Influencing Business Audiences​

Data storytelling is the practice of combining data, visuals, and narrative to create an engaging story that can influence decision-making. It has a couple of components that include data analysis, context, visualization, and insights. You need to weave these elements together to make the most of them.

Understanding the psychology behind data storytelling can significantly enhance how businesses communicate insights and persuade their audiences. This article explores key psychological principles that can elevate your data storytelling, making it more impactful and memorable.

1. The Power of Visuals in Data Storytelling​

The human brain is wired to process visual information much faster than text. Research indicates that visuals are processed in as little as 13 milliseconds​​. This rapid processing makes visual aids like charts, graphs, and infographics indispensable in data storytelling, as they help convey complex information quickly and effectively.

What’s more, visuals are not only processed quickly but are also retained better. People remember 80% of what they see and do, compared to just 20% of what they read and 10% of what they hear​. This is known as the Picture Superiority Effect, a psychological phenomenon with the idea that humans process visual stimuli more and, thus, the importance of using visuals to enhance comprehension and retention of information.

Some practical applications of this are:

1. Dashboard Reporting: Real-time dashboards provide visual summaries of key performance indicators (KPIs), helping managers make quick, informed decisions.
2. Data Presentations: Visual aids in presentations can make the data more engaging and easier to follow, which is crucial for stakeholder meetings and pitches.
3. Training and Development: Visuals can enhance learning and development programs by making the content more interactive and easier to understand.

2. Emotional Engagement Through Data Narratives​

A compelling narrative can transform dry data into an engaging story. Stories have the power to evoke emotions, making the information more relatable and memorable. Stories are up to 22 times more memorable than facts alone. To create a compelling data narrative, focus on a clear storyline, use relatable characters, and build a narrative arc that includes a beginning, middle, and end.

Why? To start, storytelling humanizes data, helping to build trust and credibility with your audience. When businesses use real-life examples and case studies, they make their data more relatable and trustworthy. For instance, showcasing how a product or service has positively impacted customers can make your data more believable and persuasive.

Here’s how businesses can achieve emotional engagement through data narratives:

1. Personalization and Relatability

   • Human Elements: Incorporate real-life stories and examples that relate to the data. Personal anecdotes and case studies can make the data more tangible and relatable to the audience.
   • Audience Connection: Tailor the narrative to resonate with the specific audience. Understand their values, interests, and concerns to make the story more compelling and relevant.

2. Visual Storytelling

   • Emotionally Engaging Visuals: Use visuals that evoke emotions. Photos, videos, and infographics can add a human touch and make abstract data more concrete.
   • Consistency and Clarity: Ensure that visuals align with the narrative and enhance understanding without overwhelming the audience​.

3. Narrative Techniques

   • Compelling Plot: Structure the data narrative with a clear beginning, middle, and end. Introduce a problem, present the data as the solution, and conclude with a call to action.
   • Emotional Triggers: Use elements like suspense, conflict, and resolution to keep the audience emotionally invested in the story.

4. Context and Impact

   • Contextual Background: Provide background information that helps the audience understand the significance of the data. Contextualizing data makes it more meaningful and impactful.
   • Real-World Impact: Highlight the real-world implications of the data. Show how the data affects people's lives, businesses, or communities to create a stronger emotional connection​.

3. The Principle of Simplicity in Data Presentation​

Simplicity is key to effective data storytelling. Overloading your audience with too much data can lead to cognitive overload, reducing their ability to absorb and retain information. Focus on presenting the most critical data points and use clear, concise visuals to convey your message.

Identify and highlight the most important insights from your data. Use techniques such as color coding and bold text to draw attention to key messages. This not only helps to maintain the audience's focus but also ensures that the main takeaways are clear and memorable.

How? Here are some key aspects you should focus on:

1. Clarity - Avoid overloading and only present the most important data. Having too much information can distract and confuse your reader.
2. Consistency - Use a uniform style and format with your fonts, colors, themes, graph types, and more.
3. Conciseness - Keep it clear and short—quickly convey the essence of your data in clear yet descriptive text and titles.
4. Minimalism - It’s the trend nowadays because it makes reading and interpreting data easier. For one, ensure you have ample white space.

4. Using Data to Drive Action​

Applying psychological principles of persuasion can enhance the effectiveness of your data storytelling. Robert Cialdini's principles of influence, such as authority, social proof, and scarcity, can be integrated into your narratives to make them more persuasive. For example, using authoritative data sources or highlighting the popularity of your product can drive your audience to take action.

Showing the tangible benefits and return on investment (ROI) through data storytelling is crucial. Businesses are more likely to be persuaded by data that clearly demonstrates the potential financial benefits. Companies using data-driven marketing are more likely to be profitable year-over-year. Including case studies and specific examples of ROI can make your data more compelling.

Here are some steps to take note of:

1. Collect data from different sources. Diversifying helps create a well-rounded perspective. For example, search for operational processes, sales transactions, and customer interactions.
2. Utilize advanced analytics techniques such as predictive analytics, machine learning, and artificial intelligence to uncover hidden patterns and insights from the data.
3. Present data in a visual format using charts, graphs, and dashboards. Visualization makes complex data more accessible and easier to understand.
4. Translate data insights into clear, actionable recommendations. Provide specific steps or strategies that can be implemented to address identified issues or leverage opportunities.
5. Invest in training and development to improve data literacy across the organization. Ensure that employees at all levels understand how to interpret data and use it to inform their decisions. Investing in education is valuable, and employees can study at their own pace with the highest paying bachelor degrees.

5. Leveraging Social Proof and Testimonials​

Social proof, such as testimonials and case studies, significantly influences decision-making. Incorporating testimonials and success stories into your data storytelling can enhance credibility and persuade your audience more effectively.

Success stories provide relatable examples that can inspire and motivate your audience. Showcasing how other businesses have successfully used your product or service can create a powerful narrative. The psychological effect of seeing others' success can drive your audience to take similar actions.

Psychology Behind Data Storytelling​

Understanding the psychology behind data storytelling is crucial for influencing business audiences. By leveraging visuals, crafting compelling narratives, simplifying data presentation, using persuasive principles, and incorporating social proof, you can enhance the impact of your data storytelling. These psychological principles not only make your data more engaging and memorable but also drive business decisions and actions. As you create your next data story, remember to focus on these key elements to maximize your influence and effectiveness.

Source: Unsplash

A Guide for Non-Technical Users​

Analyzing data is no longer just for the data team. When more people are able to analyze data within an organization, the business generally performs better. Normally, businesses perform better when more individuals can analyze data within it. Specifically, in a recent study conducted by Harvard Business Review, 87% of respondents said that their companies would be more effective if they gave their front-line workers more power with numbers.

But uncovering insights is only the beginning. The actual value comes when these findings are turned into an interesting story that motivates action and brings about tangible change. This guide will cover all the basics necessary for creating data-based narratives that are both convincing and suitable for non-technical users.

Understanding Data Storytelling​

Data storytelling is the art of combining facts and figures with narrative techniques in order to communicate important information. It consists of three essential parts, namely data, story and visuals.

Components:​

• Data: The factual backbone of your story.
• Narrative: The structured progression that guides your audience through the data.
• Visuals: Graphs using Airtable connection, charts, and images that illustrate your data and enhance comprehension.

The power of data storytelling cannot be underestimated. According to research from Stanford University, people are 22 times more likely to remember information if it’s presented in the form of a story. This method makes your data not only easier to understand but also harder to forget.

Identifying the Right Data​

Before you start telling a story, make sure that you have all the necessary information. Find out what exactly you want to achieve and who your target audience is. Also, find reliable sources of data and determine their relevance for the given context.

Purpose and Audience:​

Determine the objective of your story. Do you wish to educate, entertain, or convince? According to a Forbes article, 93% of business leaders and data professionals agree that successful data storytelling can boost revenue. Additionally, 92% of them believe that data storytelling is an effective method for communicating data and analytics results.

The most important thing here is knowing what people understand well based on their needs; this will help in developing a message that can resonate with them, thus making storytelling effective.

Finding Data:​

One must use reliable sources when gathering facts for any given narrative. Government databases like the Census Bureau, industry reports such as McKinsey, and academic journals, among others, can provide good examples in this case. Some websites like Data.gov may also be useful for finding relevant materials through searches while Google Scholar offers access to scholarly publications worldwide during the research process if you need to know more about Research.com online MLIS programs.

Evaluating Data:​

All data aren’t made the same. Evaluate all of your sources for dependability, truthfulness, and relevance. Check the date of publication to be sure the data is current and understand the methodology used to acquire it.

Structuring Your Narrative​

A compelling data story has a clear structure. Just like any good story, it should have a beginning, middle, and end.

Story Arc:​

• Beginning: Introduce the context and the main question or problem.
• Middle: Present the data, providing insights and explanations.
• End: Conclude with the implications of the data and a call to action.

Building the Framework:​

Frame your data within this structure to create a cohesive and logical flow. Start by posing a question or highlighting a problem. Use your data to explore the answers and conclude by summarizing your findings and suggesting the next steps.

Creating a Flow:​

Ensure that your narrative moves logically from one point to another. Use transitional phrases to help guide readers along and avoid jargon that may confuse those without technical knowledge.

Using Visuals to Enhance the Story​

Data storytelling relies on visualization. It helps people understand complex numbers by presenting them in digestible formats – and, more importantly, keeps them interested.

Types of Visuals:​

• Charts, Columns, and Graphs: Use AI design on columns, bar charts, line graphs, and pie charts to represent quantitative data.
• Infographics: Combine text and visuals to present data in a visually appealing way.
• Maps: Geographical data can be effectively represented with maps.

Choosing the Right Visual:​

Select visuals that best represent your data. For instance, use a line graph to show trends over time or a pie chart to display parts of a whole. Avoid clutter and keep your visuals simple and to the point.

Design Principles:​

Apply basic design principles to create clear and engaging visuals:

• Simplicity: Avoid unnecessary elements that distract from the main message.
• Consistency: Use consistent colors, fonts, and styles.
• Emphasis: Highlight key data points to draw attention.

Making the Data Relatable​

Data alone can be dry and impersonal. Make it relatable by including a human element, providing context, or playing with emotions.

Human Element:​

Include stories or examples that illustrate how the data affects real people. This helps your audience connect with the data on a personal level.

Contextualization:​

Give some background so people know why this information is relevant to them. Explain what’s at stake or how it connects with their lives.

Emotional Engagement:​

Use techniques to evoke emotions. For example, share a success story backed by data to inspire your audience or highlight a problem to spur them into action.

Tools and Resources for Non-Technical Users​

You don't need to be a data scientist to create compelling data stories. There are plenty of AI storyteller in tools available today designed for users at any level of expertise.

User-Friendly Tools:​

• Tableau: A powerful data visualization tool that's easy to use.
• Canva: Ideal for creating visually appealing infographics.
• Google Data Studio: A free tool for creating customizable reports and dashboards.

Learning Resources:​

• Online Courses: Platforms like Coursera and Udemy offer courses on data visualization and storytelling.
• Tutorials: Websites like DataCamp provide interactive tutorials.
• Books: "Storytelling with Data" by Cole Nussbaumer Knaflic is a great resource.

Common Pitfalls and How to Avoid Them​

Even the best storytellers can make mistakes. Here are some common pitfalls and tips to avoid them.

Misleading Visuals:​

Be careful not to create misleading visuals. Ensure your charts accurately represent the data without distortion. For example, avoid truncated y-axes that can exaggerate trends.

Overcomplication:​

Keep your narrative simple and focused. Too much information can overwhelm your audience. Stick to the key points and avoid unnecessary details.

Data Overload:​

Presenting too much data at once can be confusing. So, focus on the most significant information and present them in small pieces. Use summaries and highlights to underscore important points.

Real-World Examples​

Seeing how others have successfully used data storytelling can be inspiring and educational.

Case Studies:​

• New York Times: Their interactive data visualizations and stories during the COVID-19 pandemic effectively communicated complex data to the public.
• Spotify Wrapped: Spotify's annual recap uses user data to create personalized stories, making data relatable and engaging.
• Gapminder: Hans Rosling's use of animated bubble charts in his TED Talks transformed dry statistics into captivating stories.

Analysis:​

These examples are effective because they:

• Present data in an engaging and accessible format.
• Use narratives to provide context and meaning.
• Employ visuals that enhance understanding without overwhelming.

Transform Insights into Action with Data Storytelling​

Source: Unsplash

Data storytelling is a powerful skill that can change the way we communicate with each other. Understanding more about data storytelling, choosing the right data, structuring your narrative well, utilizing visuals effectively, making data relatable, using user-friendly tools for this purpose as well and avoiding common mistakes while doing so will enable you to make stories that inform, persuade, and entertain your audience.

Are you ready to start your adventure with data storytelling? Begin playing around with some of these methods or techniques outlined in this article and see how impactful can be in creating compelling narratives from numbers alone.

Color Season​

The rain has been pouring here for a few days, for Seattle, it means the beginning of the fall. I love Fall, it's the season of colors. In the morning, I feel so content walking with my daughter, on the soft yellow leaves toward the school, before the bell. That's even better if holding a warm cup of coffee, on the other hand.

I keep working on Columns because I want people to find the same beauty in their data.

Alright, here are our updates for September -

Easy to Forecast​

Time series data is the special power of Columns because it's built on top of a super-fast time-series database.

When you connect your data, if supported TIME fields are detected, you can choose one as the time column, check out this doc for supported time values.

As long as your data has a Time column, the Time Range filter will always show up. In addition, there is a special KEY called (time) you can select to represent the time column, meaning all metrics selected will be broken down by TIME. Accordingly, Time Series pane will show up for you to:

• Adjust time window
• Choose the time window type (bounded vs. unbounded)
• Predict future.

Connect a time series data set today, and see how the forecast tells the future of your data!

Consolidated Explore​

Columns is NOT a simple charting tool, it's designed to analyze your data, the two most important terms in the analysis are

1. "Metrics": the value you would like to see
2. "Keys": the dimensions you would like to break down the "metrics".

For example, in a query of "average temperature across all years" or "average temperature by years", "temperature" is the Metric, and "year" is the key.

When you get lost, always go to "Explore", simply type your question and AI understands you and translates your question into the query.

In this consolidated view, we also integrated Data Summary below query generation, so you can simply scroll down to view your data stats, data samples, each value distribution for each field, etc.

Edit Preference Inline​

Preference is the collection of properties that defines your preferred styles: background, palette, logo, font, axis color, etc.

Before you had to go to your Profile page to view and edit those properties, now you can edit them in story building time, any time you want to modify, you can just open and edit.

Window Sizer​

The last shortcut to mention - switch window size between standard vs wide. I usually switch to "Standard" when viewing a data story, but use "Wide" in creating time.

That's all for now, see you next time!

Airtable connection​

Airtable is used by many people, and we are excited to announce that Columns now supports Airtable connection. Just simply connect your Airtable, and choose any table to be analyzed and visualized in Columns.

Build beautiful and insightful graphs with your Airtable data, and share with your team or the world. You can also set a schedule to keep it up with your Airtable data.

Check out the example demo we put on the homepage: [https://columns.ai/airtable](Columns + Airtable)

Other updates​

• 🥇 Columns now support the Notion Relation field in data analysis, nearly no other tools support it due to the graph traverse complexity.
• 💕 Columns now support you to customize your visual sharing page with your own LOGO and other tweaks.
• 🎗️Columns now support you to select multiple versions of your visual story and share it or embed it as a Slideshow.
• 🔐 Columns now support you to only allow a shared story to be embedded in a specified domain whitelist for security.

Columns LTD​

We are offering Columns LTD currently, grab a lifetime access to Columns Premium now before the deal is gone.

The AI storyteller​

As we are planning Columns 2.0, we have fully embraced AI to help you tell a better story with your data. We are excited to share with you the first demo where Columns acts as a storyteller.

User experience updates​

We continiously hear from our users and made improvement upon their feedbacks.

Here are some of the updates we made recently:

1. Better navigation: like we have put My Workspace on all the places possible for signed users to access their workspace. Anything important that could be made inline, we have tuned them too: timeline settings, graph theme, data console, query history, etc.
2. Query builder: we have made it more intuitive to access the query builder from the Analysis tab directly.
3. Graph tools: we have made it easier to access the graph tools by moving them to the top, also we have added label to them for easily understand what they do.
4. AI questions: Upon a dataset connection, we have enabled AI generated questions to your data so that you can easily discover insights from your data.
5. Screen Width: we introduced screen width options (standard, wide) for you to choose from, so that you can easily switch to fit different device for better view. You can access these options on from your profile drown down menu.

The full loop with Notion​

You may have tried Columns Notion integration Notion Integration to analyze and visualize your Notion database, but do you know what the full loop with Notion is?

It means, once you bridge Notion database with Columns, you can leverage a few functions to keep data and graph in sync with 0 effort. Let's dive into the details.

1. Connect Notion​

Each Notion database can be connected as a live link on Columns, you can manage them in your 'workspace/data' page.

2. Build story​

You build a story on Columns using the previous connection, save the story to your workspace. You manage all your stories in your 'workspace/story' page.

3. Embed story​

By sharing a story with one email or make it public, you can easily grab its view link, embed url and embed code, all depends on what you want to do with the story. Grabe the embed url, find a notion page you want to embed the story, paste the url into the page, and you will see the story is embedded into the page.

4. Auto-update​

Every story offers schedule settings, you can set a schedule to ask Columns to auto-update your story, so that the story will always be up-to-date with your notion database. Any changes you made in your notion database will be reflected in your story.

Summary: the full loop​

Congrats! Now you know how to build a full loop with Notion in 4 simple steps, you can build a story on Columns, embed it back to your notion page, and set a schedule to auto-update the story, so that you can always have a live story on your notion page. You just edit your notion database, and Columns will take care of the rest.

Does that sound great? Let us know if you have any feedback or questions, we're here to help.

Columns Update April 2023​

April has been great so far for Columns and our users, people find it more and more enjoyable at data storytelling, from insights discovery, and chart building, to presentation beautification.

At the beginning of the month, we announced Notion Integration where you can easily build two-way sync between your Notion database and Columns graphs. Today, I’d love to share some more exciting features that simplify your storytelling process further.

AI Design​

On Columns, AI is used to help us with two main scenarios for easy data storytelling: Insights Discovery and Story Design.

Today, we focus on the second part, let AI design a beautiful and insightful presentation for you. This includes 3 initial functions we brought to the Columns editor

• Magic Theme: in addition to the built-in theme template, now we can ask AI to generate a customized theme for your specific story with a “topic” for styling, it generates background, colors, and palette to have a nice and comfortable look and feel.
• Smart Title: a smart title is generated based on your insights data.
• Insight Summary: a summary of your insights, AI will talk about your data and chart, I personally find the summary is a must to have for most storytelling.

These features free us from learning so many different settings to beautify a story, as time goes on, we will keep improving them based on your feedback, as we hope Columns will be your true co-pilot for data storytelling. Check them out and share your feedback!

Check out the demo on Youtube to see how these features play: AI Design on Columns.

Team Domain​

For users who have a team plan on Columns, this is a step further for team customization and white labeling. On your team page, you can find your own sub-domain on Columns, you can set up your own domain to host all your stories. We will enable your own LOGO and styles in the near future, but please let us know what matters to you.

Open-sourced Graph API​

We have open-sourced our core graph model, so if you are a developer, or you have an IT team, now you can access, build and host all your data stories in a programming interface, all through a simple API with an API key obtained from your Columns profile.

Check out this open-source project started from the Columns API landing page.

Graph/Chart Features​

Last but not least, we keep improving Columns user experiences, to list a few that were added in April below, you may want to sign in to Columns and play with it to discover more updates lively:

• Axis ticks line hide/show.
• The trend line on Bar/Column chart.
• Easier navigation between “item” settings.
• Social preview of a visual story when sharing on social networks, eg. Twitter, and Facebook.

Thank you for being with Columns so far, many great things happen in Columns day by day, and we’re glad to have you ride with us. As we put the full force investing on AI, we wish Columns AI to be the ultimate place for you to build and share your data stories.

Let’s make Columns to be “Your co-pilot for data storytelling.”.

Embed a live data story in your blog​

As a blog writer, do you dream about embedding a live data graph in your article? It shines your content to be more attractive and makes it much more digestible for your readers, also it makes it more worthwhile for your readers to share with others.

More importantly, what about if the data graph automatically updated, and you don't do anything to get the magic?

For example, current blog embeds a live interactive graph, as shown below (hover mouse over for tooltip):

If this is interesting, please read on…

By the way, prefer watching a video? This video is an explainer of this blog post!

Today, let’s lay out the process of how you can achieve this easily using Columns, you can also follow the 5 simple steps to practice it right away...

Step-1: Find relevant data​

Believe it or not, there are tons of data out there await to be analyzed and visualized. When you write about a topic, you need some data points to support it, you either can search open public data or manually create your own data. Public data can usually be found in government departments or various organizations. Data is usually available for download as CSV files.

No matter whether you get data from external sources or manually input it on your own, putting them in a Google Spreadsheet is the best way to have your scenario fully automated.

Step-2: Organize/format data​

To get live data to be analyzed as a live graph, you need to keep it as a clean table, a clean table has its first row containing all the column names, and each column has either a text value or number value.

As demo, the data for the above live graph can be found here: Product-Users-Days. From the table, you can see it has each column is named and consistent format of values for each column. Keep it as raw as it is, you don’t need to do any analysis at all, since data analysis and summarization is the job to be done by Columns later.

Step-3: Connect data & Build graph​

Connecting a google spreadsheet is very straightforward, you just need to paste your spreadsheet URL or copy its unique ID, and follow the 2 steps connection wizard. Your live connection shall be established on Columns from which you can generate as many beautiful graphs as you want. A simple doc describing the Google Spreadsheet connection can be found here.

Once a data connection is created, you can find it in the data tab of your workspace.

Just to mention, Columns don’t store or copy your data, a live connection is only used to read your data when it needs to be.

Now, it’s time to build something visually. Just open a new visual creator, and start by choosing the data connection we just created. Upon data selection, Columns loads it in our cloud computing cluster, making it ready for a super fast exploration.

A few things to get you started:

1. Inspect Data: get familiar with your data with a summary of stats, “Inspect Data” gives you the overview of the data you’re working with, including fields and data samples.

2. Quick Queries: Columns generate some quick queries for you to 1-click to start with some graph ideas, after that, you can continue modifying it to get what you want. You also get a “natural language” input box to type a question, Columns try to understand your question based on your data schema and stats.

Now, after a few clicks - this is what I got using the sample data mentioned earlier, need more polish or not, it is ready to show up lively in your blog post!

Step-4: Share and embed​

Saving a graph into your gallery only requires a name. Once it’s sitting on your board, you can set any level of visibility of it: Public Visible to list of invitees For blog post purposes, it’s most likely to be public viewable. Once the setting is turned on, we get a unique URL to this visual story. Also, you got an embeddable code that you can copy/paste into your blog post in HTML format.

From the sharing dialog, you can copy the embed code, it will look like this example:

<iframe
  src="https://columns.ai/visual/view/jZorEMEoau0dcl?headless"
  style="aspect-ratio: 16/9; width:100%;"
  frameborder="0"
  allowfullscreen />

We have some documentation on sharing and embedding, please refer to this link.

Step-5: Set up a schedule​

This is the last step, after we publish and embed a visual story into my blog post, how can I make it automatically updated if my data changes?

Absolutely, Columns covers that, the only option you need to look at is “Schedule”, from this option, you can set up a scheduler for every individual visual graph, frequency from 1 hour to 30 days, whatever suits your need. We have a doc page on how schedule works.

The only thing you need to do is to just simply update your Google Spreadsheet data and your readers shall see all the fresh live graphs, and you need to do nothing to make this “magic” happen.

Conclusion​

Use data to polish and shine your content writing, using a live graph in your article requires just a little effort. Embed a Columns graph today!

Having any questions? Contact us at support@columns.ai

Live Dashboard Release​

Greetings!

Columns is a data storytelling platform, we have continued building storytelling technology for you to do better communication using your data. It is designed to meet these goals, if any of these fails for you, we owe more work to you:

• Easy to discover and generate visual insights by data analysis.
• Handy design tools to customize and style a data story.
• Worry-free sharing with multiple communication methods.

In the last few months, we have received many user requests on building a dashboard. Columns is powered by its own big data computing infrastructure which serves large data set analysis, this distributed system is actually open-sourced, you can find it on GitHub if interested. With the advantage of this infrastructure, we can keep your visual stories in sync with your data.

So today, we introduce you Columns Live Dashboard.

Live Dashboard​

First of all, we created a 40s video as an overview, please check it out on Youtube.

On Columns, a dashboard is simply called a page, a simple page that collects and organizes multiple visual graphs together.

The gold part is “LIVE”, now you can flexibly set a “schedule” on any visual story, once set Columns will automatically update the visual story in sync with your data on a schedule.

How to set it up?​

It’s simply just these two options in the visual story menu:

• Schedule
• Page

We hope this is helpful for you as well as those who have asked! Here is the document page for the live dashboard feature.

Again, we love to hear your feedback, by any way listed on the contact page.

Changes list​

Today we only present a single update - but BIG!! 🚀🚀🚀🚀🚀 Scheduler Set a schedule to ask Columns auto-update your data story!

If you're a blogger, or marketer to maintain a web page or site where you want to your data graph to keep up with your spreadsheet, this is convinient way to go!

Check out this simple doc now to turn on schedules for your data stories, cheers!