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Selling your data science projects with the power of storytelling

03 June 2019 by Michael Giffney 0 Comments

They say a picture is worth a thousand words, but sometimes a good short story is worth so much more. A tale that takes you elsewhere and leaves your mind wandering is a fantastic thing. Some people can do this day in and day out - recounting a simple shopping trip can leave listeners on the edge of seats or laughing out loud. Storytellers make people want to stop and listen, they make them want to know more - and that’s who you want to be.

While you don’t need to be a Hemmingway or a George R.R. Martin, creating a compelling and engaging story will help sell your data science. Up to 50% of projects never get deployed, and that isn’t because the data isn’t any good, but because lousy storytelling leaves it limp and without purpose.

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Capturing and holding attention.

While you may think your data is the best thing since chicken and waffles, others may not be so interested. So, it’s up to you to make sure they stop and listen and realise just how good it is. To do this, you need to be familiar with four essential storyline patterns.

Create a level playing field so listeners can understand what is happening in your industry and how others are succeeding or failing.

Spend time hunting through all the relevant literature, such as journals, magazines, online articles and blogs, and even gartner.com to look at existing projects within your industry. Then you can thoroughly assess what others have done, and record the analytics models, algorithms and software used, along with the implementation scenarios. By logging all of this, you have a story to tell about the pitfalls, costs and risk factors and can better defend your project.

Tell a story that’s unique and comes directly from data exploration or through deep conceptualisation or your businesses’ mechanics.

Sometimes you may be unsure on the story you want to tell, but if you look hard enough, you’ll realise the data is going to do the talking for you. Overly sophisticated ML algorithms or advanced analytics may seem the way to go, but often the answer you’re looking for requires a more straightforward and more creative solution. Don’t forget that what you may find boring or too dull, can often be exactly what key stakeholders are looking for.

Hero the project by breaking down how it can save on costs, increase revenue, and lower the number of complaints.

While this is an excellent story to tell, it can be a difficult one and can’t always be done. Start by looking for a change in current business policy. This may be to control unfavourable events such as churn or process failures or to promote a positive change such as sales or renewal. Then apply scenario thinking to an aspect such as direct or indirect costs, revenue/profit changes, the costs and effectiveness of preventative actions, or other long-term, softer compound effects.

Build a working prototype and prove its value with a side-by-side comparison.

Should a project receive enough initial funding to support a prototype, or should you happen to build one because you believe in it, you can use its data to influence key stakeholders. While this may seem simple, this can be an expensive story to tell and may require significant expertise to perfect your prototype.

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