Data is useful in processing information and analyzing the challenges that come with it. Unrefined data is of no use, hence fragmentation of the data and analyzing it helps in differentiating the value and purpose of the data, presented by Clive Humby in the year 2006.
With the rapid increase in technology and the era of Artificial intelligence, data is like oil in the 21st century. Collection and storage of data in AI help in analyzing the problem statements and interpreting the suitable outcome. But how to recognize if the data is ready to use or needs to be refined?
Sometimes the data obtained is inaccurate or missing information. Big data analysis and Artificial intelligence need accurate data to predict the result and innovate the present technologies.
Availability of data
As discussed, AI requires a huge amount of data blocks to process accurate information. Consider an example where the user has more than 10000+ emails in their inbox, but to analyze the data and interpret the accurate piece of information, it needs to be accurate and available from time to time.
The availability of data gives rise to transformation in technologies and creates a profitable margin.
Data for Enterprise
Organizations collect enormous amounts of data to analyze the ongoing research in various sectors including marketing, finance, and entertainment. Some enterprises also connect to increase their profit margin and predict the sales of the product which will technically help them to increase their shares and give rise to many upcoming opportunities.
To gain the insights of data, it needs to be stored and processed with the help of AI and make optimizing decisions which in result will turn into an advantage for any organization.
Advancing the level of AI
The 21st century has shaped everything technologically. In this age of technology, AI is significantly developing and the invention of robots is magnified to a whole new level. To predict and give the right solution, the robots need accurate data, and with the help of big data and AI this has become possible.
With the progress noticed in AI, it is predicted that robots will soon enter the world and the traditional work methods will be at an extinction.
Data for educational purposes
Education is a must for progress. The obtained data can be used to mine and predict the students interested in learning or enrolling in higher education for a particular course. The databases can help the candidates to select the programs they are interested in and the institutions can assist the candidates according to the requirements of the program.
Real-time data mining
Real time data mining helps in accommodating the present data instantaneously using Real time data learning machines and big data analysis techniques.
Consistency and no data loss
Collection and storage of data require huge repositories. But what if the machine in which the data is stored suddenly fails? To ensure no data is lost even when the machine shuts down, a consistent system is required to handle such kinds of situations to retrieve the data in even worst scenarios. Cost-effective systems are required to ensure more benefits and consistency of data.
In 2018, one of the British firms breached the privacy policies and sold private data of millions of Facebook users for political analysis. This incident was quite scandalous and led to the downfall of the market by over $100 in just a few days.
Data mining for extensive use and quality analysis
If data is referred to as new oil, then the analysis is the combustion engine. With the help of analysis, big data can be used to improve quality insights and provide more reliable information for the timely decision-making process. The growth of analysis gives rise to more opportunities to provide the best response in the big data revolution.
Geoffery Moore, an American Management consultant, and the author states that ‘Without big data analysis companies are blind and deaf, wandering out onto the web like deer on a freeway. Data is generated on an infinite basis and big data is used by every data management company which eventually helps in the development of marketing research and maintenance of qualitative data.
Efficiency of Data
To perform analysis faster the data needs to be cleansed and differentiated properly. To avoid multiple errors and to complete the analysis in the allotted period, the cleansing of data should be done beforehand which increases the efficiency and accuracy of the data analysis. Also with the process of cleansing and analyzing the validity of data can be checked, such as if the data follows particular rules according to the requirements. Consider an example of email address retrieval. The customer needs only emails whereas the data consists of phone numbers as well as an email address which will take more time and is partially irrelevant according to the needs. Hence to improve efficiency and to examine certain conditions for analysis the data should be cleansed and efficient.
Real progress can be only made when the data is cleaned. Almost 60 percent of data scientists clean the data often which is later used in big data analysis and AI. Data is like new oil which needs to be refined after extraction. To increase the level of accuracy and to make significant progress in the field of AI data cleansing is a must stop. Data acts as a digital currency in the 21st century and widely affects the economical and industrial sectors worldwide.