as true for learning and development data as it is anywhere else. Data is a tool, and like any tool, it’s only as good as its user. So our team has put together three steps to get hands-on with your learning data and start using it to show business impact and drive decision making.

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Step 1: Cozy Up to Your Learning Data

Step 1: Cozy Up to Your Learning Data

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Get to know your data by spending some time in it. This will do two things: get you comfortable with the tools you have and create an understanding of the available data.

Get to know your data by spending some time in it. This will do two things: get you comfortable with the tools you have and create an understanding of the available data.

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Here are some examples of ways you can familiarize yourself with your data:

Here are some examples of ways you can familiarize yourself with your data:

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    Put your data in one place, like a business intelligence (BI) tool or even an Excel spreadsheet.

    Put your data in one place, like a business intelligence (BI) tool or even an Excel spreadsheet.

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    Explore the analytics and dashboard pages within your learning tools then try to recreate some of those graphs in your BI tool or Excel.

    Explore the analytics and dashboard pages within your learning tools then try to recreate some of those graphs in your BI tool or Excel.

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    Try to answer some basic questions by building graphs or pivot tables. (Examples: During what month over the last year did people learn the most often? What content items are the most popular with your learners?)

    Try to answer some basic questions by building graphs or pivot tables. (Examples: During what month over the last year did people learn the most often? What content items are the most popular with your learners?)

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Step 2: Work Backward From an Action

Step 2: Work Backward From an Action

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A life coach named Rob Liano once said, “If you don't know what you want, you'll never find it.” We’re not here to talk about existential life lessons, but we do know that statement is definitely true for data.

A life coach named Rob Liano once said, “If you don't know what you want, you'll never find it.” We’re not here to talk about existential life lessons, but we do know that statement is definitely true for data.

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Data exploration, a process where you jump into analysis with no specific questions in mind, is a great tool. But that’s generally better left for someone whose job is analysis and will have the necessary time and tools at their disposal. Start any data analysis by defining an insight you want to draw or a hypothesis for what you’ll find and then set out on the most direct path to find those insights or prove/disprove that hypothesis.

Data exploration, a process where you jump into analysis with no specific questions in mind, is a great tool. But that’s generally better left for someone whose job is analysis and will have the necessary time and tools at their disposal. Start any data analysis by defining an insight you want to draw or a hypothesis for what you’ll find and then set out on the most direct path to find those insights or prove/disprove that hypothesis.

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Step 3: Think Like a Data Designer

Step 3: Think Like a Data Designer

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When you’re ready to take action on data, you may want or need to share the reasoning behind your decision with others. Data visualization designers communicate data for a living, and they have great rules of thumb for this. Here are a few to keep in mind:

When you’re ready to take action on data, you may want or need to share the reasoning behind your decision with others. Data visualization designers communicate data for a living, and they have great rules of thumb for this. Here are a few to keep in mind:

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    Avoid tables and, instead, use charts whenever possible.

    Avoid tables and, instead, use charts whenever possible.

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    Give your charts titles that highlight the takeaways viewers should get from them for quick, succinct communication and clear value.

    Give your charts titles that highlight the takeaways viewers should get from them for quick, succinct communication and clear value.

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    Stay away from “no-no” graphs that are generally hard to understand, such as pie charts, donut charts, word clouds, and bubble graphs.

    Stay away from “no-no” graphs that are generally hard to understand, such as pie charts, donut charts, word clouds, and bubble graphs.

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If you’re interested in learning more, Storytelling with Data , a fantastic book on the topic, is a great place to start.

If you’re interested in learning more, Storytelling with Data, a fantastic book on the topic, is a great place to start.

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Data is a commodity, and knowing how to work with data is a skill. By increasing your understanding of the data you have, using pointed questions to drive your analysis, and applying good data visualization techniques when you’re sharing your learnings, you’ll be able to take action on your learning data and see significant results.

Data is a commodity, and knowing how to work with data is a skill. By increasing your understanding of the data you have, using pointed questions to drive your analysis, and applying good data visualization techniques when you’re sharing your learnings, you’ll be able to take action on your learning data and see significant results.

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