Artificial Intelligence: The importance of Broad Data
In today’s dynamic digital world, the use of data is constantly evolving, as are the needs of businesses. With increasingly massive amounts of data, new techniques are emerging over time to help companies better understand the reality of their information.
Methodologies such as Big Data helped companies analyze large amounts of data in order to explain the events that had already occurred. And thus, to be able to understand the reason for past events.
However, this approach is no longer as useful as it once was. In a much more dynamic digital world where changes and advances occur with increasing speed, companies have the need not only to adapt to changes in the environment, but also to anticipate them.
Give the analyzes a new predictive approach, which allows, based on the analysis of information and large databases, to establish patterns that allow predicting and estimating future events.
This is where the implementation of Artificial Intelligence can help, and where the needs of Big Data for AI diverge.
Artificial Intelligence Applications
For the efficient operation of the algorithms, above other factors, the existence of variety in the data set is necessary.
Variety
For AI to effectively analyze data and thus establish predictive analytics, there needs to be variety in the data.
And it is that, with the increasing adoption of AI in all sectors, the need to access diverse data sets also grows.
Big Data
In the case of Big Data, we can define it based on three factors: The volume, the speed, and the variety of data.
The volume
It refers to the size of the available data.
Speed
Speed, on the other hand, refers to how fast the data arrives.
Variety
It refers to the diversity of the data set.
The importance of “Broad Data”
That the data is diverse, means that today there is a need for data that is not too simple. This data is also called “wide data” and can come from internal company, external, structured and unstructured data.
The fact that the existence of extensive data is essential for the correct implementation of AI applications in companies and organizations does not mean that the volume of data is not important. Having lots of data is also important for Artificial Intelligence applications.
However, the variety of data we use is considered a more important parameter for algorithms to be efficient, and to establish more precise and accurate predictions about future behavior.
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