AI’s future lies a little in the hands of quality data; it sounds absurd, doesn’t it? Should the future not rest in man’s hands? But if you look at the progress of Machine Learning and Artificial Intelligence, you would be able to see that the new advances have piggybacked on the vast volume of data generated today by humans and machines.
The development of machine learning and deep learning algorithms used in the new developments such as self-driving vehicles and natural languages analysis has only become possible because of the rise in data quantity and efficiency. Almost all AI algorithms generate comparable results when you have fewer data, but once you have petabytes of data, you will see the shine of deep learning algorithms.
People can generate a small number of data, and the big data explosion was triggered mainly by more and more computers linking to the Internet and generating more data. The IoT revolution has generated more data than ever before. Via such massive data, which in turn contributed to the foundations of deep learning, no human being can decipher it.
The Three key Data Problems
When you collect data for your bleeding edge AI project, quantity is not the only challenge. No matter how much data you have, if you want the best outcomes from your algorithm, the consistency, cleanliness, and diversity of the data matter just as much.
You are required to face roadblocks if you are attempting to build an algorithm for autonomous cars with just a few thousand rows of data. You must train your algorithm on loads and tons of training data to guarantee that your algorithm achieves accurate results in real-world scenarios.
Collecting data is not really complicated due to the opportunity to view logs from virtually every computer today coupled with the almost endless supply of data from the web, as long as you have the right resources and you know how to use them.
Your framework needs to consider all the potential variety of data points possible while you are teaching your algorithms to solve real-world issues using AI. Your machine would have an intrinsic bias and deliver false findings if you cannot get a variety of data.
Many times, this has occurred, including the popular 1936 Presidential Survey performed in the USA by The Literary Digest. He had hoped the nominee would lead the presidential election, finally losing more than 20% by a large margin. However, the publication of 10 million people had been surveyed, to which 2.27 million had replied, an astronomical amount even by today’s standards. How might something have gone wrong?
Well, they had neglected to consider the concerns of the even greater number of subscribers who, as the nation was in the midst of a great depression, actually would not react together with others who could not afford to subscribe to a publication.
While the last two variables are very relevant and certain attempts may be tested, data consistency is easy to skip and hard to spot even though your findings do not fit. When you re-evaluate the data once it has gone into processing, the only way you can know that the data are unclean is.
Removing duplicates, validating each row’s schema that comes in, providing some hard limits to establish a check on the values that join each row, and even maintaining track of outliers are several easy approaches to maintain data consistency. Manual interventions could also be required if certain variables can not be held in check through automation.
Data transformations are a big point where errors can weigh heavily. Not all the data points would have the same units, mainly as you accumulate data from several sources. It is necessary to transform the values using the right equations, which have to be applied around the board.
Web-scraped data often consist of organized, semi-structured, and unstructured data, and you would need to be sure that you translate all of them to the same format when you choose to include these varying types of data in your AI project.
How do AI ventures affect the quality of data?
Any machine learning or AI initiative may be affected by Data consistency. Also, fundamental errors in the data will result in findings that are off by a long way, based on how vast the project is. If you build a recommendation engine and the training data is not clean enough, the consumers may not make any sense of the suggestions.
It could be challenging, though, to get a hold of whether unclean data has played a role in this result. Similarly, if you build a prediction algorithm and some bugs in the data, some predictions may still be acceptable, although some may be quite off. It could be incredibly challenging to replicate joining the dots to understand the difference the filthy data has brought.
In phases, any AI project develops. An initial algorithmic decision is made, that is, provided the dataset, which algorithm will perform better, and the particular use case is determined. Your option of the algorithm itself may go for a toss if the data has anomalies, and you may not come to know this fallacy until long after.
The best way to guarantee that your concept operates in the real environment is to ensure that the AI framework is fed clean data and proceeds to validate it on more and more data. To correct the model’s direction as it strays away, you may also use reinforcement learning.
Will the Answer be Web Scraping?
Web scraping may be a workaround, but only if it is utilized to ensure that the variety and volume of data that falls into the pipeline are properly cleaned, checked, and validated before being used in a project in conjunction with many other methods.
And suppose you use a web scraping application to extract data from the web, whether it is in-house or paid software. In that case, it is unlikely that the application would be able to conduct certain post-processing operations on the data and get it fit for usage.
You’ll need an end-to-end framework that takes care of scraping, washing, validating and checking the data so that the final production can be directly implemented in a plug and play fashion into business workflows. It is as challenging to construct such a scheme from scratch as to ascend the mountain beginning from its foundation.
The web scraping service is supported by our WSCRAPER team, which is that you send us the specifications, and we give you the data, the DaaS (Data-as-a-Service) model. You need to view and merge the data (which will be in the format and storage medium of your choice) with your established structure.
We scrape the data from numerous websites and use several tests at different levels to ensure that the information we provide is safe. This data helps our clients in diverse industries use cutting-edge technology such as AI and deep learning to streamline multiple processes and better understand their customers.