Why and How your data should tell stories?

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In the times of big data, according to Forbes only 0.5 of all data is ever analysed and used. That shows the potential of growth! Every small and big enterprises need to analyse their data. However, data alone after analysing doesn’t always make sense to business people.So, how can we make clear sense out of analysed data? Stories can help greatly in doing this.

Why we need stories for data?

Stories connect our intelligence and emotions

By placing the right context, we can frame the right story of your data to help you make more sense of your data. Statistics in the form of spreadsheets and charts with lots of data points are hard to interpret. Without setting the right context, it is hard to make decisions based on the data. Looking underneath the data to set the right context can help you build stories that can connect our intelligence with emotions.

It bridges between the data and the decision maker

Stories help us humanize the data, so we can highlight what is significant. Since the beginning of human race, we have been making our decisions after focusing on significance; and we are more confident making decisions that are backed by data. Data storytelling connects the dots between data and the decision makers. Big data storytelling driven by agility can disclose facts in timely manner.

Best stories can be formed only after following

Remove the noise

Unfathomable volume of data from variety of sources brings a lot of noise along that requires cleansing to comprehend what is going on. By asking the right questions, we need to focus on only what is needed and thus, removing the rest of the noise.

Be aware of misleading interpretation

Too much data can result into incomprehensible or misleadings. Selecting the right visual presentation and color can draw user’s attention. The real challenge comes while dealing with big data that focuses on exploratory analysis, as your interpretations are open ended. However, such analysis require deep domain knowledge for interpretation. Thus, data scientists must team up with domain experts to frame the right story. You can’t expect a single person with expertise in understanding aviation parameters as well as drug analysis data!

We would like to share some great data stories shared by Ben Wellington here.

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Krishna Meet

Krishna Meet is a software scientist having core interest in analytical dashboards. Majority of her career span was into tech-writing and UX-design. However, she thrives by intersecting multiple skill-sets : SQL & NOSQL databases, business analysis, and UX design. She is a voracious reader and possesses Masters degree in Computer Science. Her interest in agile methodologies and user-centered design has landed her a techno-functional role at Brevitaz.

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