AI software is increasingly being used in business to process and interpret data and to take on some of the responsibilities previously thought possible only by human employees. However, modern businesses encounter a vast quantity of data and not all of it is in fit shape to get the best out of AI search applications. The term GIGO (garbage in, garbage out) has been familiar to computer scientists since the early days of information processing. With the rise of machine learning technologies, the adage is as true as ever. Businesses looking to deploy AI-enabled search and data management software thus need to look seriously at how they process data to ensure accuracy and efficiency.

‘Data Munging’ Is Time-Consuming But Necessary

Efficient digital processes require data in a consistent and predictable format for processing. Without clean input, results can be unpredictable, error-prone, and slow to produce. For that reason, data scientists may spend up to 80% of their time ‘data munging’, that is, preparing and cleaning data files to render them suitable for use in a given system.

This figure sounds alarming, but anyone who has had to consolidate spreadsheets from inconsistent sources will have some idea of why it is necessary and how awkward it can be. Raw data needs to be mapped into the correct data structures to be logically coherent. Multiple sources must be aggregated, and duplicates or inconsistencies resolved. Much of this effort typically requires human judgement.

The Two Aspects Of Clean Data In AI

AI data cleaning has two facets: machine learning and actual processing. Machine learning systems use historical data for training purposes. Clearly, this data must be correct and consistent for the training to be effective, as an ML system relies on data consistencies to form patterns for its predictive models.

Once trained, input data similarly needs to be in good shape to get the best results from an AI system. High-quality, well-structured, clean data will help to ensure faster processing times and more accurate outcomes.

The Importance of Quality

Clean data is not just data in the right format. It needs to be good quality too. Well-formatted but factually erroneous data can pass through an AI system with no means for assessing its truth. Even a minor error can cause huge disruption with complex systems. Each component will process this error in turn, compounding it with cumulative effects that could result in disastrous outcomes.

Recommendations

Problems at the data level are not simply resolved by increased quantity. Quality is just as important, if not more so. ‘Dirty’ data (i.e. incompatible and inconsistent file types that prevent accurate search results) can have many causes: human error, partial or inappropriate sources, poorly calibrated measurement devices used for initial data gathering, inaccurate copying techniques, obsolete software or storage media, and so on. Avoiding these problems is essential for a successful and accurate AI search system. But it is also necessary to have robust data munging strategies in place. Ensuring consistent quality control standards is vital too, and an audit chain will help to track down sources of error.

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