शैडोइंग अभ्यास: Data vs. Findings vs. Insights - वीडियो के साथ अंग्रेजी बोलना सीखें

- Data findings and insights.

They may feel and are related, but they're three different things.

I'll explain those three different things in this video.

They communicate different levels of research analysis completion.

Insights represent the most complete level of research analysis.

Findings are slightly weaker and lack context, and data is completely devoid of any information or analysis.

Let's walk through these, starting with data and build up.

Data is an unanalyzed collection of individual observations about users that may include transcripts, notes, metrics, or survey output.

At this level, no analysis has been completed yet, and without analysis, we don't yet have information.

Take for instance, a usability study where we collected transcripts and a log of the things that users clicked on.

These are our data, but they don't tell us anything about what we learned collectively or what to do as a result.

This is why we don't wanna make decisions based on raw data alone.

However, when we take our data points and start to identify patterns using qualitative or quantitative analysis methods, we call those patterns findings.

Findings describe patterns in collected data, which still lack consideration of background, past research, and organizational factors.

For example, we might say that most users clicked on a contact support button on our website, but this finding still doesn't give the full story of what this looked like in the past and why we observed it.

It lacks context.

We need something with clear context for good design decision-making.

This is where insights come into play.

Insights do have this rich and necessary context, and this is why insights are the gold standard for making user experience design decisions based on research.

Insights are focused explanations of opportunities based on user research and business context.

So, if we knew most users clicked on the contact support button on our website, we might know it's because they were not able to find the information they were looking for, which drives up support volume costs.

In this scenario, the insight would be that there is an opportunity to improve the discoverability of important information to drive down support volume costs.

Context is key.

On teams, it is important to clearly distinguish whether you are referring to data, findings, or insights because this will communicate whether you have more analysis to do or not.

Once we have insights, we're ready to make good decisions.

- Thanks for watching.

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शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग अभ्यास: Data vs. Findings vs. Insights - वीडियो के साथ अंग्रेजी बोलना सीखें

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