Big Data has recently come to be understood as a collection of data that is huge in volume, yet growing exponentially with time. This phenomenon has come to realization due to the fact that, in recent years, businesses have begun to realize the critical impact that data of all forms has on the growth capabilities of these businesses. This led to the concept and practice of data management, which artificial intelligence has recently started to improve.
When businesses began to collect data, there was no set process for how data should be collected or organized or archived. Each business had their own way of collecting this information, and many of these unique processes had their own bottlenecks and pain points.
The main issue with these archaic forms of data management is that a good deal of this important information would get lost. This loss holds companies back from their potential growth. On top of that, the recovery of this lost data costs businesses an exorbitant amount of time and resources to restore and recover the information. Studies have shown that 14% of data loss is caused by human error.
Big Data & Artificial Intelligence
With the massive increase in volume of data that businesses use to analyze and make decisions, traditional data management methods can no longer work efficiently to accommodate typical business needs. Here, artificial intelligence can provide a symbiotic relationship with big data. Artificial needs massive data sets to learn, a data set too small may not allow AI to learn enough to work correctly. Big data needs artificial intelligence to sort through its massive amount of information, because humans take too long.
The Roadblock of Unstructured Data
As many of us know, some people have illegible handwriting. What happens when someone cannot read another person’s handwriting on a form that they need to collect data on? The information does not get collected. The same thing happens when traditional softwares like OCRs cannot recognize characters on a scanned document – the information does not get collected. This also applies to digitized records that are not classified correctly, data management tools do not know what to do with a form that just has a number with no form field description or context. This has been a huge problem for businesses that deal with big data, because the amount of unstructured data they encounter is massive.
Technologies for identifying unstructured data in big data sets, like natural language processing (NLP) and natural language understanding (NLU), underlie artificial intelligence. These technologies help AI tech to “learn” what is being written or typed, and the nuance and context of human communications. This allows the AI to determine what an unstructured piece of data was trying to communicate.
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The world of data management has changed immensely since the practice was undertaken by businesses all over the world, mostly in terms of volume and process complexity. As mentioned above, big data makes AI smarter by allowing it to “study” and “learn” to establish connections between trends. AI also speeds up data analysis through machine learning algorithms, which helps decision makers looking to extrapolate insights quickly. The data management offered by AI learns so much that it is able to think “outside the box”, giving businesses intelligent suggestions on how to manage business needs.
