Category: AI Adapt To Big Data

The more data we run through the machine learning models, the better they will be. It is a virtuous cycle.

Big Data and AI are mutually dependent. The latter helps businesses realize the potential in their data warehouses in ways that were previously impractical or unfeasible, but it also depends on the former for success.

We want more data to better understand the business problems we’re attempting to solve and to feed into machine learning models.

Today, we want as much data as we can get, not only to understand better the business problems we’re trying to solve but also because the more data we feed into machine learning models, the better.

Artificial Intelligence is creating new methods to analyze data.

One of the fundamental business problems of big data could sometimes be summed up with a simple question: what now? Let’s summarize it in the following expression: “We have all this (that’s the technical term for data) and much more to come, so what do we do with that information?“

That question wasn’t sometimes challenging to hear in the once deafening buzz around big data.

Additionally, answering that question or deriving insights from your data typically requires a lot of manual effort. AI and ML are the latest techniques, and AI is creating new methods to do this.

In the past, engineers have had to use SQL (a list of queries) or queries when analyzing data. But as data becomes increasingly important, there are increasingly more ways to find information. Artificial intelligence is the next big thing in SQL query creation.

The combination of computer science and statistical models yields artificial intelligence and machine learning.

Data analysis is becoming less laborious.

As a result, managing and analyzing data relies less on time-consuming manual effort. Although AI is speeding up processes that once took days, weeks, or longer, people are still essential to data management and analytics. When it comes to big data, there has been a trend toward a two-tier approach over time as businesses deal with the enormous amount of information, they need to handle in order to derive any benefit from it: a storage layer and an operational analysis storage layer situated above it. 

Newsflash: The CEO is concerned with the operational analytics layer, even though it depends on the storage layer to function.

In other words, choices and concepts can be made faster. By using similar concepts and AI technologies to reduce manual, labor-intensive burdens and increase speed, IT can also benefit from the background information that, let’s face it, only a few people outside of IT want to hear about.

Humans still matter a lot.

Among other cutting-edge technologies are machine learning and artificial intelligence, which are instrumental in helping businesses take a more holistic view of all that data, giving them a way to make connections between key data sets. But it is not about eliminating human intelligence and perception.

To enhance these technologies, also known as augmented intelligence, businesses must integrate the strength of human intuition with artificial intelligence. More precisely, in order for an AI system to perform, it must be able to learn from both humans and data.

Companies that have successfully combined human power and technology are able to expand the group of people who have access to crucial analytics insights. Beyond data scientists and business analysts, companies can save time and minimize potential bias resulting from users’ commercial interpretation of data by combining artificial intelligence with human intuition. This is known as augmented intelligence. More precisely, in order for an AI system to perform, it must be able to learn from both humans and data.

…