Smart Data: What are the differences with Big Data?

For ten years now, we have been heralding Big Data as a real revolution. Yet data as such is raw material. Unless  it is used correctly, data has little added value. Big Data is a bit like a gigantic puzzle or the dots of a Signac painting. In the first case, data must be assembled in the correct order to get the full picture. In the second case, only the genius of the painter will make it a masterpiece of pointillism. That's the challenge of big data: to process data using a Cartesian artificial intelligence capable of having this human-specific trait of genius, all with a minimum of latency. Welcome to the age of Smart Data.



Big Data: encrypted data

Many are familiar with the dizzying numbers associated with Big Data, but it is always good to keep them in mind. For non-IT specialists this provides a better understanding of the need for a thorough reflection on data processing. We will not mention here the enormous amount of data created daily. The important thing to remember is that there is far too much data for a human mind or a traditional computer to grasp. Imagine an Excel painting that never ends and you can get an idea.


In terms of revenues, the International Data Corporation (IDC) estimates that data revenues reached nearly $200 billion in 2019. These revenues are expected to increase by 13.2% annually to $274.3 billion in 2022. By comparison, this represents more than 10% of the French GDP.


Within the company, the added value of data is demonstrated as long as its potential is extracted. Cost reductions are often substantial. The figures differ depending on the source and type of industry, but it is not far-fetched to look at savings in the order of about 10%.


Data can generally be categorized according to five criteria:  volume, velocity, added value, authenticity, and variety. Big Data simply accumulates data and stores it. To set up the puzzle, you must deal with them with your needs and objectives in mind.

Smart data, what exactly is it?

Smart Data is a global concept that involves processing data in the right place at the right time. Taking a Smart Data approach means making sure that data is used before it becomes obsolete and, above all, making the most of it.


Smart Data is based on four distinct axes:

  • Process data early and fast: if you are told of a monster traffic jam when you are already on the road in question, it will be too late to escape and the information will be of no use to you;
  • Deal in the right place: If you're participating in an auction and need to know the status of your account before buying a painting, you can’t afford to waste precious seconds; you would risk losing the bet. Latency and response times related in particular to the geographical position of the servers are decisive here. In this context, network architecture makes perfect  sense;
  • Deal well: if before you buy your pied-à-terre in Torre Molinos, you compare the asking price with the prices charged in Marbella you will probably feel like you are doing the deal of the century;
  • Acting, automating: a Smart Data model involves effective decision-making without human intervention. Are the prices of the next store for a box of changing diapers going down? Your prices are automatically adjusted to remain competitive. The temperature of a machine on your production line rises above a certain threshold? It automatically stops and contacts the (robot) technician. There is no waste of time and little or no human intervention.


To be effective, the Smart Data philosophy requires increased attention to cybersecurity and data integrity. If automated decisions are based on incorrect or altered data, the entire company is at risk.

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How to move from Big Data to Smart Data ?

Turning these big data into Smart Data is a real challenge for businesses. This involves an investment in terms of money but also time and research. Large companies clearly have an advantage over small and medium-sized enterprises in this area. They have more resources and more data. This was demonstrated in an article published in the Journal Of Monetary Economics in 2018.


Nevertheless, SMEs can do well by focusing their efforts where it is worth it. By identifying the relevant data and the information to be extracted from it, a small or medium-sized company can also claim to play in the big leagues. This involves several elements.


On the one hand, the company must work on a data processing system that is not fixed in order to be able to adapt to changes without having to rethink each time the entire architecture. We're talking about scalability.


On the other hand, the company must seek to collaborate and obtain data from external sources in order to minimize potential biases. Because smaller structures have access to a limited amount of data, biases are potentially greater. If you don't expand the database, you should at least be aware of it.


Finally, it is important to define a comprehensive strategy to identify the objectives of data usage. This strategy must be kept in mind and known to the various employees and actors linked to the company. Everyone must understand the purpose and act accordingly. If you have a contemporary art gallery and your employees bring you works by Monet or Rubens, there's a catch. This may seem obvious, but there is a real loss of efficiency in the company due to a lack of communication or inadequate communication.


Ryax offers a SaaS software solution that helps you assemble the pieces of the puzzle. Our intuitive platform allows you to create a sustainable and cost-effective data ecosystem in a matter of days to turn your Big Data into Smart Data. If you want to know more or discuss it, please contact our teams.

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