Telling Tales: Niche of a Data Scientist!

“We are, as a species, addicted to story”
-John Gottschall
Author of The Storytelling Animal: How Stories Make Us Human

A Data Scientist Knows the Fact that Stories Sell!

Since time immemorial, storytelling has been an integral constituent of our cultures, and henceforth, of our being. We retain stories more than we understand and remember facts and figures. An ambitious hero progressing expeditiously towards his goal will always leave a lasting impact on the listeners, as compared to a dull and drab story about a layman wandering aimlessly without any significant goal in hand. A linear story with a protagonist, a quest motif, a resolution, preferably a positive outcome, is always cherished, remembered, and followed.
An American proverb says, “Tell me the facts and I’ll learn. Tell me the truth and I’ll believe. But tell me a story and it will live in heart forever.”

Facts are Dull, Stories are Interesting!

Does it mean that Data Scientists are aware of our natural inclination and inborn affinity with stories? Probably yes! Analytics data, usually tagged as dull and boring, fails to seep into our minds to create a long-lasting impact. For a Data Scientist, the art of weaving facts and figures into a soulful narrative is a must-have skill. The facts transformed into a narrative will take all the controls – it will communicate data analytics to non-analytical people, narrative along with visual analytics will make analytical data look impressive, it will persuade people into meaningful actions, it will generate goal-oriented activities, and last but not the least, it will motivate people in achieving their final goal.

Wisdom Loaded Stories Work Wonders!

Even the folk tales of all the cultures across the globe have some morals to teach. Essentially didactic in nature, stories teach us, motivate us, and even guide us to find the right directions or ways of functioning. This kind of wisdom is expected out of a Data Scientist, who should know how to impart knowledge, accumulated through diverse experiences. Presumably, a Data Scientist possesses great analytical abilities, but he should couple his abilities with level-headed maturity and considerable insights, in order to tell a great story that communicates, persuades, and works wonders.

Will Negative Stories leave a Negative Impact?

It is true that stories can be both negative and positive. For a Data Scientist, the ultimate goal of storytelling remains the communication of analytical data. To this end, positive stories are powerful, and negative stories can be even more powerful! Where positive stories tell about what went right, negative narratives can tell people about “what to avoid” or “what went horribly wrong” such as which course of action proved disastrous for an organization, which elements altered the smooth functioning of processes, how ambiguous policies led to failures, and so on. Such a story can then become an elaborate piece of information which not only tells one about the ultimate goals, the desired end, the process to be followed, but also the other significant details about the anticipated loopholes and impending dangers. Surely, a smart way to communicate!

Myriad ways of Telling Tales

Someone has rightly said, “Storytelling is the mother of all ‘pull’ marketing strategies. It encourages dialogue, engagement and interaction among equals – an exchange of meaning between people.”  As a matter of fact, the first and the foremost story is “the story before the story” – a story that springs from an idea. There will be no investments in data science projects, if there is no convincing story woven strategically and aesthetically around an idea or a concept. Every data science project begins with no data in hand. There are only ideas, and a story about the idea. The idea and the allied story lead to the actual implementation and data collections, followed by extensive data analytics, and finally paving way for another set ofdata-driven stories.

These data-driven stories may have at their hearts data from past and present. Analytical stories that center around events, patterns, and other aspects from the past, are usually termed as Reporting stories. While on the other hand,stories may also originate from the surveys done primarily to have an insight into the latest trends in varied sectors such as finance, healthcare, anthropology, Human Resources, Business, and so on. These stories are descriptive in their nature, and throw light on the present scenario. However, bothanalytical stories from the past, and descriptive stories from the present pave way for Predictive Analysis, in which a Data Scientist, based on some assumptions and probability, predicts the future activities or patterns.

Not limited to this, Data-driven stories have multiple manifestations. “What-Stories” and “Why-Stories” are equally important because these stories entail detailed analysis of the concerned event and of the underlying causes. For instance, an objective reporting about a sudden rise in the online shopping, can be termed as a “What-Story” and the detailed analysis of why this happened, would provide crux to the “Why-Story.” Causation, in this way, is central to data analysis. 

Data Scientists analyse volumes of data to find out the cause and effect relationship among multiple variables. They also seek if there is any correlation in the variables- if rise in one variable led to the rise in the other, or vice-versa.

Data-driven tales are central to Data Analytics, in the same way, as these are to the profile of a Data Scientist. A Data Scientist has to have the storytelling ability – the ability which will make his words interesting to listen to, and meaningful enough to think over. The profile of a Data Scientist is considered to be the most “sought-after” profile, who knows storytelling abilities may have added to the charm. Not only storytelling, but also other attributes such as knowledge and wisdom play key role in the career of a Data Scientist. Such a skill set comes after getting trained in the niche skill.

ETLhive organises comprehensive lectures on Data Science, during which the highly-qualified industry-experienced training Professionals at ETLhive impart knowledge on varied concepts and skills associated with Data Science. At ETLhive, you will go extensive training with hands on experiences in Data Science and Machine Learning, Data Manipulation using R, Machine Learning Techniques Using R, Supervised Learning Techniques and the implementation of various Algorithms, Unsupervised Machine Learning Techniques – Implementation of different algorithms, Regression Methods for Forecasting Numeric Data, and Deep Learning – Neural Networks and Support Vector Machines. Get trained at ETLhive and get hired for the hottest job of the century – a Data Scientist!