Data Storytelling : Enough from me, lets talk to Data dealers! Part 5 of 5 Data Storytelling

This blog is the fifth blog of my Blog Series on Data Storytelling from Data Dealers.

Who are data dealers?

People whose job day in and out is to work with data. I have coined a term for these people, Data Dealers because no one really knows better then them what it is to deal with overwhelming data and how best to make sense of it.

You can read the previous stories by clicking on the links below

First Story, Second Story, Third Story, Fourth Story

In this blog I will cover how a data dealer has learnt to use metaphors to make things easy to understand

Question: Tell us about a time when you have achieved a positive outcome due to effective data storytelling?

What really works for me all the time is using day to day examples or metaphors to bring my point across.

Let me give you some examples.

When I am data storytelling I will always say. I don’t intend to data puke today but give you an insight with an attached action that will lead to better ranking.

Now, the word puke, we all know, its visual and something that makes you go, yuck. It evokes a reaction from the audience and they all get what puke is. You don’t need to be experienced in a certain discipline to understand what puke is.

I work for a company that designs software solutions for the best costumer experience. As a part of what we do, after we have designed the solution we work with our clients to train their staff on how to use these newly developed software solutions.

Needless to say, the staff we are training are mostly unhappy and demotivated because we are asking them to stop doing what they are used to doing and do something completely new which is what we call – CHANGE.

Lack of motivation is always  a road block in successful adoption of the software solutions.

So, I decided to use the data from our previous experiences to see how long it takes for the staff to build competence ( which means I have the knowledge to use the system) and how long does it take to become efficient ( which means not only do I have the knowledge but I am also able to apply it in various scenarios)

Based on the data we discovered it takes 4 weeks to gain knowledge and around 6 months to build efficiency.

Now, if I just shared this data insight .. It would not have much impact. It will sound something like this

“We have studied the data of having successfully implemented our software solution in other business likes yours and gained an in insight that it takes 4 weeks to gain knowledge and 6 months to build efficiency for the staff on this newly implemented software solution. “

This doesn’t do very much for me or for my audience.

So, I decided to use a day to day life metaphor and made it easy for people to understand this

It went something like this

“One of the most joyous moments for any couple is when the are about to become parents. Now, from the time a couple gets to know that they are about to have a baby to the time they have a baby – its 9 months. In those 9 months we do everything we can to ensure the baby is well. The interesting thing about this process is that we are aware that it will take 9 months for the baby to come along and we never once question that

Why don’t we question that?

Because we are informed and we know what to expect. Most of the anxiety in any change comes from the uncertainty/ unknown.

As a rule, humans prefer certainty to uncertainty. Studies have proved that uncertainty/ unknown causes anxiety  

Similarly for this technology change to work and for you to gain efficiency with it will take about 6 months. This I can prove based on the data we have gathered of having executed the same change with other clients.

We are informing you and working with you to ensure we stay persistent and motivated for the next 6 months to see benefits of the  change to come to life

So, What should you do?

Try using day to day life examples to bring your insights to life. People connect to the day to day life experiences way more than insights alone.

What we are really doing here is to take the data insights we want to share and package them. It allows us to take the dryness of the data away and make a connection with our audience.

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