As originally seen on Data Informed.
There are far more smart sensors in the world than there are people (and we’ve barely gotten started). If you take a minute to let that seep in, you’ll realize just how extraordinary this is. Factory machines, hospital beds, cars, bridges, grocery store shelves, light fixtures and farming equipment, the list goes on - they are all outfitted with sensors that monitor activity and generate vast amounts of data. Yet in most cases, after the data is collected, only a fraction of it is used. That’s too bad. There are real stories in this data just waiting to be told.
The reason? It’s just too damn hard. Most people blame the data scientists, or actually the lack thereof. There is far more data than there are data scientists to analyze,explain and help people act on the resulting insights in an easily understandable way. The Internet of Things (IoT) only promises to make this task exponentially more difficult as more devices come online. In fact, by 2020 Gartner predicts that there’ll be a mind-boggling 21 billion connected things around the world. Considering that IoT is estimated to be a $1.7 trillion market by 2020, the monetization opportunities are enormous. Companies engaged in logistics, supply chain management, manufacturing, agriculture, healthcare, and distribution are already seeing early benefits of IoT and the longer-term potential is even more exciting. But, make no mistake. If something doesn’t change quickly, we’re going to hit a wall. Analyzing the enormous amount of data that connected devices are already yielding is already a huge challenge. To overcome these issues we need a new solution. We need data storytelling at machine scale.
Why Data Storytelling is an Effective, Scalable Solution
Data only has value if you know how to interpret it and then act on the resulting insight.. Typically, that means using the data you’ve gathered over time to assess a given situation, anticipate what’s might happen next, and offer advice to either mitigate or optimize the situation.
More often than not data is relayed in numbers, graphs, and charts — the language of data science — which can be difficult to interpret. Not to mention that the data science skills required for this type of analysis are still very hard to find and notably expensive. For end users who are just interested in knowing what happened and what action to take next, graphs and charts are not particularly helpful.
True data storytelling, enabled by advanced natural language generation (Advanced NLG), offers a better way. By automatically turning sensor data into meaningful and insightful narratives that people can read and easily understand, Advanced NLG makes the required data analysis and communication possible, unlocking the true value of IoT. Explaining this data in natural language provides any audience with actionable information that, ultimately, makes their job easier.
A large part of Advanced NLG is driven by a process known as Narrative Analytics, a new approach to analysis that is driven by specific communication goals versus the typical bottoms-up approach to data analysis. With Narrative Analytics, the desired narrative determines the type of analysis that needs to be performed and the data required for that analysis. The result? A natural language explanation you just read and immediately understand.
Data Storytelling In Our Everyday Lives
Let’s examine a few, real-life IoT examples where Advanced NLG could have valuable impact when applied to industries such as agriculture, logistics, retail, insurance and utilities...
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To learn more about the role of Advanced NLG in the Internet of Things, read our white paper, “Unlocking the Full Value of the Internet of Things with True Data Storytelling.”