Data sets that have been prepared for direct and efficient use are referred to as smart data. As a result, it has already been consolidated, checked for quality, and, ideally, processed with meaningful analyses.
As a concept, demonstrates the issue in today’s corporate world: There is a lot of data, and it is becoming more accessible as the digital transformation progresses.
However, there are numerous other obstacles, ranging from data quality and reliable processing to provision via sustainable infrastructure.
As previously stated, the database begins to establish itself as the digital transformation progresses. However, because of the sheer volume of data in a company, combined with the lowest processing capacities, the knowledge contained within it is frequently wasted.
This valuable “oil” is inaccessible to the average worker. It necessitates the use of specialists such as data scientists.
And this is the crux of the problem: businesses have a lot of data but aren’t given the tools to use it. As a result, all data sets classified as “smart data” are valuable to the company, whereas raw data is frequently a burden.
As a result, most businesses value data only when it is “smart data.” They will only be useful if they are usable and understandable for normal users. Do they add value to the organization?
The process of transforming data into smart data follows six steps, which primarily integrate processes from data engineering, data governance and data science:
Smart data is frequently portrayed as a stage in the growth of big da
ta. The link is that the hype around big data has raised the general data potential and perception of data utilization. However, in practice, you rapidly experience several obstacles, which is why smart data is designated as the next level.
The link is so obvious, but it is not true that it can only emerge from huge data. Any data may be classified as smart data if it is adequately prepared and adds value. As a result, Big Data does not own the concept of “smart data.”
There are several types of advanced analytics, including artificial intelligence, machine learning, prediction, and prescriptive analytics.
Smart data, like big data, is strongly related to advanced analytics, although it is not the sole use case. Traditional descriptive assessments can also have a significant influence, provided they are appropriately suited to the demands of the stakeholders. As a result, advanced analytics is frequently one data processing method, but not the only one.
While smart data as a term is certainly a good way to represent the difference between existing and processed data, for us, it is just a trailblazer.
The goal must not be to categorize data into “Smart” and “Dumb”, but rather to establish the holistic image of the Data-Driven Company.
It is, therefore, important to convey that the mere existence of data does not provide any value but that a lot of work is required to process it. But there is much more to understand how far-reaching the effort is.
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