Every story needs a hero. This can also be applied to Big Data. Because big data not only means discovering connections, making predictions and justifying decisions, but can also mean: telling a story. We have summarized the basic principles of data-driven storytelling :

What is data-driven storytelling?

Stories fascinate us today just as our ancestors did thousands of years ago. people love stories. They play an essential role in our daily lives – whether for entertainment or to share experiences with others. Modern storytelling is often associated with the popular TED conference series and its Ideas Worth Spreading slogan. In fact, analysis of the top 500 TED Talks found that told stories make up at least 65 percent of TED Talk content.

Storytelling describes a method in which recipients receive knowledge, ideas, products or other information through constructed or real stories. The story as a form of expression should enable the information conveyed to be presented as simply as possible and thus be well received and anchored in the memory in the long term. Getting messages, knowledge and data across in the best possible way and anchoring them in the recipient’s mind is a common practice in many industries and areas of the companya necessary but difficult process. Information is not only becoming more complex, but the mass and scope of available and relevant knowledge, as well as processes and data, have continued to increase with the age of digitization. At the same time, the receptivity of customers or users is thereby increasingly limited.

Why is storytelling a relevant method for big data?

The classic storytelling principles also apply to big data and can bring about understanding, emotions and action. Because how can customers and users be reached most effectively? – With an interesting story. Big data analytics storytelling is a welcome form of replay. Tiresome numbers are a thing of the past. Today, beautifully prepared graphics are finding their way into companies. In other words , the art of making data speak. And vice versa: data are the most meaningful facts about how a company is doing, how well its own marketing works or who its own customers or users are. That’s why data itself has a story worth telling. – But: not every record needs a story.

Data- driven storytelling is often associated with the visualization of data . For example in the form of infographics, dashboards or data presentations. But it’s much more than that. Data-driven storytelling is understood as a structured approach to communicating data collections and consists of the following three key elements: data, visualization and narrative . For an interesting and meaningful story, it is particularly important to understand how the elements are related and must be combined. Specifically, this means putting the data in the right context.

Typically, analysts and data scientists from the business intelligence sector (BI) deal with this topic, but are now reaching their limits. Looking to the future, we can therefore assume that handling data will increasingly be one of the core competencies of every digital marketer. According to a LinkedIn study , data analytics skills are even among the top skills of the digital age . Interestingly, the most important skill is to prepare and analyze data. But not necessarily the ability to derive statements from the data and to convert the results into concrete measures in the company. The inbound marketing and sales software HubSpot has7 good data-driven storytelling examples compiled: including Spotify or  Uber .  You can also read 10 examples of how to better use charts and graphs in your stories here .

Data security in the age of big data – rights and responsibility:

Before you prepare the desired data, you should clarify with the management of your company which data may be used and communicated at all. In this way, sensitive data can also be protected from unauthorized access. Here the IT departments can assign the rights accordingly on behalf of the management. Another task of the company management is the clarification of data from third parties. Here, too, there must be clear guidelines for the protection of customer data, for example. With these measures, the management and the users are able to protect themselves properly.

From May 2018 , new, stricter regulations will also apply across Europe for handling data. As soon  as the new EU General Data Protection Regulation comes into force, companies can expect fines of up to four percent of the annual turnover generated worldwide or up to 20 million euros in fines if they violate the new data protection rules (EU-DS-GVO) . The expansion of European data protection law primarily affects young companies from the digital economy. Read here what you need to know and do about the EU General Data Protection Regulation .

How is data most effectively used to tell compelling stories?

A good story activates, emotionalizes, inspires and binds. And like any other story, the data-driven story has a beginning, middle and end ahead. Why your data is of interest to the customer or user should be recorded at the beginning of your story. What is the reason for dealing with these numbers? The middle part is about the voyage of discovery. As a marketer, you should address the following questions here: Which insights gained have an impact on my company, the customer or user? Do the results possibly call previous assumptions into question? The core statements should be particularly emphasized here. The conclusion then follows with the final part: How does the data presented affect the company or the customer relationship – what is my message to the customer or user?

The difficulty with storytelling with big data is that you may be the only person who can understand the content of the data, because you have probably dealt with the data volumes alone for a long time. So how can you prepare the data in such a way that even a layperson can understand it?

Telling a story with Big Data – How does that work?

Data-driven storytelling is challenging – but not rocket science. The basic principles can be learned in a few hours. Many tools and data sources are also available free of charge as open source software.

When it comes to data-driven storytelling, you should proceed as follows:

1. Retrieve data : e.g. via analysis tools such as Google Analytics or via the API of a social media channel. To do this, consider all the tools available to find the data you need to tell business-relevant stories that are not only original, but also transparent and valuable to your business. Because unique content that reports on exciting insights from your industry helps to achieve more reach and possible market leadership.

Methods such as ” data scraping ” or ” data mining ” are also suitable for advanced marketing data analysts  . However, these require mastering a programming language such as “Python”, “Ruby” or “Perl”.

2. Store the data in a text-oriented NoSQL database . NoSQL databases can help companies with big data plans manage the data overload. Fragments from different sources are integrated here. Use  MongoDB or CouchDB for this, for example .

3. ” Data munging ” (or “data wrangling”): that is, recognizing, translating and cleaning up errors ,  because the data is often available in different formats. This step is usually time-consuming and nerve-wracking, but forms the basis for evaluating the data. Knowledge of statistics is an advantage here.

4. Selection of the most interesting and meaningful information . Algorithms from “Data Mining” and ” Pattern Recognition ” help the analyst to recognize abnormalities or trends, as well as to automate larger amounts of text and reduce them to the core statements.

5. Condense and visualize information so that the most important data connections and insights stand out clearly even for a non-statistician. For example, a table, an information dashboard or a data map is suitable here.

Then it’s a matter of translating the data into charts, animations and numbers to tell the story. You can find out how this works in the next section.

What is important when visualizing data?

1. Make a list of questions that you think customers and users might be interested in answering. Filter these questions according to relevance and then decide on a specific question rather than ten detailed questions.

2. Pick up the receiver narratively . The prerequisite for this is that you know your target group very well and know what level of knowledge they have on the subject. For example, an expert is much more likely to expect details than a generalist. Managers, in turn, need results that they can translate into concrete action. Standardized story plots can help here. Find  out here which 7 story plots you should know.

3. Putting the data story into the correct narrative format . Is it a report, a forecast or a solution to a problem?

4. Choosing the right narrative perspective that is critical to the impact of the story’s information and protagonists. Marketing Manager Ben Jones lists 7 types of data stories :

  • Change over Time : the representation of transformations
  • Drill Down : the explanation from the general to the specific
  • Zoom out : from the specific to the general
  • Contrast : a direct comparison of two or more protagonists
  • Intersection : describing the crossing point of two or more protagonists
  • Factors : the visualization of the causal effect of multiple storylines
  • Outlier : the story about outliers or special cases

Once the structure, the target group, the plot and the narrative perspective are in place, it is then a matter of the precise visualization of the data. The following basic rules must be observed here:

  • Colors are only used to graphically represent differences, but not to visually enhance the graphics
  • Less is more – too many details are just distracting. Instead, empty spaces should be used consciously

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