1. Impacting the decision-making process
Well done! We've discussed data storytelling and translating results for non-technical stakeholders.
2. Data storytelling
Particularly, we mentioned that a strong data story has three elements: the data, the narrative, and the visuals.
3. Compelling narrative
Let's focus on the narrative. After we've found relevant insights in our data,
we have to find a meaningful way to present them to our target audience,
using a narrative that includes only key points
and helps us drive change. In other words, we need a compelling narrative or
a description of connected events that organizes information to engage the audience and make them care for the results or information shared.
4. Narrative structure
This might sound challenging at first, but following a narrative structure will help. There are several narrative structures that are out of the scope of this course, but it all comes down to a structural framework guiding how we present our story.
Imagine that as part of the job at communicatb, we are presenting results to a food company.
To start our data story, we should mention the background:
details about what problem motivated the analysis, what changed in the
previous situation that warranted an analysis,
and who the analysis is focusing on. Are we analyzing data from customers? From employees? Or something else?.
In our case, we mention that opposite to management expectations the company's total profits have decreased in the last three quarters.
5. Narrative structure
After the problem was introduced, we
should provide evidence of the factors that contributed to the problem.
But we should only include relevant information and not detailed data that can overload the audience.
We analyzed the data and found that the chips sales have increased by 20%. However, sweet item sales have declined overall by 30%.
6. Narrative structure
We can also
provide more supporting evidence and data,
as long as it helps explain on a deeper level the cause of the problem.
So after taking a deeper look, we saw that the company's most popular chocolate has seen its sales decreased by 50%.
7. Narrative structure
All the evidence should lead to the climax:
the moment when we introduce the central finding of our analysis. It should
state clearly what could happen if nothing changes.
We predicted that if the company does not make a decision, it will lose 10 million dollars next year.
8. Narrative structure
After the main finding is revealed,
we should finish exploring potential solutions and opportunities,
by recommending a course of action to take.
We need to be proactive and guide the audience through understanding what to do with our results if we want to impact the decision making process.
So according to our predictions, rebranding the chocolate and offering special discounts can achieve a 20% profit.
With these steps, we created a narrative that will catch our audience's interest, and is easy to follow.
9. Building narrative
Running an analysis, there are several things you might look at, which will in turn drive our narrative.
We can explore how a feature changes over time or the time cycle patterns, for example, how chocolate sales are lower in summer but higher in winter.
we could focus on how two things are related to each other, for example chocolate price and the customer rating,
or display similarities and differences between chocolate consumption between adults and children.
Also, we can find groups in the data, such as high chocolate and high coffee consumers vs low chocolate and low coffee consumers. As we craft our narrative, we need to make sure that these data points make a relevant and relatable story.
10. Let's practice!
OK, now we understand more about narrative structure, let's put these concepts into practice.