# From data to story

While some users may want raw data (actual figures that can be used to create bespoke graphs and charts for example), others, particularly non-analysts, those with limited analytical skills or those with limited time for analysis, may want headlines, insights or stories, alongside (or instead of) actual raw data. 

### From data to story

Turning raw data into something we can use to make a decision or form an opinion means deciding what to show, what to emphasise, and what to filter out.

* Data = a number, statistic or measure (quantitative); a fact, an opinion or experience (qualitative) 
* Metric = data with specific parameters, type of measurement
* Information = metrics with context
* Insight = information with implications
* Story = connected insights

<figure><img src="https://242954767-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fpb6xDffhgi9f80ZORaUG%2Fuploads%2FPGpLIlz7X4vUAlZGITqL%2FODI-FromDataToStory.png?alt=media&#x26;token=20bc6313-fef4-49ac-b418-4b450be88a1f" alt=""><figcaption></figcaption></figure>

The value of data increases as it turns into a story, because a story helps connect the dots between and paint a picture with single units of data. Those who work with data must be able to communicate why it is important or useful, otherwise the value is lost.

{% hint style="info" %}
For more information about developing data stories, try this: a four step approach to [storytelling with data](https://findingstories.learndata.info/#/id/57b1b369e396ef981f39ea28).
{% endhint %}