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HR Analytics and Big Data @ Work
Articles, research and reports on HR Analytics, Big Data and related topics
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The Value of Big Data Isn't the Data

The Value of Big Data Isn't the Data | HR Analytics and Big Data @ Work | Scoop.it
To make the data useful, you need to turn it into a narrative. To do that at scale, you need machines.
Tony Brugman (Bright & Company)'s insight:

The other great HBR Blog post on Big Data and building good narratives is 'The Value of Big Data Isn't the Data'. The title contains the message of the article. The author argues that data alone won't be of any benefit for us humans.

 

In order to capitalize on Big Data, "we will need systems that not only perform data analysis, but then also communicate the results that they find in a clear, concise narrative form." The author proposes a workflow model from interpreting the data to generating a narrative from the data. 

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Analytics: Anecdotes and Storytelling | Talent Management Blog

Analytics: Anecdotes and Storytelling | Talent Management Blog | HR Analytics and Big Data @ Work | Scoop.it

"Last week I presented you with the human capital analytics continuum. This continuum is a look at how organizations collect and report data."

Tony Brugman (Bright & Company)'s insight:

Another short, simple and accurate column by Gene Pease on why there is more than just numbers/statistics/analytics.

 

Pease:

"We have found delivering analysis findings in a story format to HR executives is received much more clearly than laying out hard numbers. I encourage you to build a story around your numbers and deliver it with ease."

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How to Tell a Story with Data

How to Tell a Story with Data | HR Analytics and Big Data @ Work | Scoop.it
Five rules for doing it right.
Tony Brugman (Bright & Company)'s insight:

Two great HBR posts in the same week on benefiting from big data while using good narratives. The first is about 'How to Tell a Story with Data' and zooms in onto the visualization of data. "So how does a visual designer tell a story with a visualization? The analysis has to find the story that the data supports."

 

The author offers five principles:

1. Find the compelling narrative
2. Think about your audience
3. Be objective and offer balance (devoid of bias)
4. Don't Censor
5. Finally, Edit, Edit, Edit

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