Storytelling is data with a soul. Data Scientists are extremely good with numbers but numbers alone are not sufficient to convey the results to the end user. Being a good data storyteller is an art as well as a science. Data Scientists take the help of various data visualization tools like Tableau to present the data in visually appealing format. A Data Scientist not only understands the data but also understands the business and the end user very well. A good data storyteller is as essential to a business as a data scientist. Because a good “Data Storyteller” will ensure that the results from the data analysis and modelling gets imparted to the right audience in an understandable format.
Joseph Rudyard Kipling, famous short-story writer and poet once said “If history were taught in the form of stories, it would never be forgotten." The same thought applies to data analysis. Organizations should understand that data can be remembered only if it is presented in the right way. A graph, bar chart, pie chart, spreadsheet or a slide is usually how data is presented, but a story is so much more effective for conveying the message. When data and stories are used in combination, they reverberate with customers on both emotional and intellectual levels. A data scientist’s end goal is to see that actions are taken on the insights that he/she has drawn from the data. This can only happen if he/she can present the data in the form of a story to the end users who will be working on solving the business problem.
Shawn Callahan, world’s leading business storytelling consultant rightly said- “The best stories contain data. To think “on the one hand is the story” and “on the other hand is the data” is just wrong headed. Now we need to help scientists find and tell the stories that bring their data to life.”
Harvard Business Review Blog Network published a blog titled –“Data is Worthless if you don’t communicate it.”
Harvard Business Review blog network published a content piece –“A Data Scientist’s Real Job: Storytelling” by Jeff Bladt and Bob Filbin from DoSomething.org. The essence of the article was straightforward-data and analysis alone is not enough, for people to understand the analysis and make sense of it, storytelling with data can make a difference.
75% of the human population considers data to be dry and boring, however everybody loves a good story. Today’s data science job roles do not merely need graduate or post-graduate degrees but they need personnel who can become expert designers, programmers and data storytellers-these are three different disciplines connected by a common strand of statistics.
How to tell a story with data?
Let’s take one of the data storytelling examples. This is one of the projects that our current Data Science in Python students are working on.
Business Problem: The beer companies want to find out which beer is most preferred among the people and they want to see – if they can convince the people to change their preferred beer or brand.
The data that the beer companies will have will look something like this:
Now if the data scientist has to run analysis on say – something like – If one group prefers the beer ‘Coors Light’ and based on the reviews on aroma, taste and palate, we will find out what is the nearest possible beer they will choose to drink. The output that will come up after they run their analysis in Python, will look something like this:
If this same data is now presented to the bartenders or to the distributors of this beer – it is hardly likely that they will understand what needs to be done to solve the business problem. The data scientist has to present the same output in the form of a story, that everyone will understand. Suppose he/she says – “You will notice a pattern among people from an Irish background, will always prefer to drink stronger beers and would not even review a light beer. Their taste, palate, and everything else indicates that light beer will not be well accepted.”
This kind of an analysis would resonate better with the end user – i.e. the distributors, bartenders and the beer companies and allow them to take a business decision that will ultimately lead them to more profits.
As big data continues to get more complex and evolves at a rapid pace, big data companies are looking forward to find easy answers to a business analytic problem by making sense of all the data they have. Storytelling with data and story writing are two sides of a coin. Big data companies need data scientists who merely don’t write their stories but also know how to tell a story with data. There is an increased demand for data scientists who can transform complex graphs and charts into data driven storytelling insights.
Who is a data storyteller?
A data storyteller is an expert who reviews the data available in the forms of graphs, charts or any other visual representation, processes the information from the reviewed data to comprehend what it means to a particular industry, organization or brand and then delivers insights in the form of a story. A data storyteller discovers the most convincing viewpoint to guide its users towards making a logical decision that is supported with proof and not just derived expert opinion. Storytelling with Data not merely requires understanding of various advanced statistical concepts but also requires understanding various people.
Why do you need data storytellers?
Making the most of big data requires human context and translation irrespective of whether it is for employees of an organization, stakeholders or customers the business is trying to reach. Data will only confuse people if it does not contain any human frame like words, photos or visuals, and indeed will not lead to smart organizational behaviour. Professionals who love data and analytics must also need to be good storytellers and here’s why-
- Stories that consist of data and analysis are effective tools to convey human behaviour. To make sense of the data from complex world, data storytellers who can narrate experiences that provide insight, interpretation and relevant context to make data analytics more relevant and interesting are in demand amongst various organizations.
- Most of the people in an organization cannot understand the outcome of analytics, however they do need the proof of analysis and data. Data storytellers incorporate data and analytics in a compelling way as their stories involve real people and organizations.
- Data storytellers help an organization to figure out the various types of stories that can be told with data –reporting stories, explanatory stories, predictive stories, causation stories, correlation stories, etc.
- The main goal of big data analytics is to influence how someone takes an action or makes a decision. Regardless of how effective and impressive the analysis is, you cannot convince your stakeholders for a change unless they understand what insights you have gathered. Thus, a data storyteller will create a narrative or visual story for the outcome of analytics that can persuade and inspire trust amongst the various stakeholders of a business.
- It is boring and time consuming for stakeholders to look at all the quantitative analysis. The quantitative analysis of a problem would be ambiguous to someone who does not have a data science background and hence it will be difficult for them to understand the insights from the data. A data storyteller can communicate the findings of the analysis in a sharp and brief manner in the form of a story.
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Data Scientists Should be Great Storytellers
Storytelling with data is the next evolution of data visualization for data scientists that fits into a broader landscape of data exploration, presentation and visualization. Data science in reality embraces the use of experimentation to test any story that needs to be told with data. Data scientists are generally condemned for not being able to communicate with the masses effectively to inspire them into action. Thus, they need to take some lessons from the storytellers to tell a great story with data science.
The job of a data scientist is to be the mouthpiece of the story that every data set tells. Data science discipline has put an end to the days when analysts used to print reports with various numerical columns to provide meaningful data-driven business insights. Data has all the answers for a data scientist but the most difficult and challenging task of a data scientists is to ask questions of the data, create directives from the data and tell a relevant story.
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For people who do not have much patience for analytics, storytelling can have great influence as it presents the right information in the right format. A statement like –“100 people in US will die prematurely in the next 24 hours due to flu outbreak.” is likely to capture anyone’s attention rather than presenting the same statement through graphs showing death trends in the previous years due to flu outbreak and predicting the future death rate because of flu outbreak. It is easy to convince people who are on the verge of decision making through the right data but it requires storytelling and not just crunching numbers.
For a company’s data science equation to be successful, data scientists need to tell a predominant business story that helps organizations solve problems. One of the most critical tasks of a data scientist is to make the best of data to create compelling stories. Data scientists can use various visualization tools and spreadsheets to support the outcome of their analysis, but the real value of a data scientist lies in their ability to convey the results of analysis into narrative experiences which can be helpful for both external and internal communication within the business organizations. A great enterprise data scientist brings the propensity of storytelling to meet the challenges of an organization that can help him prove and improve the marketing value of the organization to achieve brand reputation.
How a data scientist can structure the storytelling process with data?
For data scientists to create convincing and narrative stories with data- it requires them to have a clear understanding on the format for a great story and how they can add life to their story by adding various elements like information, knowledge and wisdom. To create a compelling story data scientist must –
- Understand the business problem to define the question at hand and why it matters to the business. Think of the business question as a mystery to be solved with data. Knowing the business question will help data scientists make the data story relevant to the audience.
- Data Scientists should have an in-depth understanding of the existing business environment so that he/she can effectively measure the business impact.
- Based on what data is available, data scientists must study the implications from various perspectives- from the viewpoint of customers, from the viewpoint of third parties, from the viewpoint of executives, from the viewpoint of suppliers, etc.
- Before presenting the initial solution hypothesis, data scientists must identify any roadblocks to acceptance or understanding.
- Present the business impact of the presented solution through a resolution to the story—it can relate to the future vision of the company or relate to an outcome that has already happened. A story that does not lead the audience anywhere does not make sense.
- Last but not the least, data scientists must adapt the story to fit their audience.
Storytelling with data can lead to actionable insights with focus on 3 key points-
- Data scientists should use only that data for storytelling that impact the organizations key metrics.
- For everyone to easily grasp the insights drawn from data, data scientists should use various presentation and visualization tools to find and plot data trends.
- Data science projects are recurring and thus data scientists must keep questioning the data to find answers to the unanswered questions with the help of data.
Storytelling with Data - Most Sought after Skill to become a great Enterprise Data Scientist
The target market or audience of a business might find data science boring but all that can gain traction is the analysis packed with a compelling story that can guide them where they need to be. There are not enough people to fill the job role of enterprise data scientists who can tell convincing stories using various data visualization tools. With big data revolution on the rise, data scientists who are excellent data storytellers will be most sought after. What do you think? Let us know in comments below.
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