What is data history? Ingredients, benefits and examples

In today’s data-driven world, data storytelling is increasingly important to decision-making and business growth. Data analytics roles such as market research analyst, financial analyst, and operations research analyst are becoming more prevalent as companies realize the importance of data-driven insights.

According to the US BLS Occupational Outlook Handbook 2021-2031, these job roles are experiencing significant growth;

These analysts use a variety of data storytelling techniques to perform effective analytical operations. Let’s discuss what data storytelling is, its key components and benefits, and if you’re an analyst, how can you get better at data storytelling?

What is data history?

Data storytelling involves analyzing data using visual and compelling stories to communicate data insights to stakeholders. A data storyteller explains the “why” of data using a visualization. The goal is to clearly explain the attributes of the data and provide a meaningful context for the representation of that data. Effective decision-making requires the presentation of insights behind data and trends.

For example, a financial analyst may show investors a candlestick chart to show the price movement of a stock or asset. A candlestick chart visualizes the historical patterns of a stock using four trading indicators (“open price”, “close price”, “high price” and “low price”) to predict the upcoming market trend.

A candlestick illustration showing a bullish and bearish price trend.

A candlestick illustration showing a bullish and bearish price trend. Wikimedia Commons

Take a look at the Bitcoin price candlestick chart below for a better understanding. The chart depicts Bitcoin prices for the first two months of 2023. Green lines represent an uptrend in price, while red lines represent a downtrend in the price of Bitcoin.

Bitcoin Candlestick Chart January-February 2023

Bitcoin Candlestick Chart January-February 2023

An important aspect of data storytelling is that data storytellers need to understand the business context and stakeholder requirements. Research shows that 60% of investments in data analytics are wasted because the resulting insights don’t align with decision-making and business goals. As a result, decision makers use only 22% of the data they receive.

The 3 main components of data storytelling

Data, visuals, and narrative are the three main components of data storytelling. Let’s explore them below.

  1. Data: Data storytellers collect and pre-process the data needed to tell a story. They perform statistical analysis and visualize key trends and patterns for thorough data analysis.
  2. Story. Creating a compelling story and providing context to key findings from data is called narrative. A good story inspires the audience to take action.

Thomas: H. DavenportA business management thought leader says:

“Narrative is the way we simplify and make sense of a complex world. It provides context, insight, interpretation. all the things that make data meaningful and analysis more relevant and interesting.”

  1. Visual images. A picture is worth 1000 words. Visualization adds weight to the narrative and creates a compelling data story. Visuals can be in the form of graphs, images or videos.

A data analyst can use data storytelling frameworks such as characters, setting, conflict, and resolution to tell a compelling story. For example, in an e-commerce domain, the characters might be customers, the setting might be a company struggling with customer retention, the conflict might be an increasing churn rate, and the solution might be a set of steps the data narrator suggests to reduce the churn rate.

How can a data analyst become better at data storytelling?

Understand your audience

Understanding your audience is the key to compelling data storytelling. If you’re talking to business leaders, it’s important to provide them with high-level analytics and actionable insights for business strategy. But when talking to the team, it is necessary to explain in detail what methods were used to reach the conclusion.

Choose relevant insights

Data visualization highlights different aspects of the data such as:

  • Comparison (Line Chart, Bar Chart)
  • Relationships (scatter chart, bubble chart)
  • Distribution (histogram, scatter plots)
  • Layout (Waterfall Chart, Stacked Area Chart)

Understand what you are trying to achieve with the data and how many variables to consider. Choose the best visualization to convey your idea.

Avoid clutter

Clutter the visualization by collecting or removing unwanted information. For example, in the charts below, WGM, WIM, WCM, and WFM are the leading women in chess; the rest of the data can be aggregated as “other”.

Unnecessary FIDE title labels on the x-axis

Unnecessary FIDE title labels on the x-axis

Easy to read aggregate chart

Easy to read aggregate chart

Use bright colors

Use color palettes that are accessible to everyone, including those who are visually impaired or blind. Maintain color contrast and avoid using the same colors next to each other. For example, in the charts below, the color combination in the first chart may be difficult to distinguish compared to the second chart.

It is difficult to interpret the chart because of the color combination

It is difficult to interpret the chart because of the color combination

It is easy to interpret the chart thanks to the color combination

It is easy to interpret the chart thanks to the color combination

What are the benefits of data storytelling for organizations?

Promotes data literacy among employees

Data storytelling can increase the data literacy of an organization’s employees. According to a survey conducted by Accenture and Qlik, only 21% of employees feel confident in reading, analyzing and discussing data. Therefore, a compelling data story encourages them to explore and discuss data within the organization.

Create engaging and valuable experiences for all stakeholders

Understanding and capturing the audience’s attention is essential to effective communication. The human brain processes visuals 60,000 times faster than text, and people remember stories 22 times faster than facts. Therefore, telling data stories to your product users or stakeholders using compelling storytelling and visualization can be very engaging and valuable.

Influence on decision making

Compelling data storytelling provides a new perspective or reveals hidden aspects. It conveys what needs to be done. It allows stakeholders to make informed decisions and take action on their business strategy.

Data Stories – A Breakthrough for Data Analysts

Data storytelling is the art and science of communicating insights about data. As data continues to grow exponentially and become more complex, data-driven storytelling becomes an essential skill.

The role of data storytellers in an organization is played by data analysts or data engineers. Tools like Tableau and PowerBI enable data analysts to create compelling visualizations and dashboards without much effort. In fact, Gartner estimates that by 2025, most data stories will be automatically generated.

Data analysts need to stay in touch with the latest trends and tools in the field of data analytics to tell compelling data stories. For more AI-related content, visit unite.ai.

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