Artificial Intelligence in Data Analytics
Artificial intelligence (AI) is a contemporary technology that has a lot of applications in the practical world. The technology is still in its development phase, yet many tech companies have developed remarkable ways to put AI into use. Artificial Intelligence continues to produce its benefits for mankind. Technological devices that are integrated with software and systems with AI programming are common in the lifestyles of many. The primary agenda behind every developed AI system is to help its users benefit from automation and emulate the functioning of a human host.
Pushing the technology in the dimensions of data analytics appears to be a remarkable idea, and it excites data enthusiasts and professionals because the prospect of contemporary technology like artificial intelligence combined with big data analytics can prove to be significantly beneficial. There have been years and years of research and development carried out in data analytics and its assimilation with machine learning.
We are moving towards the usage of statistical algorithms and machine learning techniques to drive results convenient for the business sector. In a business dimension, data analytics is sought to drive streams of revenue, cater to the top trends in the market, increase operational efficiency, and transform marketing practices in order to benefit from a competitive advantage.
One of the companies actively working in this space is Anodot. It is a tech company which is engaged in marketing business analysis software that is integrated with machine learning systems to scrutinize anomalies or inconsistencies within large data volumes. It certainly helps an organization detect any kind of discrepancies and problems, solves them, and also identifies contributing factors within its dimensions.
It is vital to grasp the benefits of the incorporation of machine learning with big data. The following account explains the primary benefits of combining the two:
Optimized Data Structuring
Organizations working in data analytics always face the challenge of structuring large volumes of data. Data sources are fragmented and scattered, and in conventional practices, organizations simply manually cleanse and process data before running an analysis. Many studies indicate that the structuring of data takes up an extensive amount of time. An analyst is consumed by simply cleaning up chunks of data before the analysis. Information generated has absolutely no structure. Contracts, credentials, survey information, and email texts, etc. contain abundant information which can be used in further processes.
AI would allow for swifter processing and structuring of data, liberating an analyst from the mind-numbing task of funneling through volumes of data. This would not only save time but contribute towards the productivity of a data analytics employee.
Companies have goals that are needed to be achieved using data, and Deep Learning is one way to extract patterns from data to help make such decisions. The critical aspect of every analysis is defining your objective. If a system that is capable of specifically conducting the kind of analysis you are looking for is created, it would help in the transformation of the quality of analysis and keep your goals aligned. For this, businesses do not need to employ a professional or outsource it to service providers, but rather employ a system integrated with AI technology. Linking your data goals with smart software that specializes in focusing on the objective that you share can deliver meaningful and consistent insights for decision making.
Another company specializing in the respective area of artificial intelligence’s applications into data analytics remains OpenText. Its product, OpenText Magellan is an AI-powered tool used in business that provides system imporvements based on statistical data. It combines structured and unstructured data volumes and analyzes to deduce different patterns.
The collaboration of artificial intelligence and data analytics may deliver a unique benefit that is not possible to achieve in the conventional model.