Smart Data – Short Explanation

The amount of data being gathered and compiled in the world is continuing to grow at an astronomical pace. From a social media perspective alone, a single minute is massive when it comes to data. In one minute:

  • 48 hours of video files are uploaded
  • 100,000 tweets get sent
  • 600,000 messages and conversations happen on Facebook

When you consider that data comprises IoT sensors, medical records, corporate communications, and so much more, you can start to see the real picture with regards to data. Now, data in itself is not bad – it is, in fact, the basis of the resurgence of interest and progress in Artificial Intelligence (AI) and Machine Learning (ML).

Unfortunately, data quality can, at times, be suspect. Some estimates put the cost of “bad data” as close to 12% of an organization’s revenue. The adage – garbage in, garbage – out is generally quite applicable in this scenario.

With smart data, the information is already tagged and categorized at the collection point. This ensures that the data can be analyzed without further optimization being required. The “smart” label relates to the data gathering point having the right skills to make a decision on the data it gathers instead of sending that information onwards.

Smart Data in the Real World

When discussing smart data, think about it as data that formatted in a fashion that allows it to make sense. Traditionally data is compiled from multiple sources and then compiled, formatted, and analyzed on a fixed schedule. This often means that when the information is actually reviewed in person, the information is already out of date.

Smart data helps to transform this paradigm by analyzing the data at the collection point. This not only benefits companies and organizations in terms of time savings but can improve the ability of data to drive actions.

Many organizations believe that collecting data is by itself a virtue. They collect copious amounts of data on their customers, transactions, products, and services in data warehouses and data lakes. They believe that the collection of data with a future intent to use it is the right choice. In reality, a better decision is to collect data that is useful to their business. Data storage is expensive and takes time to gather. Gathering smart data that can be used is a more efficient use of time and resources.


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Smart Data in the World of AI

When thinking about smart data, a great example is that of self-driving vehicles. Here sensor data needs to be acted upon instantaneously to ensure that the driver and passengers are protected and kept safe. Sensors that are able to receive data and analyze the information is only one step in the process. These sensors also need to make decisions so that a vehicles steering and braking are influenced.

However, smart data is both data at the edge (IoT and similar) as well as data that has been processed and categorized. The latter is ideal for ML as a tool to train and educate algorithms. Using smart data in this way lets ML algorithms process data in an unsupervised fashion.