Unclean Data Can Let Down Artificial Intelligence and Machine Learning

by | Published on Aug 26, 2019 | Document Conversion / Scanning Services

The pressure to reduce costs, comply with stringent regulations and ever increasing customer service demands impose considerable strain on businesses. Organizations must also have the right data that is clean and consistent, at the right time to identify various business opportunities, to penetrate into the market, determine customer demands, and make viable decisions. Document conversion services can help accelerate document-driven business processes by automating manual, slow and error-prone tasks of document classification, extraction of information and routing the information to the next step.

Artificial Intelligence and Machine Learning

In this present industrial age, the future of a business depends on the quality of data that they have. Lack of clean, well-managed and labelled data is one of the major reasons why businesses get valued out of AI or Artificial Intelligence. AI is a competitive tool that is widely used today to make business processes easier. It is a type of computer software that engages in human-like activities such as learning, planning, problem solving and so on.

A study released by Figure Eight, a company which helps generate training data for customers, discusses the state of AI and machine learningThey found a decided lack of data ready to be used to train machine learning algorithms. The study showed that only 21 percent of respondents indicated that their data was both ready for AI i.e. the data was organized, accessible and annotated. These data are used for the purpose aptly. 15 percent of respondents said that their data is organized, accessed and annotated and ready for AI but it is not being utilized for any business purpose.

Another study by Alegion, a data labelling company, also came up with a similar conclusion. This study also found that data quality and labelling issues had made a poor impact on nearly four out of five AI and machine learning projects. From this we can understand that the advent of enterprise Ai has led more than half of the surveyed companies to label their training data internally or build their own data annotation tool. 8 out of 10 companies show that training AI or ML algorithms is more challenging than they expected.

Almost all organizations have poor or bad data and for years IT professionals have been crying “garbage in and garbage out” to ensure clean and actionable data. Database administrators  would spend hours building referential integrity into their relational databases which cancels gibberish from records. Data warehouse architects also build millions of data models to enforce master data management standards. Enterprises spend a huge amount of money during the peak of warehousing era and ensure that transactional data is clean before loading into MPP database for analysis.

In the present age i.e. after 12 years into Apache Hadoop experiment and cloud migration, getting clean data is still not that easy. Today with ever increasing data volume, data is less structured. It is a fact that a considerable amount of data used in businesses is incorrect and such data comes from various sources. With incorrect data it is difficult to apply them to AI and machine learning.

The amount of hype around AI may not be giving a true picture of its actual utility because more than eight out of 10 big data projects are failures. According to a study in 2018 on AI from PwC, only 3 percent of AI implementation have been implemented and are generating a positive ROI. With the rapid pace of technology, it is easier to get side tracked but the focus should be on the data.

Today all companies are collecting and preserving data in the hope of using it to train machine learning algorithms. But they face the problem of poor quality data from the past 20 years. The rising growth of AI and ML has highlighted the significance of accurate data. With poor data the last thing that you want to do is automated bad decisions. On the other hand, with quality data businesses have many opportunities ahead of them, and can also understand how motivated the customers are to really make meaningful progress. So, make sure that you give your data to those people who know how to handle the data.

Every organization deals with various data and poor data can lead to poor understanding and ineffective business decisions which is a major setback for any organization. Therefore, it is important for businesses to take the necessary steps to ensure that the data quality is excellent. It helps the business to move forward and achieve a competitive edge. All quality data can be digitized with the help of data conversion services with utmost accuracy.

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