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Five Important Narratives in Writing Data-Driven Stories
data driven story

An obvious growing interest is being shown in data-driven stories where they refer to its growing trend as the next big thing in the world of content marketing. They also acknowledge that this growing trend will also reshape both content and advertising.

Although some people think that data-enhanced storytelling is just new in data journalism, the truth is, this form of journalism has been around since the mid-1800s. It is even recorded that the first data-driven stories that were recorded were in 1860.

These days, the current interest about data-driven stories underlies in the increased access to large datasets and convenient data analysis tools. The combination of the two is helping set up a new trend for journalists and writers in discovering and telling new stories regarding data research.

MLA is also fuelling this new breed of journalism as we look for these hidden stories inside social data. We mostly look for new breakthroughs into content marketing, and how particular content gets boosted via linking and sharing. To help you understand what data-driven stories are and how it can help you, here are five core narratives:

 

Understanding Data Driven Stories

According to Wikipedia, data-driven journalism is a form of analyzing and filtering large data sets so that they create a news story. The formula is to bring to light insights from the analysis of large data sets to show stories that may be hidden. Data-driven journalism lets journalists find hidden or possible new angles to a story.

The process of finding new stories includes finding and filtering data, analysis, and visualization, and telling the story. Charts and images as a form of visualization are often an essential part of storytelling.

It Is Not That Easy to Create Data Driven Stories

data driven storiesSome think that creating data-driven stories are quick and easy to make. Although data analysis is far different from telling stories from journalists who brave to cover dangerous war zones. It is also true that some writers create quick and cheap stories through giving out simple polls just to say it is a form of a data-driven journalism.

However, data-driven stories should be 80 percent hard work, 10 percent great ideas and to percent output. The same on how MLA works. Our process is to analyze different data without knowing if there will be significant insights that tell a story. To create a relative story, a large amount of time will be spent in gathering, filtering and cleaning the data. This will also be followed by running particular types of analysis, going beyond possible difficulties and test theories that can provide more datasets.

MLA is also known for analyzing datasets of millions of articles. We mostly look for insights and at times, we spend days, weeks and even months to uncover anything that has a value in the story. We may at times not find a story that is news worthy, but if we do, the discovery is rewarding since we are able to find new insights and hidden stories that only we could have found in a data.

Data Driven Stories are Mostly Unique

One of the best assets of data-driven stories is that they are mostly created telling unique stories. They can show new trends and interactions that can make people find interest to a new issue. This does not mean though that you will need new data to create an original research. For instance, in the famous MoneyBall story, there was a lot of data available to Peter Brand at that time. To make his data unique, he changed his analysis and research. These days, widely available datasets and a range of tools can be used to help analyse the data.

It would be to your advantage if you have access to a unique data set. Here at MLA Web Design, our main business is to crawl and collect very big data sets since most of these companies have such unique data. As these businesses have data that is also necessary to industry and growth, they will have available sales data, market intelligence and issues regarding data from your support desk. The available data that you might think that is just common may indeed provide insights that can help your audience.

Five Important Data-Driven Stories

Trends. There are many different stories that can be trending. Mostly these stories centres on what are popular and falling over time. But, there are also trends that are flat that can become a major story. A good example of trends is the increasing or decreasing number of smartphones. Once you find a trend, the next question is why its story is increasing or falling? So the trend itself is not the whole story, which could prompt a new investigation.

Rank order or league tables. For instance, this could be an area which gathered a lot of crime rates or a politician that is getting the most social coverage.

Comparisons. This is how one company is comparing its performance over another. For instance, we can find a comparison between Twitter’s failure to grow its active users and how Facebook is performing better than Twitter. This story has also been taken by many different publications.

Relationships. Investigating relationships between data can be difficult, especially if you are comparing the relationship of one factor to another factor. But, with the advances in machine learning, we may be able to gather more data driven stories.

To look at the relationships between the two sets of data, it is necessary to keep in mind that the connection is not the same as the cause but it can still allow areas for more studies. Another way to explore relationships is to build a more in-depth predictive linear regression models. There are still a lot of tools that can allow you to use advanced techniques like the use of machine learning.

Machine learning uses algorithms that can get data and create predictions. In nature, you can create a model from data inputs that will allow algorithms to create its data-driven predictions. Machine learning is a growing trend where we can expect more activity.

Surprising data. This is data that challenge or support something that readers may view as correct or surprising.

Tips in Creating Data-driven Stories

1. Begin with an idea. All news begin with an idea. Once you find a good story, search for data that can confirm your ideas or probably oppose your ideas. Make certain that you will focus on an interesting standpoint that will capture your audience.

2. Verify your facts. If you create a mistake about an inaccurate data you have posted, you may soon get calls about it. Inaccurate data mostly get scrutinized on the Internet so it is important to check the facts and be careful about how you write your story. Make sure that you can verity your facts to support the flow of your story.

3. Provide at least two key highlights. Since your data will need supporting statistics, you will need to highlight at least two key statistics as the main focus of your research. This way your readers will be able to remember your story more, instead of placing a couple of statistics most would not understand.

4. Use of visuals and tables. It is most suitable to use charts and graphics when writing data driven stories. Trends mostly work well with line charts. You can also use tables to highlight data as well.

5. Make it human. Make sure that that the data driven story you are creating can be understandable by those who will read it. Even if it is a story about you, a client or a colleague, the data should be in a matter of writing that people can comprehend. While economics is the perfect way to predict human behaviour which most people don’t care about, data-driven stories should focus on things people think and care about.

6. Provide insights and guidance. Think of ways on how to improve your story based on the analysis provided. Analyze how other people will also view your story and if another person writes it, would they be giving the same data as you are giving? If you can analyze your story this way, then you may be able to create a good story as it tells you that your insights are making you create better decisions.

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